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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. 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?

A) Enable GPU-accelerated caching with RAPIDS cuDF and persist intermediate results in GPU memory.
B) Disable caching altogether to force a recomputation of results, ensuring up-to-date data processing.
C) Use a CPU-based caching solution like Memcached to store intermediate data before reloading into cuDF.
D) Use traditional disk-based caching by writing intermediate results to CSV files and reloading when needed.

2. 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?

A) Nvidia Nsight Compute
B) Nvidia Riva
C) Nvidia TensorRT
D) Nvidia CUDA Profiler

3. 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?

A) Assume that a high training accuracy guarantees excellent real-world performance, skipping the evaluation phase.
B) Rely only on training loss as the primary evaluation metric without considering validation performance.
C) Use RAPIDS cuML to rapidly compute evaluation metrics like RMSE and R-squared on large datasets using GPUs.
D) Perform evaluation on a small CPU-based subset of the dataset instead of using full GPU-accelerated inference.

4. You are working on a financial dataset that tracks stock prices over time, and you need to detect anomalies such as sudden spikes or drops using NVIDIA technologies.
Which of the following approaches would be the most effective for anomaly detection in a time-series dataset using NVIDIA's RAPIDS AI and TensorRT?

A) Use RAPIDS cuML's Isolation Forest for anomaly detection and deploy it with NVIDIA Triton Inference Server.
B) Perform anomaly detection by applying DBSCAN clustering with RAPIDS cuML without any feature engineering.
C) Use traditional ARIMA modeling with RAPIDS cuML to classify anomalies based on residual analysis.
D) Apply a traditional rule-based thresholding method using pandas and NumPy for detecting sudden spikes in stock prices.

5. You are working on a large-scale machine learning workload that involves training a deep learning model using multiple GPUs. You want to leverage Dask to implement data parallelism efficiently using NVIDIA GPUs.
Which of the following approaches best achieves data parallelism in this context?

A) Run a single large Dask task on the CPU and use Dask-MPI for multi-GPU execution
B) Use Dask DataFrame to parallelize deep learning model training across multiple GPUs
C) Leverage Dask-CUDA to automatically assign computations to available GPUs using the worker pool
D) Use Dask with CuPy to distribute NumPy-based computations across multiple GPUs

Solutions: