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1. Consider an AMD EPYC-based server with 8 NVIDIAAIOO GPUs connected via PCle Gen4. You’re running a distributed training job using Horovod. You’ve noticed that communication between GPUs is a bottleneck.

Which of the following NCCL configuration options would be MOST beneficial in this scenario? (Assume all options are syntactically correct for NCCL).

NCCL SOCKET IF-NAME=eth0

NCCL IB DISABLE-1

NCCL P2P DISABLE-0

NCCL IB HCA=mlx5 0

NCCL NET PLUGIN=none

2. In an InfiniBand fabric, what is the primary role of the Subnet Manager (SM) with respect to routing?

To forward packets based on destination IP addresses, similar to a traditional IP router.

To discover the network topology, calculate routing paths, and program the forwarding tables (LID tables) in the switches.

To monitor the network for congestion and dynamically adjust packet priorities using Quality of Service (QOS) mechanisms.

To provide a command-line interface for users to manually configure routing tables on each InfiniBand switch.

To act as a firewall, blocking unauthorized traffic based on pre-defined rules.

3. You have a large dataset stored on a BeeGFS file system. The training job is single node and uses data augmentation to generate more data on the fly. The data augmentation process is CPU-bound, but you notice that the GPU is underutilized due to the training data not being fed to the GPU fast enough.

How can you reduce the load on the CPU and improve the overall training throughput?

Move the training data to a local NVMe drive on the training node.

Increase the number of BeeGFS metadata servers (MDSs) to improve metadata performance.

Implement asynchronous 1/0 in the data loading pipeline using a library like NVIDIA DALI to offload data processing tasks from the CPU to the GP

Decrease the batch size of the training job to reduce the amount of data being processed at each iteration.

Enable data compression on the BeeGFS file system to reduce the amount of data being transferred over the network.

4. A server with eight NVIDIAAIOO GPUs experiences frequent CUDA errors during large model training. ‘nvidia-smi’ reports seemingly normal temperatures for all GPUs. However, upon closer inspection using IPMI, the inlet temperature for GPUs 3 and 4 is significantly higher than others.

What is the MOST likely cause and the immediate action to take?

A driver issue is causing incorrect temperature reporting; reinstall the NVIDIA driver.

The temperature sensors on GPUs 3 and 4 are faulty; replace the GPUs immediately.

There is a localized airflow problem affecting GPUs 3 and 4; check fan speeds and airflow obstructions.

The power supply is failing to provide sufficient power to GPUs 3 and 4; replace the power supply.

A software bug in the CUDA toolkit is causing the errors; downgrade to an earlier version.

5. You’re optimizing an Intel Xeon server with 4 NVIDIAAIOO GPUs for a computer vision application that uses CODA. You notice that the GPU utilization is fluctuating significantly, and performance is inconsistent. Using ‘nvprof, you identify that there are frequent stalls in the CUDA kernels due to thread divergence.

What are possible causes and solutions?

The input data is not properly aligned in memory. Ensure that data is aligned to 128-byte boundaries using aligned memory allocation techniques.

The CUDA code contains conditional branches that lead to different execution paths for different threads within the same warp. Rewrite the CUDA code to minimize branching and favor uniform execution paths within warps.

The GPUs are overheating, causing thermal throttling. Improve the server’s cooling.

The CUDA compiler is generating suboptimal code. Try using different compiler optimization flags (e.g., ‘-O3’ or ‘-ftz=true’).

The CUDA driver version is incompatible with the CUDA toolkit version. Update the CUDA driver to a compatible version.

6. 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

What could be a plausible reason for this behavior?

One GPU is defective and drawing excessive power.

The server’s airflow is inadequate, causing poor cooling for one of the GPUs.

The workload is not evenly distributed between the GPUs, causing one GPU to be more heavily utilized.

One GPU’s driver version is outdated, leading to inefficient power management.

The ambient temperature in the server room is higher on one side of the rack.

7. 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.

How can you optimize metadata handling in BeeGFS to potentially improve performance?

Increase the number of storage targets (OSTs) to distribute the data across more devices.

Implement data striping across multiple OSTs.

Increase the number of metadata servers (MDSs) and distribute the metadata load across them.

Enable client-side caching of metadata on the training nodes.

Configure BeeGFS to use a different network protocol with lower overhead.

8. 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.

What is the typical MTU (Maximum Transmission Unit) size you would set on the network interfaces and switches, and why?

1500 bytes, as it’s the default and compatible with most networks.

9000 bytes, also known as Jumbo Frames, reduces overhead and improves throughput for large data transfers.

65535 bytes, the theoretical maximum MTU size, for maximum performance.

576 bytes, the minimum MTU size required by IPv4.

Any MTU size between 1500 and 9000 bytes; the specific value doesn’t matter.

9. You are deploying a new A1 cluster using RoCEv2 over a lossless Ethernet fabric.

Which of the following QOS (Quality of Service) mechanisms is critical for ensuring reliable RDMA communication?

DSCP (Differentiated Services Code Point) marking

ECN (Explicit Congestion Notification)

PFC (Priority Flow control)

ACL (Access Control List)

Rate Limiting

10. You need to remotely monitor the GPU temperature and utilization of a server without installing any additional software on the server itself.

Assuming you have network access to the server’s BMC (Baseboard Management Controller), which protocol and standard data format would BEST facilitate this?

SNMP (Simple Network Management Protocol) with MIB (Management Information Base)

HTTP with JSON

SSH with plain text output from ‘nvidia-smi’

IPMI (Intelligent Platform Management Interface) with SDR (Sensor Data Records)

Syslog with CSV (Comma-separated Values)

11. You’re monitoring the storage I/O for an AI training workload and observe high disk utilization but relatively low CPU utilization.

Which of the following actions is LEAST likely to improve the performance of the training job?

Switching from HDDs to NVMe SSDs.

Implementing data prefetching to load data into memory before it’s needed.

Increasing the batch size of the training job.

Adding more RAM to the system.

Reducing the number of parallel data loading threads.

12. 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.

Which strategy or tool would be least effective in mitigating these memory allocation stalls?

Using CIJDA memory pools to pre-allocate memory and reduce allocation overhead during inference requests.

Enabling CUDA graph capture to reduce kernel launch overhead.

Reducing the model’s memory footprint by using quantization or pruning techniques.

Increasing the GPU’s TCC (Tesla Compute Cluster) mode priority.

Optimize the model using TensorR

13. You are deploying a new NVLink Switch based cluster. The GPUs are installed in different servers, but need to be configured to utilize

NVLink interconnect.

Which of the following should be performed during the installation phase to confirm correct configuration?

Run NCCL tests to verify the GPU-to-GPU bandwidth and latency between servers.

Verify that GPUDirect RDMA is enabled and functioning correctly.

Check that the ‘nvidia-sm’ command shows the correct NVLink topology.

Run standard TCP/IP network bandwidth tests to check inter-server communication.

All the GPU’s are in the same IP subnet

14. You are configuring a Mellanox InfiniBand network for a DGXAIOO cluster.

What is the RECOMMENDED subnet manager for a large, high-performance A1 training environment, and why?

OpenSM, because it’s the default and easiest to configure.

UFM (Unified Fabric Manager), because it provides advanced management, monitoring, and optimization capabilities.

IBA management tools that ship with the OS (e.g., ‘ibnetdiscover’).

Any subnet manager; the performance difference is negligible.

A custom-built subnet manager using the InfiniBand verbs AP

15. Consider a scenario where you are using NCCL (NVIDIA Collective Communications Library) for multi-GPU training across multiple servers connected via NVLink switches.

Which NCCL environment variable would you use to specify the network interface to be used for communication?

NCCL PORT

NCCL SOCKET IFNAME

NCCL NET INTERFACE

NCCL 1B HCA

NCCL COMM ID

16. Which of the following is the MOST important reason for using a dedicated storage network (e.g., InfiniBand or RoCE) for AI/ML workloads compared to using the existing Ethernet network?

Improved security due to network isolation.

Lower latency and higher bandwidth for data transfer.

Simplified network management and configuration.

Reduced cost compared to upgrading the existing Ethernet infrastructure.

Automatic Quality of Service (QOS) prioritization for AI/ML traffic.

17. Which of the following statements regarding VXLAN (Virtual Extensible LAN) is MOST accurate in the context of data center networking for AI/ML workloads?

VXLAN provides Layer 2 connectivity across Layer 3 networks, enabling virtual machine mobility.

VXLAN primarily improves network security by encrypting all traffic.

VXLAN is only suitable for small-scale networks due to its limited scalability.

VXLAN reduces network overhead compared to traditional VLANs.

VXLAN requires specialized hardware and cannot be implemented in software.

18. You are tasked with diagnosing performance issues on a GPU server running a large-scale HPC simulation. The simulation utilizes multiple GPUs and InfiniBand for inter-GPU communication. You suspect that RDMA (Remote Direct Memory Access) is not functioning correctly.

How would you comprehensively test and verify the proper operation of RDMA between the GPUs?

Use ‘ping’ to verify basic network connectivity between the server’s InfiniBand interfaces.

Employ and from the ‘perftest’ suite to measure RDMA bandwidth and latency between GPUs.

Run ‘nvidia-smi topo -m’ to check the GPU interconnect topology and verify that NVLink or PCle is being used for communication.

Utilize NCCL’s internal diagnostic tools to verify proper inter-GPU communication within the simulation.

Monitor CPU utilization during the simulation; high CPU usage suggests that RDMA is not offloading communication effectively.

19. You are troubleshooting a network performance issue in your NCP-AII environment.

After running ‘ibstat’ on a host, you see the following output for one of the InfiniBand ports:





What does the ‘LMC: 0’ indicate, and what are the implications for network performance?

LMC: 0 indicates that the link is down and not functioning correctly.

LMC: 0 indicates that Link Aggregation (LAG) is not enabled on this port, meaning only a single link is being used for communication.

LMC: 0 indicates the port is operating at the lowest possible speed.

LMC: 0 indicates that the Subnet Manager is not running correctly.

LMC: 0 is the default and expected value; it has no impact on performance.

20. You’re 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.

Considering the different traffic patterns, what routing strategy would MOST effectively minimize latency and maximize bandwidth utilization across the fabric?

Rely solely on the default Subnet Manager (SM) with a Min Hop path selection algorithm.

Implement a static routing scheme with manually configured forwarding tables on each switch.

Utilize a combination of Adaptive Routing (AR) to handle dynamic traffic patterns and Quality of Service (QOS) to prioritize small-message parameter synchronization.

Implement a purely deterministic routing scheme, disabling all adaptive routing features.

Disable multicast.

21. An AI server with 8 GPUs is experiencing random system crashes under heavy load. The system logs indicate potential memory errors, but standard memory tests (memtest86+) pass without any failures. The GPUs are passively cooled.

What are the THREE most likely root causes of these crashes?

Incompatible NVIDIA driver version with the installed Linux kernel.

GPIJ memory errors that are not detectable by standard CPU-based memory tests.

Insufficient airflow within the server, leading to overheating of the GPUs and VRMs.

A faulty power supply unit (PSU) that is unable to provide stable power under peak load.

Network congestion causing intermittent data corruption during distributed training.

22. You’re optimizing an Intel Xeon server with 4 NVIDIA GPUs for inference serving using Triton Inference Server. You’ve deployed multiple models concurrently. You observe that the overall throughput is lower than expected, and the GPU utilization is not consistently high.

What are potential bottlenecks and optimization strategies? (Select all that apply)

Model loading and unloading overhead. Use model ensemble or dynamic batching to reduce frequency.

Insufficient CPU cores to handle the model loading and preprocessing requests. Increase the number of Triton instance groups for CPU-based models.

The models are memory-bound. Reduce the model precision (e.g., FP32 to FP16 or INT8).

The GPUs are underutilized due to small batch sizes. Implement dynamic batching to increase batch sizes.

Insufficient PCle bandwidth between CPU and GPIJs. Reconfigure PCle lanes to improve bandwidth allocation to each GPI

23. You’re working with a large dataset of microscopy images stored as individual TIFF files. The images are accessed randomly during a training job. The current storage solution is a single HDD. You’re tasked with improving data loading performance.

Which of the following storage optimizations would provide the GREATEST performance improvement in this specific scenario?

Implementing data deduplication on the storage volume.

Migrating the data to a large, sequential HD

Replacing the HDD with a RAID 5 array of HDDs.

Replacing the HDD with a single NVMe SS

Compressing the TIFF files using a lossless compression algorithm.

24. 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.

Which of the following is the correct command sequence to create the bridge interface ‘br0’, add physical interfaces ‘eth0’ and ‘eth1’ to it, and bring up the bridge interface? Assume the required packages are installed. Consider using ‘ip’ command.

A )





B )





C )





D )





E )

Option A

Option B

Option C

Option D

Option E

25. You are managing a cluster of GPU servers for deep learning. You observe that one server consistently exhibits high GPU temperature during training, causing thermal throttling and reduced performance. You’ve already ensured adequate airflow.

Which of the following actions would be MOST effective in addressing this issue?

Reduce the ambient temperature of the data center.

Lower the GPU power limit using ‘nvidia-smi ―power-limit*.

Update the NVIDIA drivers to the latest version.

Re-seat the GPU in its PCle slot to ensure proper contact and heat dissipation.

Increase the fan speed of the GPU cooler using ‘nvidia-smi --fan’.

26. Your deep learning training job that utilizes NCCL (NVIDIA Collective Communications Library) for multi-GPU communication is failing with "NCCL internal error, unhandled system error" after a recent CUDA update. The error occurs during the ‘all reduce’ operation.

What is the most likely root cause and how would you address it?

Incompatible NCCL version with the new CUDA version. Update NCCL to a version compatible with the installed CUDA version.

Insufficient shared memory allocated to the CUDA context. Increase the shared memory limit using ‘cudaDeviceSetLimit(cudaLimitSharedMemory, new_limity.

Firewall rules blocking inter-GPU communication. Configure the firewall to allow communication on the NCCL-defined ports (typically 8000-8010).

Faulty network cables used for inter-node communication (if the training job spans multiple servers). Replace the network cables with certified high-speed cables.

GPU Direct RDMA is not properly configured. Check ‘dmesg’ for errors and ensure RDMA is enabled.

27. You observe high latency and low bandwidth between two GPUs connected via an NVLink switch. You suspect a problem with the NVLink link itself.

Which of the following methods would be the most effective in diagnosing the physical NVLink link health?

Using ‘iperf3’ to measure network throughput between the servers.

Running a CUDA-aware memory bandwidth test specifically designed for NVLink.

Examining system logs for NVLink-related error messages.

Using ‘ping’ to check network connectivity between the servers.

Physically inspecting the NVLink cables for damage.

28. 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.

What steps can you take to improve the data loading performance in this scenario? Select all that apply.

Increase the number of NFS client threads on the AMD EPYC server.

Use a local SSD or NVMe drive to cache frequently accessed data.

Mount the NFS share with the ‘nolock’ option.

Switch to a parallel file system like Lustre or BeeGF

Reduce the batch size to decrease the amount of data loaded per iteration.

29. You are configuring network fabric ports for NVIDIA GPUs in a server. The GPUs are connected to the network via PCIe.

What is the primary factor that determines the maximum achievable bandwidth between the GPUs and the network?

The clock speed of the CP

The amount of system RA

The PCIe generation and number of lanes connecting the GPUs to the network adapter (e.g., PCIe 4.0 x16).

The speed of the system’s hard drives or SSDs.

The color of the Ethernet cables.

30. An InfiniBand fabric is experiencing intermittent packet loss between two high-performance compute nodes. You suspect a faulty cable or connector.

Besides physically inspecting the cables, what software-based tools or techniques can you employ to diagnose potential link errors contributing to this packet loss?

Use ‘ibdiagnet’ to perform a comprehensive fabric analysis, including link integrity checks and error detection.

Monitor the port counters on the InfiniBand switches connected to the compute nodes. Look for excessive CRC errors, symbol errors, or other link-related error counts.

Run ‘ipeff or ‘ibperf between the two compute nodes and analyze the reported packet loss rate. Correlate this with the error counters on the switches.

All of the above

Disable port mirroring.

31. You notice that one of the fans in your GPU server is running at a significantly higher RPM than the others, even under minimal load. ipmitool sensor’ output shows a normal temperature for that GPU.

What could be the potential causes?

The fan’s PWM control signal is malfunctioning, causing it to run at full speed.

The fan bearing is wearing out, causing increased friction and requiring higher RPM to maintain airflow.

The fan is attempting to compensate for restricted airflow due to dust buildup.

The server’s BMC (Baseboard Management Controller) has a faulty temperature sensor reading, causing it to overcompensate.

A network connectivity issue is causing higher CPU utilization, leading to increased system-wide heat.

32. You are tasked with ensuring optimal power efficiency for a GPU server running machine learning workloads. You want to dynamically adjust the GPU’s power consumption based on its utilization.

Which of the following methods is the MOST suitable for achieving this, assuming the server’s BIOS and the NVIDIA drivers support it?

Manually set the GPU’s power limit using ‘nvidia-smi -pl and create a script to monitor utilization and adjust the power limit periodically.

Configure the server’s BIOS/UEFI to use a power-saving profile, which will automatically reduce the GPU’s power consumption when idle.

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.

Use NVIDIA’s Data Center GPU Manager (DCGM) to monitor GPU utilization and dynamically adjust the power limit based on a predefined policy.

Disable ECC (Error Correcting Code) on the GPU to reduce power consumption.

33. You are tasked with troubleshooting a performance bottleneck in a multi-node, multi-GPU deep learning training job utilizing Horovod.

The training loss is decreasing, but the overall training time is significantly longer than expected.

Which of the following monitoring approaches would provide the most insight into the cause of the bottleneck?

Using ‘nvidia-smi’ on each node to monitor GPU utilization and memory usage.

Enabling Horovod’s timeline and profiling features to visualize the communication patterns and identify synchronization bottlenecks.

Monitoring network bandwidth utilization on each node using ‘iftop’ or ‘iperf3’

Analyzing the training loss curve to identify potential issues with the model architecture or hyperparameters.

Using Shtop’ to monitor CPIJ utilization on each node.

34. 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’ve verified the power connections and seating of the card.

Which of the following steps should you take next to troubleshoot the issue?

Immediately RMA the new GPU as it is likely defective.

Update the system BIOS and BMC firmware to the latest versions.

Reinstall the operating system to ensure proper driver installation.

Check if the new GPU requires a different driver version than the currently installed one and update if needed.

Disable and re-enable the GPU slot in the system BIO

35. You’re troubleshooting a DGX-I server exhibiting performance degradation during a large-scale distributed training job. ‘nvidia-smü shows all GPUs are detected, but one GPU consistently reports significantly lower utilization than the others. Attempts to reschedule orkloads to that GPU frequently result in CUDA errors.

Which of the following is the MOST likely cause and the BEST initial roubleshooting step?

A driver issue affecting only one GPU; reinstall NVIDIA drivers completely.

A software bug in the training script utilizing that specific GPU’s resources inefficiently; debug the training script.

A hardware fault with the GPU, potentially thermal throttling or memory issues; run ‘nvidia-smi -i -q’ to check temperatures, power limits, and error counts.

Insufficient cooling in the server rack; verify adequate airflow and cooling capacity for the rack.

Power supply unit (PSU) overload, causing reduced power delivery to that GPU; monitor PSU load and check PSU specifications.

36. 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.

What are the MOST likely causes and initial troubleshooting steps you should take?

The BMC IP address is not configured or is on a different subnet. Check the BMC’s network configuration using the DGX’s front panel or via serial console. Verify DHCP is enabled and functioning or manually configure a static IP address.

The BMC firmware is corrupted and needs to be reflashed using a USB drive. Check the DGX support site for the latest BMC firmware.

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.

The network switch port is not configured for the correct VLA

Verify the switch port configuration to ensure it is on the same VLAN as the BM

The BMC is faulty and needs to be replaced. Contact NVIDIA support for RM

37. A user reports that their GPU-accelerated application is crashing with a CUDA error related to ‘out of memory’. You have confirmed that the GPU has sufficient physical memory.

What are the likely causes and troubleshooting steps?

The application is leaking GPU memory. Use a memory profiling tool like ‘cuda-memcheck’ to identify the source of the leak.

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.

The CUDA driver version is incompatible with the CUDA runtime version used by the application. Update the CUDA driver to match the runtime version.

The process has exceeded the maximum number of GPU contexts allowed. Reduce the number of concurrent CUDA applications running on the GP

The system’s virtual memory is exhausted. Increase the swap space.

38. 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.

Which of the following methods would be MOST effective for verifying and addressing this issue?

Use ‘ping’ or ‘traceroute’ to analyze the paths taken by packets between the affected nodes. If they always take the same path, ECMP is likely not working correctly.

Use switch telemetry tools (e.g., NVIDIA What’s Up Gold, Mellanox NEO, or similar) to monitor link utilization across all available paths between the nodes. Look for significant imbalances in traffic volume.

Restart the switches to force the ECMP hashing algorithm to recalculate paths.

Disable ECMP entirely and rely solely on static routing.

Reduce the TCP window size.

39. You are tasked with optimizing storage performance for a deep learning training job on an NVIDIA DGX server. The training data consists of millions of small image files.

Which of the following storage optimization techniques would be MOST effective in reducing I/O bottlenecks?

Implementing RAID 0 across all storage devices.

Using a distributed file system with data striping across multiple storage nodes.

Enabling data compression on the storage volume.

Increasing the block size of the file system to the maximum supported value.

Implementing a tiered storage system with NVMe drives for frequently accessed data and HDDs for less frequently accessed data.

40. Consider the following ‘ibroute’ command used on an InfiniBand host: ‘ibroute add dest Oxla dev ib0’.

What is the MOST likely purpose of this command?

To add a default route for all traffic destined outside the InfiniBand subnet.

To create a static route for traffic destined to LID Ox1a, using the InfiniBand interface ib0.

To configure the MTU size on the ib0 interface to Ox1a bytes.

To disable routing on the ib0 interface.

To configure a static route for traffic destined to IP address Ox1a, using the InfiniBand interface ib0.

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