NVIDIA-Certified Professional NCP-ADS

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are building a fraud detection system and have a dataset that includes a feature representing transaction amounts. The values range from a few cents to several thousand dollars.
What is the most appropriate data type for this feature?

A) Categorical (grouping transactions into bins such as "low", "medium", "high").
B) Boolean (True for transactions above $100, False otherwise).
C) Floating-point (with precision to store cents or fractions of a cent).
D) Integer (rounding all transaction amounts to whole dollars).

2. You are preparing a dataset for training a machine learning model using NVIDIA RAPIDS cuML. The dataset contains a feature representing timestamps in nanoseconds.
To optimize GPU performance while ensuring precision, which data type should you choose?

A) datetime64[ns] - Optimizes storage and computation for timestamp data in RAPIDS.
B) object - Allows flexibility in storing timestamps as strings for easier parsing.
C) int32 - Uses less memory and can store high-precision timestamps efficiently.
D) bool - Provides a highly efficient way to store timestamps as binary values.

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

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

5. Which of the following hardware components is most critical for accelerating the training of deep learning models?

A) Graphics Processing Unit (GPU)
B) Random Access Memory (RAM)
C) Central Processing Unit (CPU)
D) Solid State Drive (SSD)

Solutions: