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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are monitoring a GPU-accelerated ETL pipeline using RAPIDS cuDF and Dask-cuDF. You suspect that a bottleneck is causing the pipeline to slow down.
Which of the following methods is the most effective way to diagnose performance bottlenecks in your data processing pipeline?

A) Use print() statements in the code to manually track execution times of different operation
B) Increase the batch size of data loading without checking GPU memory usage
C) Use NVIDIA Nsight Systems to profile GPU utilization and identify potential kernel execution inefficiencies
D) Monitor CPU usage in the system to detect high CPU load that might indicate a bottleneck

2. A financial services company is deploying an AI-driven risk assessment model using NVIDIA GPUs on a cloud platform. To optimize resource utilization and cost efficiency, they need to determine the best GPU deployment strategy.
Which of the following is the most effective approach?

A) Run all AI workloads on a single large GPU instance without any partitioning or workload separation.
B) Choose an on-demand cloud instance with an outdated GPU model to reduce costs, even if performance is compromised.
C) Deploy the model using NVIDIAAI Enterprise with MIG (Multi-Instance GPU) to allocate multiple workloads on a single GPU efficiently.
D) Deploy separate full-GPU instances for each workload, even if they have variable compute demands.

3. You are developing an accelerated ETL workflow that requires data transformations such as filtering, aggregating, and joining large datasets. You decide to leverage NVIDIA GPUs to accelerate the transformation phase of your ETL pipeline.
Which of the following approaches will provide the greatest performance improvements when working with large-scale tabular datasets?

A) Relying on traditional pandas for in-memory transformations
B) Performing transformations using SQL-based queries on CPU
C) Using TensorFlow for data transformation tasks
D) Using RAPIDS cuDF to perform transformations on a GPU

4. You are training a machine learning model using NVIDIA RAPIDS cuML and notice that the training process is significantly slower than expected. You suspect that there are bottlenecks in data movement and computation.
Which of the following techniques can best help you diagnose and resolve these bottlenecks?

A) Move all data from GPU memory to CPU memory before training the model.
B) Use cuml.common.device_auto_mem_size() to check GPU memory usage and adjust batch sizes accordingly.
C) Reduce the number of features used in training without profiling the actual bottlenecks.
D) Use cudf.DataFrame.to_pandas() to convert the dataset to a pandas DataFrame for analysis.

5. A research team is analyzing large-scale social interactions and wants to identify strongly connected communities within a massive graph dataset using NVIDIA's cuGraph library.
Which method would be the most efficient for this task?

A) Apply cuGraph's Label Propagation Algorithm (LPA) to divide the graph into communities without predefining the number of clusters.
B) Apply cuGraph's Dijkstra's algorithm to find the shortest paths between all nodes and group them into communities.
C) Use cuGraph's Louvain method to detect hierarchical communities based on modularity optimization.
D) Run the cuGraph PageRank algorithm and classify nodes with high scores as community leaders.

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