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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question 1

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?

A. DLProf is primarily designed for debugging model accuracy rather than performance analysis.
B. DLProf only works with TensorFlow models and does not support PyTorch-based workloads.
C. DLProf requires significant modifications to the source code to collect profiling data.
D. DLProf can generate detailed reports that highlight kernel-level execution times and GPU utilization trends.

Question 2

A financial institution is training a fraud detection model on a GPU-powered NVIDIA RAPIDS cuML pipeline. Their dataset includes customer ages, transaction amounts, merchant names, and transaction timestamps.
To optimize GPU memory usage while preserving accuracy, how should they store these features?

A. Convert all numeric data to float64 for maximum precision, as rounding errors in lower precision could impact the model's accuracy.
B. Store transaction amounts as float32, customer ages as int8, merchant names as categorical, and timestamps as datetime64.
C. Keep customer ages in float16 format to reduce memory consumption, as integer types are less efficient in GPU operations.
D. Convert timestamps into UNIX epoch integers instead of using datetime64 for more efficient GPU computations.

Question 3

Which of the following actions can you perform using DLProf to analyze a deep learning model's performance?

A. Visualize GPU memory utilization over time
B. Modify the training dataset during model execution
C. Automatically adjust the learning rate based on the model's convergence
D. Increase batch size to improve accuracy

Question 4

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

Question 5

Which of the following is the primary benefit of using NVIDIA RAPIDS in the context of data science workflows?

A. It simplifies the creation of visualizations for large datasets
B. It speeds up the preprocessing and analysis of large datasets by utilizing GPUs
C. It allows for the implementation of reinforcement learning algorithms
D. It enables real-time data streaming and analytics

Solutions: