{"id":116448,"date":"2025-12-22T03:59:51","date_gmt":"2025-12-22T03:59:51","guid":{"rendered":"https:\/\/www.dumpsbase.com\/freedumps\/?p=116448"},"modified":"2025-12-26T07:44:13","modified_gmt":"2025-12-26T07:44:13","slug":"passing-your-ncp-ai-infrastructure-exam-with-the-updated-ncp-aii-dumps-v9-03-continue-to-check-our-ncp-aii-free-dumps-part-2-q41-q80-online","status":"publish","type":"post","link":"https:\/\/www.dumpsbase.com\/freedumps\/passing-your-ncp-ai-infrastructure-exam-with-the-updated-ncp-aii-dumps-v9-03-continue-to-check-our-ncp-aii-free-dumps-part-2-q41-q80-online.html","title":{"rendered":"Passing Your NCP AI Infrastructure Exam with the Updated NCP-AII Dumps (V9.03): Continue to Check Our NCP-AII Free Dumps (Part 2, Q41-Q80) Online"},"content":{"rendered":"<p>Now, you can pass your NVIDIA Certified Professional AI Infrastructure certification exam with the most updated NCP-AII dumps (V9.03) from DumpsBase. All the practice questions in V9.03 are created and evaluated by certified professionals. This means every question has been carefully inspected for accuracy and relevance. If you want to feel them before downloading the full version, you can read the <a href=\"https:\/\/www.dumpsbase.com\/freedumps\/latest-ncp-aii-dumps-v9-03-for-smooth-and-efficient-exam-preparation-read-nvidia-ncp-aii-free-dumps-part-1-q1-q40.html\"><em><strong>NCP-AII free dumps (Part 1, Q1-Q40) of V9.03<\/strong><\/em><\/a> first. From these demo questions, you can trust that our dumps stay current with the evolving exam patterns and topics. With the NCP-AII dumps (V9.03), you&#8217;re guaranteed access to the latest content, ensuring no surprises come exam day. Today, we will continue to share more demos online. Then you can read them to check more about the V9.03.<\/p>\n<h2>Below are our <span style=\"background-color: #ffff99;\"><em>NCP-AII free dumps (Part 2, Q41-Q80) of V9.03<\/em><\/span> for checking more:<\/h2>\n<script>\n\t  window.fbAsyncInit = function() {\n\t    FB.init({\n\t      appId            : '622169541470367',\n\t      autoLogAppEvents : true,\n\t      xfbml            : true,\n\t      version          : 'v3.1'\n\t    });\n\t  };\n\t\n\t  (function(d, s, id){\n\t     var js, fjs = d.getElementsByTagName(s)[0];\n\t     if (d.getElementById(id)) {return;}\n\t     js = d.createElement(s); js.id = id;\n\t     js.src = \"https:\/\/connect.facebook.net\/en_US\/sdk.js\";\n\t     fjs.parentNode.insertBefore(js, fjs);\n\t   }(document, 'script', 'facebook-jssdk'));\n\t<\/script><script type=\"text\/javascript\" >\ndocument.addEventListener(\"DOMContentLoaded\", function(event) { \nif(!window.jQuery) alert(\"The important jQuery library is not properly loaded in your site. Your WordPress theme is probably missing the essential wp_head() call. You can switch to another theme and you will see that the plugin works fine and this notice disappears. If you are still not sure what to do you can contact us for help.\");\n});\n<\/script>  \n  \n<div  id=\"watupro_quiz\" class=\"quiz-area single-page-quiz\">\n<p id=\"submittingExam11331\" style=\"display:none;text-align:center;\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/plugins\/watupro\/img\/loading.gif\" width=\"16\" height=\"16\"><\/p>\n\n<div class=\"watupro-exam-description\" id=\"description-quiz-11331\"><\/div>\n\n<form action=\"\" method=\"post\" class=\"quiz-form\" id=\"quiz-11331\"  enctype=\"multipart\/form-data\" >\n<div class='watu-question ' id='question-1' style=';'><div id='questionWrap-1'  class='   watupro-question-id-445408'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>1. <\/span>A data center is designed for A1 training with a high degree of east-west traffic. Considering cost and performance, which network topology is generally the most suitable?<\/div><input type='hidden' name='question_id[]' id='qID_1' value='445408' \/><input type='hidden' id='answerType445408' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445408[]' id='answer-id-1723320' class='answer   answerof-445408 ' value='1723320'   \/><label for='answer-id-1723320' id='answer-label-1723320' class=' answer'><span>Spine-Leaf<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445408[]' id='answer-id-1723321' class='answer   answerof-445408 ' value='1723321'   \/><label for='answer-id-1723321' id='answer-label-1723321' class=' answer'><span>Three-Tier<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445408[]' id='answer-id-1723322' class='answer   answerof-445408 ' value='1723322'   \/><label for='answer-id-1723322' id='answer-label-1723322' class=' answer'><span>Ring<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445408[]' id='answer-id-1723323' class='answer   answerof-445408 ' value='1723323'   \/><label for='answer-id-1723323' id='answer-label-1723323' class=' answer'><span>Bus<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445408[]' id='answer-id-1723324' class='answer   answerof-445408 ' value='1723324'   \/><label for='answer-id-1723324' id='answer-label-1723324' class=' answer'><span>Mesh<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-2' style=';'><div id='questionWrap-2'  class='   watupro-question-id-445409'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>2. <\/span>Which of the following are valid methods for verifying the health and connectivity of InfiniBand links in an NCP-AII environment? (Select TWO)<\/div><input type='hidden' name='question_id[]' id='qID_2' value='445409' \/><input type='hidden' id='answerType445409' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445409[]' id='answer-id-1723325' class='answer   answerof-445409 ' value='1723325'   \/><label for='answer-id-1723325' id='answer-label-1723325' class=' answer'><span>Using \u2018ping\u2019 to test basic IP connectivity over the InfiniBand interface.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445409[]' id='answer-id-1723326' class='answer   answerof-445409 ' value='1723326'   \/><label for='answer-id-1723326' id='answer-label-1723326' class=' answer'><span>Using \u2018ibstat\u2019 to check the link state, physical state, and other relevant parameters of InfiniBand ports.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445409[]' id='answer-id-1723327' class='answer   answerof-445409 ' value='1723327'   \/><label for='answer-id-1723327' id='answer-label-1723327' class=' answer'><span>Using \u2018netstat\u2019 to check TCP connections.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445409[]' id='answer-id-1723328' class='answer   answerof-445409 ' value='1723328'   \/><label for='answer-id-1723328' id='answer-label-1723328' class=' answer'><span>Using \u2018sminfo\u2019 to query the Subnet Manager for network topology and status information.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445409[]' id='answer-id-1723329' class='answer   answerof-445409 ' value='1723329'   \/><label for='answer-id-1723329' id='answer-label-1723329' class=' answer'><span>Checking the system logs ( \u2018 \/var\/log\/messages\u2019 or equivalent) for any InfiniBand-related error messages.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-3' style=';'><div id='questionWrap-3'  class='   watupro-question-id-445410'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>3. <\/span>You\u2019re optimizing an AMD EPYC server with 4 NVIDIAAIOO GPUs for a large language model training workload. You observe that the GPUs are consistently underutilized (50-60% utilization) while the CPUs are nearly maxed out. <br \/>\r<br>Which of the following is the MOST likely bottleneck?<\/div><input type='hidden' name='question_id[]' id='qID_3' value='445410' \/><input type='hidden' id='answerType445410' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445410[]' id='answer-id-1723330' class='answer   answerof-445410 ' value='1723330'   \/><label for='answer-id-1723330' id='answer-label-1723330' class=' answer'><span>Insufficient CPU cores to prepare and feed data to the GPUs.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445410[]' id='answer-id-1723331' class='answer   answerof-445410 ' value='1723331'   \/><label for='answer-id-1723331' id='answer-label-1723331' class=' answer'><span>The PCle interconnect between the CPUs and GPUs is saturated.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445410[]' id='answer-id-1723332' class='answer   answerof-445410 ' value='1723332'   \/><label for='answer-id-1723332' id='answer-label-1723332' class=' answer'><span>The system RAM is too small, causing excessive swapping.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445410[]' id='answer-id-1723333' class='answer   answerof-445410 ' value='1723333'   \/><label for='answer-id-1723333' id='answer-label-1723333' class=' answer'><span>The storage system (SSD\/NVMe) is too slow, leading to data starvation.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445410[]' id='answer-id-1723334' class='answer   answerof-445410 ' value='1723334'   \/><label for='answer-id-1723334' id='answer-label-1723334' class=' answer'><span>The NCCL (NVIDIA Collective Communications Library) is not properly configured for inter-GPU communication.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-4' style=';'><div id='questionWrap-4'  class='   watupro-question-id-445411'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>4. <\/span>A large A1 model is training using a dataset stored on a network-attached storage (NAS) device. The data transfer speeds are significantly lower than expected. After initial troubleshooting, you discover that the MTU (Maximum Transmission Unit) size on the network interfaces of the training server and the NAS device are mismatched. The server is configured with an MTIJ of 1500, while the NAS device is configured with an MTU of 9000 (Jumbo Frames). <br \/>\r<br>What is the MOST likely consequence of this MTU mismatch, and what action should you take?<\/div><input type='hidden' name='question_id[]' id='qID_4' value='445411' \/><input type='hidden' id='answerType445411' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445411[]' id='answer-id-1723335' class='answer   answerof-445411 ' value='1723335'   \/><label for='answer-id-1723335' id='answer-label-1723335' class=' answer'><span>Data packets will be fragmented, leading to increased overhead and reduced performance. Configure both the server and the NAS device to use the same MTU size (either 1500 or 9000).<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445411[]' id='answer-id-1723336' class='answer   answerof-445411 ' value='1723336'   \/><label for='answer-id-1723336' id='answer-label-1723336' class=' answer'><span>The connection between the server and the NAS device will be unreliable, resulting in data corruption. Increase the MTU size on both devices to the maximum supported value.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445411[]' id='answer-id-1723337' class='answer   answerof-445411 ' value='1723337'   \/><label for='answer-id-1723337' id='answer-label-1723337' class=' answer'><span>The server will be unable to communicate with the NAS device. Reduce the MTU size on the server to match the MTU size of the NAS device.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445411[]' id='answer-id-1723338' class='answer   answerof-445411 ' value='1723338'   \/><label for='answer-id-1723338' id='answer-label-1723338' class=' answer'><span>The data transfer will be limited to the lowest common MTU size, but there will be no significant performance impact. No action is required.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445411[]' id='answer-id-1723339' class='answer   answerof-445411 ' value='1723339'   \/><label for='answer-id-1723339' id='answer-label-1723339' class=' answer'><span>Data packets will be retransmitted, increasing the latency but still getting the full throughput. Configure the server to use Path MTU Discovery (PMTUD).<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-5' style=';'><div id='questionWrap-5'  class='   watupro-question-id-445412'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>5. <\/span>Given the following \u2018nvswitch-cli\u2019 output, what does the \u2018Link Speed\u2019 indicate, and what potential bottleneck might a low \u2018Link Speed\u2019 suggest?<\/div><input type='hidden' name='question_id[]' id='qID_5' value='445412' \/><input type='hidden' id='answerType445412' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445412[]' id='answer-id-1723340' class='answer   answerof-445412 ' value='1723340'   \/><label for='answer-id-1723340' id='answer-label-1723340' class=' answer'><span>It indicates the effective bandwidth of the NVLink connection; a low value suggests a potential cable issue or misconfiguration.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445412[]' id='answer-id-1723341' class='answer   answerof-445412 ' value='1723341'   \/><label for='answer-id-1723341' id='answer-label-1723341' class=' answer'><span>It indicates the clock speed of the GPU memory; a low value suggests a memory bottleneck.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445412[]' id='answer-id-1723342' class='answer   answerof-445412 ' value='1723342'   \/><label for='answer-id-1723342' id='answer-label-1723342' class=' answer'><span>It indicates the PCle generation supported by the GPIJ; a low value suggests an outdated GP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445412[]' id='answer-id-1723343' class='answer   answerof-445412 ' value='1723343'   \/><label for='answer-id-1723343' id='answer-label-1723343' class=' answer'><span>It indicates the NVLink protocol version; a low value suggests firmware incompatibility.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445412[]' id='answer-id-1723344' class='answer   answerof-445412 ' value='1723344'   \/><label for='answer-id-1723344' id='answer-label-1723344' class=' answer'><span>It indicates the power consumption of the NVLink switch; a high value suggests overheating issues.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-6' style=';'><div id='questionWrap-6'  class='   watupro-question-id-445413'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>6. <\/span>In an InfiniBand fabric, what is the primary role of the Subnet Manager (SM) with respect to routing?<\/div><input type='hidden' name='question_id[]' id='qID_6' value='445413' \/><input type='hidden' id='answerType445413' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445413[]' id='answer-id-1723345' class='answer   answerof-445413 ' value='1723345'   \/><label for='answer-id-1723345' id='answer-label-1723345' class=' answer'><span>To forward packets based on destination IP addresses, similar to a traditional IP router.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445413[]' id='answer-id-1723346' class='answer   answerof-445413 ' value='1723346'   \/><label for='answer-id-1723346' id='answer-label-1723346' class=' answer'><span>To discover the network topology, calculate routing paths, and program the forwarding tables (LID tables) in the switches.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445413[]' id='answer-id-1723347' class='answer   answerof-445413 ' value='1723347'   \/><label for='answer-id-1723347' id='answer-label-1723347' class=' answer'><span>To monitor the network for congestion and dynamically adjust packet priorities using Quality of Service (QOS) mechanisms.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445413[]' id='answer-id-1723348' class='answer   answerof-445413 ' value='1723348'   \/><label for='answer-id-1723348' id='answer-label-1723348' class=' answer'><span>To provide a command-line interface for users to manually configure routing tables on each InfiniBand switch.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445413[]' id='answer-id-1723349' class='answer   answerof-445413 ' value='1723349'   \/><label for='answer-id-1723349' id='answer-label-1723349' class=' answer'><span>To act as a firewall, blocking unauthorized traffic based on pre-defined rules.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-7' style=';'><div id='questionWrap-7'  class='   watupro-question-id-445414'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>7. <\/span>You are tasked with ensuring optimal power efficiency for a GPU server running machine learning workloads. You want to dynamically adjust the GPU\u2019s power consumption based on its utilization. <br \/>\r<br>Which of the following methods is the MOST suitable for achieving this, assuming the server\u2019s BIOS and the NVIDIA drivers support it?<\/div><input type='hidden' name='question_id[]' id='qID_7' value='445414' \/><input type='hidden' id='answerType445414' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445414[]' id='answer-id-1723350' class='answer   answerof-445414 ' value='1723350'   \/><label for='answer-id-1723350' id='answer-label-1723350' class=' answer'><span>Manually set the GPU\u2019s power limit using \u2018nvidia-smi -pl and create a script to monitor utilization and adjust the power limit periodically.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445414[]' id='answer-id-1723351' class='answer   answerof-445414 ' value='1723351'   \/><label for='answer-id-1723351' id='answer-label-1723351' class=' answer'><span>Configure the server\u2019s BIOS\/UEFI to use a power-saving profile, which will automatically reduce the GPU\u2019s power consumption when idle.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445414[]' id='answer-id-1723352' class='answer   answerof-445414 ' value='1723352'   \/><label for='answer-id-1723352' id='answer-label-1723352' class=' answer'><span>Enable Dynamic Boost in the NVIDIA Control Panel (if available), which will automatically allocate power between the CPU and GPU based on their current needs.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445414[]' id='answer-id-1723353' class='answer   answerof-445414 ' value='1723353'   \/><label for='answer-id-1723353' id='answer-label-1723353' class=' answer'><span>Use NVIDIA\u2019s Data Center GPU Manager (DCGM) to monitor GPU utilization and dynamically adjust the power limit based on a predefined policy.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445414[]' id='answer-id-1723354' class='answer   answerof-445414 ' value='1723354'   \/><label for='answer-id-1723354' id='answer-label-1723354' class=' answer'><span>Disable ECC (Error Correcting Code) on the GPU to reduce power consumption.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-8' style=';'><div id='questionWrap-8'  class='   watupro-question-id-445415'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>8. <\/span>Which of the following statements are true regarding the use of Congestion Management (CM) and Congestion Avoidance (CA) techniques within an InfiniBand fabric using NVIDIA technology? (Select TWO)<\/div><input type='hidden' name='question_id[]' id='qID_8' value='445415' \/><input type='hidden' id='answerType445415' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445415[]' id='answer-id-1723355' class='answer   answerof-445415 ' value='1723355'   \/><label for='answer-id-1723355' id='answer-label-1723355' class=' answer'><span>CM\/CA mechanisms are primarily implemented at the IP layer and are independent of the InfiniBand transport layer.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445415[]' id='answer-id-1723356' class='answer   answerof-445415 ' value='1723356'   \/><label for='answer-id-1723356' id='answer-label-1723356' class=' answer'><span>CM aims to reduce the severity of congestion once it has already occurred, while CA aims to prevent congestion from happening in the first place.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445415[]' id='answer-id-1723357' class='answer   answerof-445415 ' value='1723357'   \/><label for='answer-id-1723357' id='answer-label-1723357' class=' answer'><span>InfiniBand\u2019s Explicit Congestion Notification (ECN) is a CA mechanism that allows switches to signal congestion to endpoints before packet loss occurs.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445415[]' id='answer-id-1723358' class='answer   answerof-445415 ' value='1723358'   \/><label for='answer-id-1723358' id='answer-label-1723358' class=' answer'><span>CM\/CA are not relevant in InfiniBand fabrics because InfiniBand\u2019s lossless nature guarantees that no packets will ever be dropped due to congestion.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445415[]' id='answer-id-1723359' class='answer   answerof-445415 ' value='1723359'   \/><label for='answer-id-1723359' id='answer-label-1723359' class=' answer'><span>CM can include techniques like rate limiting to throttle traffic flows when congestion is detected.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-9' style=';'><div id='questionWrap-9'  class='   watupro-question-id-445416'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>9. <\/span>You are troubleshooting a network performance issue in your NVIDIA Spectrum-X based A1 cluster. You suspect that the Equal-Cost Multi-Path (ECMP) hashing algorithm is not distributing traffic evenly across available paths, leading to congestion on some links. <br \/>\r<br>Which of the following methods would be MOST effective for verifying and addressing this issue?<\/div><input type='hidden' name='question_id[]' id='qID_9' value='445416' \/><input type='hidden' id='answerType445416' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445416[]' id='answer-id-1723360' class='answer   answerof-445416 ' value='1723360'   \/><label for='answer-id-1723360' id='answer-label-1723360' class=' answer'><span>Use \u2018ping\u2019 or \u2018traceroute\u2019 to analyze the paths taken by packets between the affected nodes. If they always take the same path, ECMP is likely not working correctly.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445416[]' id='answer-id-1723361' class='answer   answerof-445416 ' value='1723361'   \/><label for='answer-id-1723361' id='answer-label-1723361' class=' answer'><span>Use switch telemetry tools (e.g., NVIDIA What\u2019s Up Gold, Mellanox NEO, or similar) to monitor link utilization across all available paths between the nodes. Look for significant imbalances in traffic volume.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445416[]' id='answer-id-1723362' class='answer   answerof-445416 ' value='1723362'   \/><label for='answer-id-1723362' id='answer-label-1723362' class=' answer'><span>Restart the switches to force the ECMP hashing algorithm to recalculate paths.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445416[]' id='answer-id-1723363' class='answer   answerof-445416 ' value='1723363'   \/><label for='answer-id-1723363' id='answer-label-1723363' class=' answer'><span>Disable ECMP entirely and rely solely on static routing.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445416[]' id='answer-id-1723364' class='answer   answerof-445416 ' value='1723364'   \/><label for='answer-id-1723364' id='answer-label-1723364' class=' answer'><span>Reduce the TCP window size.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-10' style=';'><div id='questionWrap-10'  class='   watupro-question-id-445417'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>10. <\/span>1.A GPU in your AI server consistently overheats during inference workloads. You\u2019ve ruled out inadequate cooling and software bugs. <br \/>\r<br>Running \u2018nvidia-smi\u2019 shows high power draw even when idle. <br \/>\r<br>Which of the following hardware issues are the most likely causes?<\/div><input type='hidden' name='question_id[]' id='qID_10' value='445417' \/><input type='hidden' id='answerType445417' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445417[]' id='answer-id-1723365' class='answer   answerof-445417 ' value='1723365'   \/><label for='answer-id-1723365' id='answer-label-1723365' class=' answer'><span>Degraded thermal paste between the GPU die and the heatsink.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445417[]' id='answer-id-1723366' class='answer   answerof-445417 ' value='1723366'   \/><label for='answer-id-1723366' id='answer-label-1723366' class=' answer'><span>A failing voltage regulator module (VRM) on the GPU board, causing excessive power leakage.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445417[]' id='answer-id-1723367' class='answer   answerof-445417 ' value='1723367'   \/><label for='answer-id-1723367' id='answer-label-1723367' class=' answer'><span>Incorrectly seated GPU in the PCle slot, leading to poor power delivery.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445417[]' id='answer-id-1723368' class='answer   answerof-445417 ' value='1723368'   \/><label for='answer-id-1723368' id='answer-label-1723368' class=' answer'><span>A BIOS setting that is overvolting the GP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445417[]' id='answer-id-1723369' class='answer   answerof-445417 ' value='1723369'   \/><label for='answer-id-1723369' id='answer-label-1723369' class=' answer'><span>Insufficient system RA<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-11' style=';'><div id='questionWrap-11'  class='   watupro-question-id-445418'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>11. <\/span>You need to verify the NVLink connectivity between GPUs in a DGX server. <br \/>\r<br>Which command-line utility is the MOST reliable and provides detailed NVLink status?<\/div><input type='hidden' name='question_id[]' id='qID_11' value='445418' \/><input type='hidden' id='answerType445418' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445418[]' id='answer-id-1723370' class='answer   answerof-445418 ' value='1723370'   \/><label for='answer-id-1723370' id='answer-label-1723370' class=' answer'><span>nvidia-smi<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445418[]' id='answer-id-1723371' class='answer   answerof-445418 ' value='1723371'   \/><label for='answer-id-1723371' id='answer-label-1723371' class=' answer'><span>Ispci<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445418[]' id='answer-id-1723372' class='answer   answerof-445418 ' value='1723372'   \/><label for='answer-id-1723372' id='answer-label-1723372' class=' answer'><span>nvlink_info (Hypothetical command)<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445418[]' id='answer-id-1723373' class='answer   answerof-445418 ' value='1723373'   \/><label for='answer-id-1723373' id='answer-label-1723373' class=' answer'><span>gpustat<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445418[]' id='answer-id-1723374' class='answer   answerof-445418 ' value='1723374'   \/><label for='answer-id-1723374' id='answer-label-1723374' class=' answer'><span>dcgmi diag -t 1004<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-12' style=';'><div id='questionWrap-12'  class='   watupro-question-id-445419'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>12. <\/span>You\u2019re optimizing an Intel Xeon server with 4 NVIDIA GPUs for inference serving using Triton Inference Server. You\u2019ve deployed multiple models concurrently. You observe that the overall throughput is lower than expected, and the GPU utilization is not consistently high. <br \/>\r<br>What are potential bottlenecks and optimization strategies? (Select all that apply)<\/div><input type='hidden' name='question_id[]' id='qID_12' value='445419' \/><input type='hidden' id='answerType445419' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445419[]' id='answer-id-1723375' class='answer   answerof-445419 ' value='1723375'   \/><label for='answer-id-1723375' id='answer-label-1723375' class=' answer'><span>Model loading and unloading overhead. Use model ensemble or dynamic batching to reduce frequency.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445419[]' id='answer-id-1723376' class='answer   answerof-445419 ' value='1723376'   \/><label for='answer-id-1723376' id='answer-label-1723376' class=' answer'><span>Insufficient CPU cores to handle the model loading and preprocessing requests. Increase the number of Triton instance groups for CPU-based models.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445419[]' id='answer-id-1723377' class='answer   answerof-445419 ' value='1723377'   \/><label for='answer-id-1723377' id='answer-label-1723377' class=' answer'><span>The models are memory-bound. Reduce the model precision (e.g., FP32 to FP16 or INT8).<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445419[]' id='answer-id-1723378' class='answer   answerof-445419 ' value='1723378'   \/><label for='answer-id-1723378' id='answer-label-1723378' class=' answer'><span>The GPUs are underutilized due to small batch sizes. Implement dynamic batching to increase batch sizes.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445419[]' id='answer-id-1723379' class='answer   answerof-445419 ' value='1723379'   \/><label for='answer-id-1723379' id='answer-label-1723379' class=' answer'><span>Insufficient PCle bandwidth between CPU and GPIJs. Reconfigure PCle lanes to improve bandwidth allocation to each GPI<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-13' style=';'><div id='questionWrap-13'  class='   watupro-question-id-445420'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>13. <\/span>When setting up a multi-server, multi-GPU environment using NVLink switches, what is the primary consideration when planning the network topology for optimal performance?<\/div><input type='hidden' name='question_id[]' id='qID_13' value='445420' \/><input type='hidden' id='answerType445420' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445420[]' id='answer-id-1723380' class='answer   answerof-445420 ' value='1723380'   \/><label for='answer-id-1723380' id='answer-label-1723380' class=' answer'><span>Minimizing the number of hops between GPUs that need to communicate frequently.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445420[]' id='answer-id-1723381' class='answer   answerof-445420 ' value='1723381'   \/><label for='answer-id-1723381' id='answer-label-1723381' class=' answer'><span>Maximizing the distance between servers to improve cooling.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445420[]' id='answer-id-1723382' class='answer   answerof-445420 ' value='1723382'   \/><label for='answer-id-1723382' id='answer-label-1723382' class=' answer'><span>Using a star topology for simplified management.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445420[]' id='answer-id-1723383' class='answer   answerof-445420 ' value='1723383'   \/><label for='answer-id-1723383' id='answer-label-1723383' class=' answer'><span>Ensuring all servers are on the same subnet for ease of configuration.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445420[]' id='answer-id-1723384' class='answer   answerof-445420 ' value='1723384'   \/><label for='answer-id-1723384' id='answer-label-1723384' class=' answer'><span>Placing servers near the network\u2019s edge to reduce latency.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-14' style=';'><div id='questionWrap-14'  class='   watupro-question-id-445421'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>14. <\/span>You are deploying a new A1 inference service using Triton Inference Server on a multi-GPU system. After deploying the models, you observe that only one GPU is being utilized, even though the models are configured to use multiple GPUs. <br \/>\r<br>What could be the possible causes for this?<\/div><input type='hidden' name='question_id[]' id='qID_14' value='445421' \/><input type='hidden' id='answerType445421' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445421[]' id='answer-id-1723385' class='answer   answerof-445421 ' value='1723385'   \/><label for='answer-id-1723385' id='answer-label-1723385' class=' answer'><span>The model configuration file does not specify the \u2018instance_group\u2019 parameter correctly to utilize multiple GPUs.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445421[]' id='answer-id-1723386' class='answer   answerof-445421 ' value='1723386'   \/><label for='answer-id-1723386' id='answer-label-1723386' class=' answer'><span>The Triton Inference Server is not configured to enable CUDA Multi-Process Service (MPS).<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445421[]' id='answer-id-1723387' class='answer   answerof-445421 ' value='1723387'   \/><label for='answer-id-1723387' id='answer-label-1723387' class=' answer'><span>Insufficient CPU cores are available for the Triton Inference Server, limiting its ability to spawn multiple inference processes.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445421[]' id='answer-id-1723388' class='answer   answerof-445421 ' value='1723388'   \/><label for='answer-id-1723388' id='answer-label-1723388' class=' answer'><span>The models are not optimized for multi-GPU inference, resulting in a single GPU bottleneck.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445421[]' id='answer-id-1723389' class='answer   answerof-445421 ' value='1723389'   \/><label for='answer-id-1723389' id='answer-label-1723389' class=' answer'><span>The GPUs are not of the same type and Triton cannot properly schedule across them.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-15' style=';'><div id='questionWrap-15'  class='   watupro-question-id-445422'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>15. <\/span>You are setting up network fabric ports for hosts in an NVIDIA-Certified Professional A1 Infrastructure (NCP-AII) environment. You need to configure Jumbo Frames to improve network throughput. <br \/>\r<br>What is the typical MTU (Maximum Transmission Unit) size you would set on the network interfaces and switches, and why?<\/div><input type='hidden' name='question_id[]' id='qID_15' value='445422' \/><input type='hidden' id='answerType445422' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445422[]' id='answer-id-1723390' class='answer   answerof-445422 ' value='1723390'   \/><label for='answer-id-1723390' id='answer-label-1723390' class=' answer'><span>1500 bytes, as it\u2019s the default and compatible with most networks.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445422[]' id='answer-id-1723391' class='answer   answerof-445422 ' value='1723391'   \/><label for='answer-id-1723391' id='answer-label-1723391' class=' answer'><span>9000 bytes, also known as Jumbo Frames, reduces overhead and improves throughput for large data transfers.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445422[]' id='answer-id-1723392' class='answer   answerof-445422 ' value='1723392'   \/><label for='answer-id-1723392' id='answer-label-1723392' class=' answer'><span>65535 bytes, the theoretical maximum MTU size, for maximum performance.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445422[]' id='answer-id-1723393' class='answer   answerof-445422 ' value='1723393'   \/><label for='answer-id-1723393' id='answer-label-1723393' class=' answer'><span>576 bytes, the minimum MTU size required by IPv4.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445422[]' id='answer-id-1723394' class='answer   answerof-445422 ' value='1723394'   \/><label for='answer-id-1723394' id='answer-label-1723394' class=' answer'><span>Any MTU size between 1500 and 9000 bytes; the specific value doesn\u2019t matter.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-16' style=';'><div id='questionWrap-16'  class='   watupro-question-id-445423'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>16. <\/span>You are troubleshooting slow I\/O performance in a deep learning training environment utilizing BeeGFS parallel file system. You suspect the metadata operations are bottlenecking the training process. <br \/>\r<br>How can you optimize metadata handling in BeeGFS to potentially improve performance?<\/div><input type='hidden' name='question_id[]' id='qID_16' value='445423' \/><input type='hidden' id='answerType445423' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445423[]' id='answer-id-1723395' class='answer   answerof-445423 ' value='1723395'   \/><label for='answer-id-1723395' id='answer-label-1723395' class=' answer'><span>Increase the number of storage targets (OSTs) to distribute the data across more devices.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445423[]' id='answer-id-1723396' class='answer   answerof-445423 ' value='1723396'   \/><label for='answer-id-1723396' id='answer-label-1723396' class=' answer'><span>Implement data striping across multiple OSTs.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445423[]' id='answer-id-1723397' class='answer   answerof-445423 ' value='1723397'   \/><label for='answer-id-1723397' id='answer-label-1723397' class=' answer'><span>Increase the number of metadata servers (MDSs) and distribute the metadata load across them.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445423[]' id='answer-id-1723398' class='answer   answerof-445423 ' value='1723398'   \/><label for='answer-id-1723398' id='answer-label-1723398' class=' answer'><span>Enable client-side caching of metadata on the training nodes.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445423[]' id='answer-id-1723399' class='answer   answerof-445423 ' value='1723399'   \/><label for='answer-id-1723399' id='answer-label-1723399' class=' answer'><span>Configure BeeGFS to use a different network protocol with lower overhead.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-17' style=';'><div id='questionWrap-17'  class='   watupro-question-id-445424'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>17. <\/span>You observe high latency and low bandwidth between two GPUs connected via an NVLink switch. You suspect a problem with the NVLink link itself. <br \/>\r<br>Which of the following methods would be the most effective in diagnosing the physical NVLink link health?<\/div><input type='hidden' name='question_id[]' id='qID_17' value='445424' \/><input type='hidden' id='answerType445424' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445424[]' id='answer-id-1723400' class='answer   answerof-445424 ' value='1723400'   \/><label for='answer-id-1723400' id='answer-label-1723400' class=' answer'><span>Using \u2018iperf3\u2019 to measure network throughput between the servers.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445424[]' id='answer-id-1723401' class='answer   answerof-445424 ' value='1723401'   \/><label for='answer-id-1723401' id='answer-label-1723401' class=' answer'><span>Running a CUDA-aware memory bandwidth test specifically designed for NVLink.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445424[]' id='answer-id-1723402' class='answer   answerof-445424 ' value='1723402'   \/><label for='answer-id-1723402' id='answer-label-1723402' class=' answer'><span>Examining system logs for NVLink-related error messages.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445424[]' id='answer-id-1723403' class='answer   answerof-445424 ' value='1723403'   \/><label for='answer-id-1723403' id='answer-label-1723403' class=' answer'><span>Using \u2018ping\u2019 to check network connectivity between the servers.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445424[]' id='answer-id-1723404' class='answer   answerof-445424 ' value='1723404'   \/><label for='answer-id-1723404' id='answer-label-1723404' class=' answer'><span>Physically inspecting the NVLink cables for damage.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-18' style=';'><div id='questionWrap-18'  class='   watupro-question-id-445425'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>18. <\/span>A user reports that their deep learning training job is crashing with a \u2018CUDA out of memory\u2019 error, even though \u2018nvidia-smi\u2019 shows plenty of free memory on the GPU. The job uses TensorFlow. <br \/>\r<br>What are the TWO most likely causes?<\/div><input type='hidden' name='question_id[]' id='qID_18' value='445425' \/><input type='hidden' id='answerType445425' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445425[]' id='answer-id-1723405' class='answer   answerof-445425 ' value='1723405'   \/><label for='answer-id-1723405' id='answer-label-1723405' class=' answer'><span>The TensorFlow version is incompatible with the installed NVIDIA driver.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445425[]' id='answer-id-1723406' class='answer   answerof-445425 ' value='1723406'   \/><label for='answer-id-1723406' id='answer-label-1723406' class=' answer'><span>TensorFlow is allocating memory on the CPU instead of the GP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445425[]' id='answer-id-1723407' class='answer   answerof-445425 ' value='1723407'   \/><label for='answer-id-1723407' id='answer-label-1723407' class=' answer'><span>TensorFlow is fragmenting GPU memory, making it difficult to allocate contiguous blocks.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445425[]' id='answer-id-1723408' class='answer   answerof-445425 ' value='1723408'   \/><label for='answer-id-1723408' id='answer-label-1723408' class=' answer'><span>The CUDA VISIBLE DEVICES environment variable is not set correctly.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445425[]' id='answer-id-1723409' class='answer   answerof-445425 ' value='1723409'   \/><label for='answer-id-1723409' id='answer-label-1723409' class=' answer'><span>The system\u2019s swap space is full, preventing memory from being allocated.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-19' style=';'><div id='questionWrap-19'  class='   watupro-question-id-445426'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>19. <\/span>You are configuring a server with multiple GPUs for CUDA-aware MPI. <br \/>\r<br>Which environment variable is critical for ensuring proper GPU affinity, so that each MPI process uses the correct GPU?<\/div><input type='hidden' name='question_id[]' id='qID_19' value='445426' \/><input type='hidden' id='answerType445426' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445426[]' id='answer-id-1723410' class='answer   answerof-445426 ' value='1723410'   \/><label for='answer-id-1723410' id='answer-label-1723410' class=' answer'><span>CUDA VISIBLE DEVICES<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445426[]' id='answer-id-1723411' class='answer   answerof-445426 ' value='1723411'   \/><label for='answer-id-1723411' id='answer-label-1723411' class=' answer'><span>CUDA DEVICE ORDER<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445426[]' id='answer-id-1723412' class='answer   answerof-445426 ' value='1723412'   \/><label for='answer-id-1723412' id='answer-label-1723412' class=' answer'><span>LD LIBRARY PATH<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445426[]' id='answer-id-1723413' class='answer   answerof-445426 ' value='1723413'   \/><label for='answer-id-1723413' id='answer-label-1723413' class=' answer'><span>MPI GPU SUPPORT<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445426[]' id='answer-id-1723414' class='answer   answerof-445426 ' value='1723414'   \/><label for='answer-id-1723414' id='answer-label-1723414' class=' answer'><span>CUDA LAUNCH BLOCKING-I<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-20' style=';'><div id='questionWrap-20'  class='   watupro-question-id-445427'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>20. <\/span>In a large-scale InfiniBand fabric, you need to implement a mechanism to prioritize traffic for a specific application that requires low latency and high bandwidth. You want to leverage Quality of Service (QOS) to achieve this. <br \/>\r<br>Which of the following steps are essential to properly configure QOS in this scenario? (Select THREE)<\/div><input type='hidden' name='question_id[]' id='qID_20' value='445427' \/><input type='hidden' id='answerType445427' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445427[]' id='answer-id-1723415' class='answer   answerof-445427 ' value='1723415'   \/><label for='answer-id-1723415' id='answer-label-1723415' class=' answer'><span>Configure VLAN tagging on the application\u2019s traffic to isolate it from other traffic.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445427[]' id='answer-id-1723416' class='answer   answerof-445427 ' value='1723416'   \/><label for='answer-id-1723416' id='answer-label-1723416' class=' answer'><span>Map the application\u2019s traffic to a specific traffic class with appropriate priority settings within the InfiniBand switches.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445427[]' id='answer-id-1723417' class='answer   answerof-445427 ' value='1723417'   \/><label for='answer-id-1723417' id='answer-label-1723417' class=' answer'><span>Configure Weighted Fair Queueing (WFQ) or Strict Priority Queueing on the egress ports of the InfiniBand switches to prioritize the application\u2019s traffic class.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445427[]' id='answer-id-1723418' class='answer   answerof-445427 ' value='1723418'   \/><label for='answer-id-1723418' id='answer-label-1723418' class=' answer'><span>Disable Adaptive Routing (AR) to ensure that the application\u2019s traffic always takes the shortest path.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445427[]' id='answer-id-1723419' class='answer   answerof-445427 ' value='1723419'   \/><label for='answer-id-1723419' id='answer-label-1723419' class=' answer'><span>Mark the application\u2019s traffic with appropriate DiffServ Code Point (DSCP) values.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-21' style=';'><div id='questionWrap-21'  class='   watupro-question-id-445428'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>21. <\/span>Which of the following are key benefits of using NVIDIA Spectrum-X switches in an A1 infrastructure compared to traditional Ethernet switches? (Select THREE)<\/div><input type='hidden' name='question_id[]' id='qID_21' value='445428' \/><input type='hidden' id='answerType445428' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445428[]' id='answer-id-1723420' class='answer   answerof-445428 ' value='1723420'   \/><label for='answer-id-1723420' id='answer-label-1723420' class=' answer'><span>Lower cost per port.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445428[]' id='answer-id-1723421' class='answer   answerof-445428 ' value='1723421'   \/><label for='answer-id-1723421' id='answer-label-1723421' class=' answer'><span>Support for RoCE (RDMA over Converged Ethernet) and InfiniBand protocols, enabling high-bandwidth, low-latency communication.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445428[]' id='answer-id-1723422' class='answer   answerof-445428 ' value='1723422'   \/><label for='answer-id-1723422' id='answer-label-1723422' class=' answer'><span>Advanced telemetry and monitoring capabilities for network performance optimization.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445428[]' id='answer-id-1723423' class='answer   answerof-445428 ' value='1723423'   \/><label for='answer-id-1723423' id='answer-label-1723423' class=' answer'><span>Hardware-based acceleration for collective communication operations used in distributed A1 training.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445428[]' id='answer-id-1723424' class='answer   answerof-445428 ' value='1723424'   \/><label for='answer-id-1723424' id='answer-label-1723424' class=' answer'><span>Native support for IPv6.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-22' style=';'><div id='questionWrap-22'  class='   watupro-question-id-445429'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>22. <\/span>Consider an AMD EPYC-based server with 8 NVIDIAAIOO GPUs connected via PCle Gen4. You\u2019re running a distributed training job using Horovod. You\u2019ve noticed that communication between GPUs is a bottleneck. <br \/>\r<br>Which of the following NCCL configuration options would be MOST beneficial in this scenario? (Assume all options are syntactically correct for NCCL).<\/div><input type='hidden' name='question_id[]' id='qID_22' value='445429' \/><input type='hidden' id='answerType445429' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445429[]' id='answer-id-1723425' class='answer   answerof-445429 ' value='1723425'   \/><label for='answer-id-1723425' id='answer-label-1723425' class=' answer'><span>NCCL SOCKET IF-NAME=eth0<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445429[]' id='answer-id-1723426' class='answer   answerof-445429 ' value='1723426'   \/><label for='answer-id-1723426' id='answer-label-1723426' class=' answer'><span>NCCL IB DISABLE-1<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445429[]' id='answer-id-1723427' class='answer   answerof-445429 ' value='1723427'   \/><label for='answer-id-1723427' id='answer-label-1723427' class=' answer'><span>NCCL P2P DISABLE-0<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445429[]' id='answer-id-1723428' class='answer   answerof-445429 ' value='1723428'   \/><label for='answer-id-1723428' id='answer-label-1723428' class=' answer'><span>NCCL IB HCA=mlx5 0<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445429[]' id='answer-id-1723429' class='answer   answerof-445429 ' value='1723429'   \/><label for='answer-id-1723429' id='answer-label-1723429' class=' answer'><span>NCCL NET PLUGIN=none<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-23' style=';'><div id='questionWrap-23'  class='   watupro-question-id-445430'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>23. <\/span>Consider a scenario where you\u2019re using GPUDirect Storage to enable direct memory access between GPUs and NVMe drives. You observe that while GPUDirect Storage is enabled, you\u2019re not seeing the expected performance gains. <br \/>\r<br>What are potential reasons and configurations you should check to ensure optimal GPUDirect Storage performance? Select all that apply.<\/div><input type='hidden' name='question_id[]' id='qID_23' value='445430' \/><input type='hidden' id='answerType445430' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445430[]' id='answer-id-1723430' class='answer   answerof-445430 ' value='1723430'   \/><label for='answer-id-1723430' id='answer-label-1723430' class=' answer'><span>Verify that the NVMe drives are properly configured in a RAID 0 configuration.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445430[]' id='answer-id-1723431' class='answer   answerof-445430 ' value='1723431'   \/><label for='answer-id-1723431' id='answer-label-1723431' class=' answer'><span>Ensure that the NVMe drives are connected to the system via PCle Gen4 or Gen5.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445430[]' id='answer-id-1723432' class='answer   answerof-445430 ' value='1723432'   \/><label for='answer-id-1723432' id='answer-label-1723432' class=' answer'><span>Confirm that the CUDA driver version is compatible with GPIJDirect Storage.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445430[]' id='answer-id-1723433' class='answer   answerof-445430 ' value='1723433'   \/><label for='answer-id-1723433' id='answer-label-1723433' class=' answer'><span>Check if the file system supports direct I\/O (e.g., using \u2018directio\u2019 mount option).<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445430[]' id='answer-id-1723434' class='answer   answerof-445430 ' value='1723434'   \/><label for='answer-id-1723434' id='answer-label-1723434' class=' answer'><span>Disable CPU-side caching to force all I\/O operations to go directly to the GPU memory.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-24' style=';'><div id='questionWrap-24'  class='   watupro-question-id-445431'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>24. <\/span>You are tasked with configuring an NVIDIA NVLink&#65533; Switch system. After physically connecting the GPUs and the switch, what is the typical first step in the software configuration process?<\/div><input type='hidden' name='question_id[]' id='qID_24' value='445431' \/><input type='hidden' id='answerType445431' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445431[]' id='answer-id-1723435' class='answer   answerof-445431 ' value='1723435'   \/><label for='answer-id-1723435' id='answer-label-1723435' class=' answer'><span>Installing the latest NVIDIA drivers on all connected GPUs.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445431[]' id='answer-id-1723436' class='answer   answerof-445431 ' value='1723436'   \/><label for='answer-id-1723436' id='answer-label-1723436' class=' answer'><span>Configuring the system BIOS to enable NVLink support.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445431[]' id='answer-id-1723437' class='answer   answerof-445431 ' value='1723437'   \/><label for='answer-id-1723437' id='answer-label-1723437' class=' answer'><span>Updating the firmware of the NVLink Switch.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445431[]' id='answer-id-1723438' class='answer   answerof-445431 ' value='1723438'   \/><label for='answer-id-1723438' id='answer-label-1723438' class=' answer'><span>Installing the NVLink Switch management software.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445431[]' id='answer-id-1723439' class='answer   answerof-445431 ' value='1723439'   \/><label for='answer-id-1723439' id='answer-label-1723439' class=' answer'><span>Running a memory bandwidth test between all connected GPUs.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-25' style=';'><div id='questionWrap-25'  class='   watupro-question-id-445432'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>25. <\/span>You are troubleshooting performance issues in an A1 training clusten You suspect network congestion. <br \/>\r<br>Which of the following network monitoring tools would be MOST helpful in identifying the source of the congestion?<\/div><input type='hidden' name='question_id[]' id='qID_25' value='445432' \/><input type='hidden' id='answerType445432' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445432[]' id='answer-id-1723440' class='answer   answerof-445432 ' value='1723440'   \/><label for='answer-id-1723440' id='answer-label-1723440' class=' answer'><span>Ping<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445432[]' id='answer-id-1723441' class='answer   answerof-445432 ' value='1723441'   \/><label for='answer-id-1723441' id='answer-label-1723441' class=' answer'><span>Traceroute<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445432[]' id='answer-id-1723442' class='answer   answerof-445432 ' value='1723442'   \/><label for='answer-id-1723442' id='answer-label-1723442' class=' answer'><span>iPerf\/Netperf<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445432[]' id='answer-id-1723443' class='answer   answerof-445432 ' value='1723443'   \/><label for='answer-id-1723443' id='answer-label-1723443' class=' answer'><span>tcpdump\/Wireshark<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445432[]' id='answer-id-1723444' class='answer   answerof-445432 ' value='1723444'   \/><label for='answer-id-1723444' id='answer-label-1723444' class=' answer'><span>netstat<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-26' style=';'><div id='questionWrap-26'  class='   watupro-question-id-445433'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>26. <\/span>You are troubleshooting a performance issue on an Intel Xeon server with NVIDIAAI 00 GPUs. Your application involves frequent data transfers between CPU memory and GPU memory. You suspect that the PCle bus is a bottleneck. <br \/>\r<br>How can you verify and mitigate this bottleneck?<\/div><input type='hidden' name='question_id[]' id='qID_26' value='445433' \/><input type='hidden' id='answerType445433' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445433[]' id='answer-id-1723445' class='answer   answerof-445433 ' value='1723445'   \/><label for='answer-id-1723445' id='answer-label-1723445' class=' answer'><span>Use \u2018nvidia-smi\u2019 to monitor the PCle bandwidth utilization of the GPUs. If it\u2019s consistently high (near the theoretical limit), the PCle bus is likely a bottleneck. Mitigate by reducing the frequency of CPU-GPU data transfers, using pinned (page-locked) memory, and ensuring that the GPUs are connected to PCle slots with sufficient bandwidth.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445433[]' id='answer-id-1723446' class='answer   answerof-445433 ' value='1723446'   \/><label for='answer-id-1723446' id='answer-label-1723446' class=' answer'><span>Check the CPU utilization. If it\u2019s low, the PCle bus is likely the bottleneck. Mitigate by increasing the number of CPU cores assigned to the data transfer tasks.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445433[]' id='answer-id-1723447' class='answer   answerof-445433 ' value='1723447'   \/><label for='answer-id-1723447' id='answer-label-1723447' class=' answer'><span>Examine the system logs for PCle errors. If there are many errors, the PCle bus is likely unstable. Mitigate by reseating the GPUs and checking the power supply.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445433[]' id='answer-id-1723448' class='answer   answerof-445433 ' value='1723448'   \/><label for='answer-id-1723448' id='answer-label-1723448' class=' answer'><span>Monitor the GPU temperature. If it\u2019s high, the PCle bus is likely overheating. Mitigate by improving the server\u2019s cooling.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445433[]' id='answer-id-1723449' class='answer   answerof-445433 ' value='1723449'   \/><label for='answer-id-1723449' id='answer-label-1723449' class=' answer'><span>Use \u2018nvprof to profile the application and identify the exact lines of code that are causing the high PCle traffic. Optimize those sections of code to reduce data transfers.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-27' style=';'><div id='questionWrap-27'  class='   watupro-question-id-445434'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>27. <\/span>You are tasked with optimizing an Intel Xeon scalable processor-based server running a TensorFlow model with multiple NVIDIA GPUs. <br \/>\r<br>You observe that the CPU utilization is low, but the GPU utilization is also not optimal. The profiler shows significant time spent in \u2018tf.data\u2019 operations. <br \/>\r<br>Which of the following actions would MOST likely improve performance?<\/div><input type='hidden' name='question_id[]' id='qID_27' value='445434' \/><input type='hidden' id='answerType445434' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445434[]' id='answer-id-1723450' class='answer   answerof-445434 ' value='1723450'   \/><label for='answer-id-1723450' id='answer-label-1723450' class=' answer'><span>Increase the number of threads used for CPU-bound operations in TensorFlow using \u2018tf.config.threading.set_intra_op_parallelism_threads()\u2019.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445434[]' id='answer-id-1723451' class='answer   answerof-445434 ' value='1723451'   \/><label for='answer-id-1723451' id='answer-label-1723451' class=' answer'><span>Enable XLA (Accelerated Linear Algebra) compilation in TensorFlow.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445434[]' id='answer-id-1723452' class='answer   answerof-445434 ' value='1723452'   \/><label for='answer-id-1723452' id='answer-label-1723452' class=' answer'><span>Use \u2018tf.data.AUTOTIJNE to allow TensorFlow to dynamically optimize the data pipeline.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445434[]' id='answer-id-1723453' class='answer   answerof-445434 ' value='1723453'   \/><label for='answer-id-1723453' id='answer-label-1723453' class=' answer'><span>Reduce the global batch size to improve memory utilization.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445434[]' id='answer-id-1723454' class='answer   answerof-445434 ' value='1723454'   \/><label for='answer-id-1723454' id='answer-label-1723454' class=' answer'><span>Upgrade the server\u2019s network adapter to a faster interface, such as 100Gb<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-28' style=';'><div id='questionWrap-28'  class='   watupro-question-id-445435'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>28. <\/span>During NVLink Switch configuration, you encounter issues where certain GPUs are not being recognized by the system. <br \/>\r<br>Which of the following troubleshooting steps are most likely to resolve this problem?<\/div><input type='hidden' name='question_id[]' id='qID_28' value='445435' \/><input type='hidden' id='answerType445435' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445435[]' id='answer-id-1723455' class='answer   answerof-445435 ' value='1723455'   \/><label for='answer-id-1723455' id='answer-label-1723455' class=' answer'><span>Verify that all NVLink cables are securely connected and properly seated.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445435[]' id='answer-id-1723456' class='answer   answerof-445435 ' value='1723456'   \/><label for='answer-id-1723456' id='answer-label-1723456' class=' answer'><span>Check the system BIOS settings to ensure that NVLink is enabled and configured correctly.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445435[]' id='answer-id-1723457' class='answer   answerof-445435 ' value='1723457'   \/><label for='answer-id-1723457' id='answer-label-1723457' class=' answer'><span>Ensure that the NVLink Switch firmware is compatible with the installed GPUs.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445435[]' id='answer-id-1723458' class='answer   answerof-445435 ' value='1723458'   \/><label for='answer-id-1723458' id='answer-label-1723458' class=' answer'><span>Reinstall the operating system.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445435[]' id='answer-id-1723459' class='answer   answerof-445435 ' value='1723459'   \/><label for='answer-id-1723459' id='answer-label-1723459' class=' answer'><span>Check the Power supply for enough capacity and stability.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-29' style=';'><div id='questionWrap-29'  class='   watupro-question-id-445436'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>29. <\/span>You are configuring an InfiniBand subnet with multiple switches. You need to ensure that traffic between two specific nodes always takes the shortest path, bypassing a potentially congested link. <br \/>\r<br>Which of the following approaches is MOST effective for achieving this using InfiniBand\u2019s routing capabilities?<\/div><input type='hidden' name='question_id[]' id='qID_29' value='445436' \/><input type='hidden' id='answerType445436' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445436[]' id='answer-id-1723460' class='answer   answerof-445436 ' value='1723460'   \/><label for='answer-id-1723460' id='answer-label-1723460' class=' answer'><span>Rely solely on the Subnet Manager\u2019s (SM) default path computation algorithm (e.g., Min Hop) without any modifications.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445436[]' id='answer-id-1723461' class='answer   answerof-445436 ' value='1723461'   \/><label for='answer-id-1723461' id='answer-label-1723461' class=' answer'><span>Use static routing by manually configuring forwarding tables on each switch along the desired path. This involves specifying DLID-to-Port mappings.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445436[]' id='answer-id-1723462' class='answer   answerof-445436 ' value='1723462'   \/><label for='answer-id-1723462' id='answer-label-1723462' class=' answer'><span>Implement Quality of Service (QOS) to prioritize the traffic between the two nodes, hoping that this will influence the path selection.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445436[]' id='answer-id-1723463' class='answer   answerof-445436 ' value='1723463'   \/><label for='answer-id-1723463' id='answer-label-1723463' class=' answer'><span>Utilize the ibroute command or similar tool to inject a static route between the nodes, forcing traffic to follow a specific path identified by LID and port number.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445436[]' id='answer-id-1723464' class='answer   answerof-445436 ' value='1723464'   \/><label for='answer-id-1723464' id='answer-label-1723464' class=' answer'><span>Decrease the MTIJ size on the potential congested link.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-30' style=';'><div id='questionWrap-30'  class='   watupro-question-id-445437'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>30. <\/span>You have an Intel Xeon Gold server with 2 NVIDIA Tesla VI 00 GPUs. After deploying your A1 application, you observe that one GPU is consistently running at a significantly higher temperature than the other <br \/>\r<br>What could be a plausible reason for this behavior?<\/div><input type='hidden' name='question_id[]' id='qID_30' value='445437' \/><input type='hidden' id='answerType445437' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445437[]' id='answer-id-1723465' class='answer   answerof-445437 ' value='1723465'   \/><label for='answer-id-1723465' id='answer-label-1723465' class=' answer'><span>One GPU is defective and drawing excessive power.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445437[]' id='answer-id-1723466' class='answer   answerof-445437 ' value='1723466'   \/><label for='answer-id-1723466' id='answer-label-1723466' class=' answer'><span>The server\u2019s airflow is inadequate, causing poor cooling for one of the GPUs.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445437[]' id='answer-id-1723467' class='answer   answerof-445437 ' value='1723467'   \/><label for='answer-id-1723467' id='answer-label-1723467' class=' answer'><span>The workload is not evenly distributed between the GPUs, causing one GPU to be more heavily utilized.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445437[]' id='answer-id-1723468' class='answer   answerof-445437 ' value='1723468'   \/><label for='answer-id-1723468' id='answer-label-1723468' class=' answer'><span>One GPU\u2019s driver version is outdated, leading to inefficient power management.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445437[]' id='answer-id-1723469' class='answer   answerof-445437 ' value='1723469'   \/><label for='answer-id-1723469' id='answer-label-1723469' class=' answer'><span>The ambient temperature in the server room is higher on one side of the rack.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-31' style=';'><div id='questionWrap-31'  class='   watupro-question-id-445438'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>31. <\/span>You have a large dataset stored on a network file system (NFS) and are training a deep learning model on an AMD EPYC server with NVIDIA GPUs. Data loading is very slow. <br \/>\r<br>What steps can you take to improve the data loading performance in this scenario? Select all that apply.<\/div><input type='hidden' name='question_id[]' id='qID_31' value='445438' \/><input type='hidden' id='answerType445438' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445438[]' id='answer-id-1723470' class='answer   answerof-445438 ' value='1723470'   \/><label for='answer-id-1723470' id='answer-label-1723470' class=' answer'><span>Increase the number of NFS client threads on the AMD EPYC server.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445438[]' id='answer-id-1723471' class='answer   answerof-445438 ' value='1723471'   \/><label for='answer-id-1723471' id='answer-label-1723471' class=' answer'><span>Use a local SSD or NVMe drive to cache frequently accessed data.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445438[]' id='answer-id-1723472' class='answer   answerof-445438 ' value='1723472'   \/><label for='answer-id-1723472' id='answer-label-1723472' class=' answer'><span>Mount the NFS share with the \u2018nolock\u2019 option.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445438[]' id='answer-id-1723473' class='answer   answerof-445438 ' value='1723473'   \/><label for='answer-id-1723473' id='answer-label-1723473' class=' answer'><span>Switch to a parallel file system like Lustre or BeeGF<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445438[]' id='answer-id-1723474' class='answer   answerof-445438 ' value='1723474'   \/><label for='answer-id-1723474' id='answer-label-1723474' class=' answer'><span>Reduce the batch size to decrease the amount of data loaded per iteration.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-32' style=';'><div id='questionWrap-32'  class='   watupro-question-id-445439'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>32. <\/span>You are configuring a network bridge on a Linux host that will connect multiple physical network interfaces to a virtual machine. You need to ensure that the virtual machine receives an IP address via DHCP. <br \/>\r<br>Which of the following is the correct command sequence to create the bridge interface \u2018br0\u2019, add physical interfaces \u2018eth0\u2019 and \u2018eth1\u2019 to it, and bring up the bridge interface? Assume the required packages are installed. Consider using \u2018ip\u2019 command. <br \/>\r<br>A ) <br \/>\r<br><br><img decoding=\"async\" width=649 height=18 id=\"\u56fe\u7247 32\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/12\/image002-6.jpg\"><br><br \/>\r<br>B ) <br \/>\r<br><br><img decoding=\"async\" width=649 height=9 id=\"\u56fe\u7247 31\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/12\/image003-7.jpg\"><br><br \/>\r<br>C ) <br \/>\r<br><br><img decoding=\"async\" width=649 height=13 id=\"\u56fe\u7247 30\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/12\/image004-6.jpg\"><br><br \/>\r<br>D ) <br \/>\r<br><br><img decoding=\"async\" width=649 height=7 id=\"\u56fe\u7247 29\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/12\/image005-6.jpg\"><br><br \/>\r<br>E ) <br \/>\r<br><br><img decoding=\"async\" width=650 height=12 id=\"\u56fe\u7247 28\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/uploads\/2025\/12\/image006-6.jpg\"><br><\/div><input type='hidden' name='question_id[]' id='qID_32' value='445439' \/><input type='hidden' id='answerType445439' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445439[]' id='answer-id-1723475' class='answer   answerof-445439 ' value='1723475'   \/><label for='answer-id-1723475' id='answer-label-1723475' class=' answer'><span>Option A<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445439[]' id='answer-id-1723476' class='answer   answerof-445439 ' value='1723476'   \/><label for='answer-id-1723476' id='answer-label-1723476' class=' answer'><span>Option B<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445439[]' id='answer-id-1723477' class='answer   answerof-445439 ' value='1723477'   \/><label for='answer-id-1723477' id='answer-label-1723477' class=' answer'><span>Option C<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445439[]' id='answer-id-1723478' class='answer   answerof-445439 ' value='1723478'   \/><label for='answer-id-1723478' id='answer-label-1723478' class=' answer'><span>Option D<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445439[]' id='answer-id-1723479' class='answer   answerof-445439 ' value='1723479'   \/><label for='answer-id-1723479' id='answer-label-1723479' class=' answer'><span>Option E<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-33' style=';'><div id='questionWrap-33'  class='   watupro-question-id-445440'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>33. <\/span>Your A1 inference server utilizes Triton Inference Server and experiences intermittent latency spikes. Profiling reveals that the GPU is frequently stalling due to memory allocation issues. <br \/>\r<br>Which strategy or tool would be least effective in mitigating these memory allocation stalls?<\/div><input type='hidden' name='question_id[]' id='qID_33' value='445440' \/><input type='hidden' id='answerType445440' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445440[]' id='answer-id-1723480' class='answer   answerof-445440 ' value='1723480'   \/><label for='answer-id-1723480' id='answer-label-1723480' class=' answer'><span>Using CIJDA memory pools to pre-allocate memory and reduce allocation overhead during inference requests.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445440[]' id='answer-id-1723481' class='answer   answerof-445440 ' value='1723481'   \/><label for='answer-id-1723481' id='answer-label-1723481' class=' answer'><span>Enabling CUDA graph capture to reduce kernel launch overhead.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445440[]' id='answer-id-1723482' class='answer   answerof-445440 ' value='1723482'   \/><label for='answer-id-1723482' id='answer-label-1723482' class=' answer'><span>Reducing the model\u2019s memory footprint by using quantization or pruning techniques.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445440[]' id='answer-id-1723483' class='answer   answerof-445440 ' value='1723483'   \/><label for='answer-id-1723483' id='answer-label-1723483' class=' answer'><span>Increasing the GPU\u2019s TCC (Tesla Compute Cluster) mode priority.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445440[]' id='answer-id-1723484' class='answer   answerof-445440 ' value='1723484'   \/><label for='answer-id-1723484' id='answer-label-1723484' class=' answer'><span>Optimize the model using TensorR<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-34' style=';'><div id='questionWrap-34'  class='   watupro-question-id-445441'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>34. <\/span>You are monitoring a server with 8 GPUs used for deep learning training. You observe that one of the GPUs reports a significantly lower utilization rate compared to the others, even though the workload is designed to distribute evenly. \u2018nvidia-smi\u2019 reports a persistent &quot;XID 13&quot; error for that GPU. <br \/>\r<br>What is the most likely cause?<\/div><input type='hidden' name='question_id[]' id='qID_34' value='445441' \/><input type='hidden' id='answerType445441' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445441[]' id='answer-id-1723485' class='answer   answerof-445441 ' value='1723485'   \/><label for='answer-id-1723485' id='answer-label-1723485' class=' answer'><span>A driver bug causing incorrect workload distribution.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445441[]' id='answer-id-1723486' class='answer   answerof-445441 ' value='1723486'   \/><label for='answer-id-1723486' id='answer-label-1723486' class=' answer'><span>Insufficient system memory preventing data transfer to that GP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445441[]' id='answer-id-1723487' class='answer   answerof-445441 ' value='1723487'   \/><label for='answer-id-1723487' id='answer-label-1723487' class=' answer'><span>A hardware fault within the GPU, such as a memory error or core failure.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445441[]' id='answer-id-1723488' class='answer   answerof-445441 ' value='1723488'   \/><label for='answer-id-1723488' id='answer-label-1723488' class=' answer'><span>An incorrect CUDA version installed.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445441[]' id='answer-id-1723489' class='answer   answerof-445441 ' value='1723489'   \/><label for='answer-id-1723489' id='answer-label-1723489' class=' answer'><span>The GPU\u2019s compute mode is set to \u2018Exclusive Process\u2019.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-35' style=';'><div id='questionWrap-35'  class='   watupro-question-id-445442'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>35. <\/span>A user reports that their GPU-accelerated application is crashing with a CUDA error related to \u2018out of memory\u2019. You have confirmed that the GPU has sufficient physical memory. <br \/>\r<br>What are the likely causes and troubleshooting steps?<\/div><input type='hidden' name='question_id[]' id='qID_35' value='445442' \/><input type='hidden' id='answerType445442' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445442[]' id='answer-id-1723490' class='answer   answerof-445442 ' value='1723490'   \/><label for='answer-id-1723490' id='answer-label-1723490' class=' answer'><span>The application is leaking GPU memory. Use a memory profiling tool like \u2018cuda-memcheck\u2019 to identify the source of the leak.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445442[]' id='answer-id-1723491' class='answer   answerof-445442 ' value='1723491'   \/><label for='answer-id-1723491' id='answer-label-1723491' class=' answer'><span>The application is requesting a larger block of memory than is available in a single allocation. Try breaking the allocation into smaller chunks or using managed memory.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445442[]' id='answer-id-1723492' class='answer   answerof-445442 ' value='1723492'   \/><label for='answer-id-1723492' id='answer-label-1723492' class=' answer'><span>The CUDA driver version is incompatible with the CUDA runtime version used by the application. Update the CUDA driver to match the runtime version.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445442[]' id='answer-id-1723493' class='answer   answerof-445442 ' value='1723493'   \/><label for='answer-id-1723493' id='answer-label-1723493' class=' answer'><span>The process has exceeded the maximum number of GPU contexts allowed. Reduce the number of concurrent CUDA applications running on the GP<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445442[]' id='answer-id-1723494' class='answer   answerof-445442 ' value='1723494'   \/><label for='answer-id-1723494' id='answer-label-1723494' class=' answer'><span>The system\u2019s virtual memory is exhausted. Increase the swap space.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-36' style=';'><div id='questionWrap-36'  class='   watupro-question-id-445443'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>36. <\/span>You are tasked with installing a DGX A100 server. After racking and connecting power and network cables, you power it on, but the BMC (Baseboard Management Controller) is not accessible via the network. You have verified the network cable is connected and the switch port is active. <br \/>\r<br>What are the MOST likely causes and initial troubleshooting steps you should take?<\/div><input type='hidden' name='question_id[]' id='qID_36' value='445443' \/><input type='hidden' id='answerType445443' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445443[]' id='answer-id-1723495' class='answer   answerof-445443 ' value='1723495'   \/><label for='answer-id-1723495' id='answer-label-1723495' class=' answer'><span>The BMC IP address is not configured or is on a different subnet. Check the BMC\u2019s network configuration using the DGX\u2019s front panel or via serial console. Verify DHCP is enabled and functioning or manually configure a static IP address.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445443[]' id='answer-id-1723496' class='answer   answerof-445443 ' value='1723496'   \/><label for='answer-id-1723496' id='answer-label-1723496' class=' answer'><span>The BMC firmware is corrupted and needs to be reflashed using a USB drive. Check the DGX support site for the latest BMC firmware.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445443[]' id='answer-id-1723497' class='answer   answerof-445443 ' value='1723497'   \/><label for='answer-id-1723497' id='answer-label-1723497' class=' answer'><span>The BMC is not powered on because the main power supply is faulty. Verify the power supply LEDs are lit and providing power to the system.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445443[]' id='answer-id-1723498' class='answer   answerof-445443 ' value='1723498'   \/><label for='answer-id-1723498' id='answer-label-1723498' class=' answer'><span>The network switch port is not configured for the correct VLA<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445443[]' id='answer-id-1723499' class='answer   answerof-445443 ' value='1723499'   \/><label for='answer-id-1723499' id='answer-label-1723499' class=' answer'><span>Verify the switch port configuration to ensure it is on the same VLAN as the BM<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445443[]' id='answer-id-1723500' class='answer   answerof-445443 ' value='1723500'   \/><label for='answer-id-1723500' id='answer-label-1723500' class=' answer'><span>The BMC is faulty and needs to be replaced. Contact NVIDIA support for RM<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-37' style=';'><div id='questionWrap-37'  class='   watupro-question-id-445444'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>37. <\/span>You are replacing a faulty NVIDIA Tesla V 100 GPU in a server. After physically installing the new GPU, the system fails to recognize it. You\u2019ve verified the power connections and seating of the card. <br \/>\r<br>Which of the following steps should you take next to troubleshoot the issue?<\/div><input type='hidden' name='question_id[]' id='qID_37' value='445444' \/><input type='hidden' id='answerType445444' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445444[]' id='answer-id-1723501' class='answer   answerof-445444 ' value='1723501'   \/><label for='answer-id-1723501' id='answer-label-1723501' class=' answer'><span>Immediately RMA the new GPU as it is likely defective.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445444[]' id='answer-id-1723502' class='answer   answerof-445444 ' value='1723502'   \/><label for='answer-id-1723502' id='answer-label-1723502' class=' answer'><span>Update the system BIOS and BMC firmware to the latest versions.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445444[]' id='answer-id-1723503' class='answer   answerof-445444 ' value='1723503'   \/><label for='answer-id-1723503' id='answer-label-1723503' class=' answer'><span>Reinstall the operating system to ensure proper driver installation.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445444[]' id='answer-id-1723504' class='answer   answerof-445444 ' value='1723504'   \/><label for='answer-id-1723504' id='answer-label-1723504' class=' answer'><span>Check if the new GPU requires a different driver version than the currently installed one and update if needed.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445444[]' id='answer-id-1723505' class='answer   answerof-445444 ' value='1723505'   \/><label for='answer-id-1723505' id='answer-label-1723505' class=' answer'><span>Disable and re-enable the GPU slot in the system BIO<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-38' style=';'><div id='questionWrap-38'  class='   watupro-question-id-445445'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>38. <\/span>You are running a distributed training job on a multi-GPU server. After several hours, the job fails with a NCCL (NVIDIA Collective Communications Library) error. The error message indicates a failure in inter-GPU communication. \u2018nvidia-smi\u2019 shows all GPUs are healthy. <br \/>\r<br>What is the MOST probable cause of this issue?<\/div><input type='hidden' name='question_id[]' id='qID_38' value='445445' \/><input type='hidden' id='answerType445445' value='checkbox'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445445[]' id='answer-id-1723506' class='answer   answerof-445445 ' value='1723506'   \/><label for='answer-id-1723506' id='answer-label-1723506' class=' answer'><span>A bug in the NCCL library itself; downgrade to a previous version of NCC<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445445[]' id='answer-id-1723507' class='answer   answerof-445445 ' value='1723507'   \/><label for='answer-id-1723507' id='answer-label-1723507' class=' answer'><span>Incorrect NCCL configuration, such as an invalid network interface or incorrect device affinity settings.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445445[]' id='answer-id-1723508' class='answer   answerof-445445 ' value='1723508'   \/><label for='answer-id-1723508' id='answer-label-1723508' class=' answer'><span>Insufficient inter-GPU bandwidth; reduce the batch size to decrease communication overhead.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445445[]' id='answer-id-1723509' class='answer   answerof-445445 ' value='1723509'   \/><label for='answer-id-1723509' id='answer-label-1723509' class=' answer'><span>A faulty network cable connecting the server to the rest of the cluster.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='checkbox' name='answer-445445[]' id='answer-id-1723510' class='answer   answerof-445445 ' value='1723510'   \/><label for='answer-id-1723510' id='answer-label-1723510' class=' answer'><span>Driver incompatibility issue between NCCL and the installed NVIDIA driver version.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-39' style=';'><div id='questionWrap-39'  class='   watupro-question-id-445446'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>39. <\/span>You are designing a storage solution for a new AI inference cluster that requires extremely low latency for model serving. <br \/>\r<br>Which storage technology and configuration would be MOST suitable to meet this stringent latency requirement?<\/div><input type='hidden' name='question_id[]' id='qID_39' value='445446' \/><input type='hidden' id='answerType445446' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445446[]' id='answer-id-1723511' class='answer   answerof-445446 ' value='1723511'   \/><label for='answer-id-1723511' id='answer-label-1723511' class=' answer'><span>A distributed file system deployed on spinning HDDs with a large read-ahead cache.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445446[]' id='answer-id-1723512' class='answer   answerof-445446 ' value='1723512'   \/><label for='answer-id-1723512' id='answer-label-1723512' class=' answer'><span>NVMe-oF (NVMe over Fabrics) using RDMA over Converged Ethernet (RoCE) connected to a cluster of NVMe drives.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445446[]' id='answer-id-1723513' class='answer   answerof-445446 ' value='1723513'   \/><label for='answer-id-1723513' id='answer-label-1723513' class=' answer'><span>A software-defined storage (SDS) solution running on commodity hardware with SATA SSDs.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445446[]' id='answer-id-1723514' class='answer   answerof-445446 ' value='1723514'   \/><label for='answer-id-1723514' id='answer-label-1723514' class=' answer'><span>Amazon S3 object storage accessed over a high-bandwidth internet connection.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445446[]' id='answer-id-1723515' class='answer   answerof-445446 ' value='1723515'   \/><label for='answer-id-1723515' id='answer-label-1723515' class=' answer'><span>A traditional Fibre Channel SAN with a dedicated storage array.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div class='watu-question ' id='question-40' style=';'><div id='questionWrap-40'  class='   watupro-question-id-445447'>\n\t\t\t<div class='question-content'><div><span class='watupro_num'>40. <\/span>You\u2019re designing a new InfiniBand network for a distributed deep learning workload. The workload consists of a mix of large-message all- to-all communication and small-message parameter synchronization. <br \/>\r<br>Considering the different traffic patterns, what routing strategy would MOST effectively minimize latency and maximize bandwidth utilization across the fabric?<\/div><input type='hidden' name='question_id[]' id='qID_40' value='445447' \/><input type='hidden' id='answerType445447' value='radio'><!-- end question-content--><\/div><div class='question-choices watupro-choices-columns '><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445447[]' id='answer-id-1723516' class='answer   answerof-445447 ' value='1723516'   \/><label for='answer-id-1723516' id='answer-label-1723516' class=' answer'><span>Rely solely on the default Subnet Manager (SM) with a Min Hop path selection algorithm.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445447[]' id='answer-id-1723517' class='answer   answerof-445447 ' value='1723517'   \/><label for='answer-id-1723517' id='answer-label-1723517' class=' answer'><span>Implement a static routing scheme with manually configured forwarding tables on each switch.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445447[]' id='answer-id-1723518' class='answer   answerof-445447 ' value='1723518'   \/><label for='answer-id-1723518' id='answer-label-1723518' class=' answer'><span>Utilize a combination of Adaptive Routing (AR) to handle dynamic traffic patterns and Quality of Service (QOS) to prioritize small-message parameter synchronization.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445447[]' id='answer-id-1723519' class='answer   answerof-445447 ' value='1723519'   \/><label for='answer-id-1723519' id='answer-label-1723519' class=' answer'><span>Implement a purely deterministic routing scheme, disabling all adaptive routing features.<\/span><\/label><\/div><div class='watupro-question-choice  ' dir='auto' ><input type='radio' name='answer-445447[]' id='answer-id-1723520' class='answer   answerof-445447 ' value='1723520'   \/><label for='answer-id-1723520' id='answer-label-1723520' class=' answer'><span>Disable multicast.<\/span><\/label><\/div><!-- end question-choices--><\/div><!-- end questionWrap--><\/div><\/div><div style='display:none' id='question-41'>\n\t<div class='question-content'>\n\t\t<img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.dumpsbase.com\/freedumps\/wp-content\/plugins\/watupro\/img\/loading.gif\" width=\"16\" height=\"16\" alt=\"Loading...\" title=\"Loading...\" \/>&nbsp;Loading...\t<\/div>\n<\/div>\n\n<br \/>\n\t\n\t\t\t<div class=\"watupro_buttons flex \" id=\"watuPROButtons11331\" >\n\t\t  <div id=\"prev-question\" style=\"display:none;\"><input type=\"button\" value=\"&lt; 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   \t \n<\/script>\n<p>&nbsp;<\/p>\n<h3>We also have the <a href=\"https:\/\/www.dumpsbase.com\/freedumps\/ncp-aii-exam-dumps-v9-03-are-online-for-your-ncp-ai-infrastructure-exam-preparation-continue-to-check-the-ncp-aii-free-dumps-part-3-q81-q120-today.html\"><span style=\"background-color: #ffff99;\"><em>NCP-AII free dumps (Part 3, Q81-Q120) of V9.03<\/em><\/span><\/a> here for reading.<\/h3>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Now, you can pass your NVIDIA Certified Professional AI Infrastructure certification exam with the most updated NCP-AII dumps (V9.03) from DumpsBase. All the practice questions in V9.03 are created and evaluated by certified professionals. This means every question has been carefully inspected for accuracy and relevance. 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