NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on optimizing a deep learning model for inference on an NVIDIA GPU. You decide to use NVIDIA DLProf to profile the model and analyze its performance. After running DLProf, you review the generated reports and find that the GPU Utilization is significantly lower than expected.
Which of the following is the most likely reason for this issue, as indicated by the profiling data?

A) The model contains a large number of small, inefficient kernel launches that introduce overhead.
B) DLProf detected a high level of tensor core utilization, which generally indicates poor performance.
C) The GPU lacks sufficient VRAM, causing frequent memory swaps to system RAM.
D) The batch size is too large, leading to excessive memory allocation failures.

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

3. A machine learning engineer is training a large transformer-based model for natural language processing (NLP). They want to maximize training speed and efficiency using NVIDIA GPUs.
Which of the following techniques would most effectively enhance GPU utilization and reduce training time?

A) Using mixed-precision training with Tensor Cores
B) Running training exclusively on CPU
C) Disabling data parallelism
D) Prefetching data with the CPU while training on the GPU

4. You are working on a structured dataset of around 10GB and need to perform exploratory data analysis (EDA), feature engineering, and filtering operations efficiently using NVIDIA technologies. The dataset fits into a single GPU's memory.
Which data processing library should you use to achieve the best performance?

A) Spark with RAPIDS Accelerator
B) cuDF
C) pandas
D) Dask DataFrame with Dask-CUDA

5. You are working with a social network dataset containing millions of user interactions and need to identify influential users based on their connectivity and interactions.
Which approach using NVIDIA's cuGraph library is the most appropriate for this task?

A) Use cuGraph's DBSCAN clustering to detect communities in the social network.
B) Run a breadth-first search (BFS) on the entire graph to find the most influential users.
C) Use cuGraph's PageRank algorithm to rank users based on their importance in the network.
D) Apply cuGraph's K-Means clustering to group users with similar connectivity patterns.

Solutions: