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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. 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

2. A data scientist is working on a social network analysis project where they need to find the most influential users in a large-scale graph dataset. The dataset consists of millions of users connected through directed edges.
Which of the following approaches would be the best choice for this task using NVIDIA GPU-accelerated tools?

A) Use cuDF's groupby().sum() function to count the number of connections per user.
B) Convert the graph into a pandas DataFrame and apply NetworkX's PageRank implementation.
C) Use cuGraph's bfs() (Breadth-First Search) to find the most influential nodes.
D) Use cuGraph's pagerank() function to identify influential nodes based on link structure.

3. You are working with a GPU-based cloud environment and need to optimize the memory usage for a dataset that contains a column item_id representing unique product IDs. The item_id values are large integers, and there are over 10 million distinct product IDs.
Which of the following is the most memory-efficient data type choice for this column?

A) df['item_id'] = df['item_id'].astype('float64')
B) df['item_id'] = df['item_id'].astype('int64')
C) df['item_id'] = df['item_id'].astype('int32')
D) df['item_id'] = df['item_id'].astype('string')

4. You are working on an MLOps project where GPU-accelerated workflows are being used for model training. You want to benchmark and optimize these workflows to ensure the best performance.
Which of the following steps should you consider to effectively benchmark and optimize GPU- accelerated workflows? (Select two)

A) Use profiling tools to measure the GPU utilization and memory usage during training to identify performance bottlenecks.
B) Use a dynamic batch size strategy that adjusts the batch size based on available GPU memory to maximize throughput.
C) Increase the batch size and learning rate simultaneously to maximize GPU usage and reduce training time.
D) Optimize data loading by using data augmentation techniques during training to reduce the time spent on I/O operations.

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

Solutions: