NVIDIA NCP-ADS Dumps (V8.02) with Real Exam Questions for Your NVIDIA-Certified-Professional Accelerated Data Science Exam Preparation: Start Reading NCP-ADS Free Dumps (Part 1, Q1-Q40)

1. You have deployed a deep learning model for image classification in a production environment, but inference latency is high. You need to optimize the model to reduce response time while maintaining accuracy.

Which NVIDIA technology is best suited for this task?

2. Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?

3. You are working with a dataset consisting of 100 million records stored in a distributed system. The dataset includes numerical and categorical variables, requiring both exploratory data analysis (EDA) and machine learning model training. The processing time using traditional CPU-based methods is too slow.

Which of the following techniques would be the most effective acceleration method to handle this workload efficiently?

4. You are working on a data processing pipeline using NVIDIA GPUs for accelerating computations. You need to monitor the pipeline's performance to identify bottlenecks.

Which of the following tools or techniques can be used to efficiently recognize bottlenecks in such a GPU-accelerated pipeline? (Select two)

5. A retail company is deploying an AI-driven demand forecasting system using NVIDIA GPUs. The team follows the CRISP-DM framework and is currently in the Evaluation phase.

Which approach best leverages NVIDIA technologies to assess model performance effectively?

6. You are working on a large-scale graph analysis problem that involves computing the shortest paths between nodes in a massive social network dataset. You decide to leverage NVIDIA RAPIDS cuGraph for accelerated computation.

Which of the following cuGraph functions should you use?

7. You are working on a structured dataset of around 10GB and need to perform exploratory data analysis (EDA), feature engineering, and filtering operations efficiently using NVIDIA technologies. The dataset fits into a single GPU’s memory.

Which data processing library should you use to achieve the best performance?

8. You are working with a dataset containing hundreds of millions of records, and you need to perform ETL operations such as filtering, joins, and aggregations. Given the dataset size, which NVIDIA-accelerated library should you use to achieve optimal performance?

9. You are working with a large time-series dataset consisting of millions of records and want to efficiently visualize trends over time using NVIDIA technologies. The dataset is stored as a cuDF DataFrame, and you need to generate an interactive line plot with minimal performance overhead.

Which of the following is the best approach to achieve this goal?

10. You are working with a dataset containing billions of records stored in a Parquet file. You need to load this dataset efficiently into an NVIDIA-accelerated RAPIDS environment for feature engineering.

Which of the following is the best approach?

11. You are working with a large dataset containing millions of high-resolution images for a deep learning project. The dataset needs to be processed efficiently on a GPU before training a model.

Which NVIDIA technology is best suited for preprocessing, augmenting, and efficiently loading the dataset into memory?

12. After profiling a deep learning model using NVIDIA DLProf, you notice that a specific GEMM (General Matrix Multiplication) operation takes significantly longer than expected. The profiler output reveals that tensor cores are underutilized despite having an Ampere-based GPU with Tensor Cores enabled.

Which of the following actions is the MOST appropriate to improve performance?

13. In the context of cloud computing, what are the key benefits of using GPUs for data science tasks? (Select two)

14. You are working with cloud-based GPUs to process a large dataset (terabytes in size) stored in Parquet format. One column represents a unique identifier (e.g., product ID), and it contains only positive integers ranging from 1 to 100,000.

Which of the following data types provides the best balance of memory efficiency and performance?

15. You are setting up a GPU-accelerated data science environment that includes NVIDIA RAPIDS, PyTorch, TensorFlow, and other libraries for machine learning and data processing.

Given that these frameworks have different dependencies and version requirements, what is the best approach to avoid software conflicts while ensuring reproducibility across multiple environments?

16. You have trained a machine learning model using cuML as part of the Modeling phase in the CRISP-DM framework. Now, you need to assess how well the model performs before moving forward with deployment.

Which of the following steps aligns best with the Evaluation phase of CRISP-DM using NVIDIA technologies?

17. You are working on a data science project where you need to process a large dataset containing 500 million records. You want to determine whether GPU acceleration would significantly improve performance.

Which of the following factors best indicates that you should use an accelerated computing solution like RAPIDS?

18. You are working on an MLOps workflow that loads a dataset into GPU memory for model training using RAPIDS cuDF. Before performing transformations, you want to verify that the dataset will fit into available GPU memory.

Which of the following methods provides the most accurate estimate of dataset memory consumption in a RAPIDS cudf.DataFrame?

19. When performing benchmarking and optimization for GPU-accelerated workflows, which of the following tools is best suited for analyzing the memory utilization and computational efficiency of deep learning models running on Nvidia GPUs?

20. You are training a deep learning model for image classification and want to optimize its hyperparameters, including learning rate, batch size, and number of layers.

Which of the following techniques is the most effective for efficiently searching through a high-dimensional hyperparameter space?

21. What is the primary advantage of using NVIDIA Triton Inference Server for deploying and monitoring machine learning models in production?

22. A team of data scientists needs to deploy a machine learning model that depends on specific versions of CUDA and TensorFlow, ensuring it runs consistently across different machines without manually

configuring each system.

Which of the following approaches best ensures consistency while leveraging NVIDIA GPUs?

23. You have a pandas DataFrame with a column containing floating-point numbers, but it takes up too much memory. You want to convert it into a lower-precision type using CuDF or pandas while ensuring computational efficiency.

Which function would you use?

24. Which feature of NVIDIA MLFlow integration with Triton Inference Server allows for the seamless deployment and monitoring of models in production?

25. You are building a real-time recommendation system that processes high-frequency transactional data from millions of users.

The system must:

- Ingest and preprocess data efficiently

- Perform similarity computations for user-item recommendations

- Scale to handle rapid incoming transactions

Which of the following NVIDIA technologies is the best choice for this use case?

26. You are implementing a Dask-based solution for distributed data parallelism across a multi-GPU system.

Which configuration steps would ensure effective use of GPUs for parallel computation? (Select two)

27. A data engineering team is tasked with processing terabytes of log data every hour using an ETL pipeline. Due to the large data volume, they need a scalable GPU-accelerated solution that can distribute data processing across multiple GPUs.

Which approach best meets their needs?

28. You are processing a large dataset in a distributed computing environment using RAPIDS and Dask. Your workflow involves frequent shuffling of data between partitions, leading to significant slowdowns.

Which of the following strategies is the best way to implement data caching to reduce shuffle overhead using NVIDIA technologies?

29. Which NVIDIA technology is specifically designed for accelerating deep learning workloads in the cloud?

30. Which of the following can DLProf specifically help identify when profiling a deep learning model on Nvidia GPUs?

31. You are tasked with selecting the optimal data processing library for an AI project that involves handling varying dataset sizes. The project must be flexible enough to scale from small datasets (a few GBs) to large datasets (hundreds of GBs or more) using NVIDIA technologies.

Which of the following libraries would you choose for optimal performance at both small and large scales?

32. A data scientist is working with an imbalanced dataset in a fraud detection project. The dataset contains 1 million transactions, but only 2% of them are labeled as fraudulent. To improve the performance of the model, the scientist decides to generate synthetic data using NVIDIA RAPIDS cuDF.

Which of the following approaches is the best way to generate synthetic samples while preserving data characteristics?

33. You are using NVIDIA DLProf to analyze the performance of a deep learning model deployed on an A100 GPU. The report indicates that compute-bound operations are dominating execution time, and kernel execution efficiency is below 50%.

What is the best action to take based on this insight?

34. You are working with a large dataset using NVIDIA RAPIDS cuDF and need to normalize a numerical column (price) to scale its values between 0 and 1.

Which of the following approaches correctly normalizes the column using cuDF?

35. A data scientist is analyzing a large dataset of financial transactions containing millions of records.

To efficiently perform exploratory data analysis (EDA) using RAPIDS cuDF, which approach provides the most optimized performance while ensuring comprehensive insights?

36. You are training a machine learning model using RAPIDS cuML and need to ensure that all numeric features are standardized for better model performance.

Which of the following is the best approach for scaling data using RAPIDS?

37. A data scientist is working with a dataset of sensor readings (temperature, pressure, vibration) in different scales and units. To ensure all features contribute equally to a machine learning model, the data needs to be standardized.

Which approach is best for standardizing numerical features?

38. Which of the following tools can be used for profiling deep learning models to identify performance bottlenecks and optimize execution on NVIDIA GPUs? (Select two)

39. You are tasked with profiling a deep learning model using NVIDIA’s DLProf to identify performance bottlenecks and optimize resource utilization.

Which of the following statements correctly describes the capabilities of DLProf?

40. You are training a large-scale random forest model on a dataset with millions of rows and hundreds of features. The training time is significantly high when using traditional CPU-based machine learning frameworks.

Which NVIDIA technology should you use to accelerate training while maintaining compatibility with common ML frameworks like scikit-learn?