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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question 1

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?

A. Disable CUDA graphs and enforce PyTorch's eager execution mode to improve kernel execution order.
B. Convert the model's data type to float16 or bfloat16 and re-run the training with automatic mixed precision (AMP).
C. Increase the batch size to maximize GPU memory usage and reduce kernel launch overhead.
D. Switch from stochastic gradient descent (SGD) to Adam optimizer, as Adam improves convergence and computational efficiency.

Question 2

When scaling a distributed data processing framework using NVIDIA GPU technology for big data processing, which of the following factors is most critical to optimize performance?

A. Using more CPU cores to handle computation-heavy tasks.
B. Ensuring the proper configuration of GPU resources across all nodes in the distributed system.
C. Maximizing the amount of data transferred between GPUs for faster processing.
D. Limiting the number of GPU nodes used in the cluster to avoid complexity.

Question 3

Which of the following is the most appropriate way to perform large-scale data processing in a GPU- accelerated environment using NVIDIA RAPIDS?

A. Use TensorFlow for all data manipulations in a GPU environment.
B. Use Dask on top of RAPIDS for distributed computing across multiple GPUs.
C. Use NumPy exclusively for processing large datasets on GPUs.
D. Use pandas for all data manipulations and rely on multi-threading for parallel execution.

Question 4

You are processing a large dataset using NVIDIA Dask-cuDF to distribute GPU-accelerated computation across multiple nodes. Users report inconsistent execution times, with some jobs taking significantly longer than expected.
Which of the following actions would best help diagnose the performance bottleneck?

A. Limit GPU memory usage to force more frequent spilling to disk and observe performance differences.
B. Switch to using Pandas with Dask to compare execution speed differences.
C. Reduce the number of Dask workers to minimize parallel execution overhead.
D. Use Dask's dashboard and NVTX markers to analyze task execution times and GPU utilization.

Question 5

You are working with a large dataset that contains missing values in multiple columns. Your goal is to prepare this dataset for training a machine learning model on an NVIDIA GPU using RAPIDS.
Which of the following approaches is the most efficient method to handle missing values in this scenario?

A. Apply a deep learning-based imputation model before moving data to the GPU
B. Use fillna() with a fixed value on the GPU using cuDF
C. Convert the dataset to a NumPy array and manually replace missing values with the mean
D. Drop all rows containing missing values using Pandas before transferring data to the GPU

Solutions: