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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is using an NVIDIA RAPIDS-based data processing pipeline on a GPU cluster. They notice that the pipeline is not performing as expected and suspect a bottleneck.
Which of the following approaches would best help identify the source of the bottleneck?

A) Disable GPU parallelism and run computations on the CPU to compare performance.
B) Use nsys (NVIDIA Nsight Systems) to profile GPU kernel execution and memory transfer times.
C) Increase the batch size of data being processed without analyzing memory utilization.
D) Rely on nvidia-smi alone to check GPU utilization without considering other profiling tools.

2. A machine learning engineer runs NVIDIA DLProf to analyze the performance of a deep learning model and receives a report indicating high GPU idle time.
What is the most likely cause of this issue?

A) The model is experiencing data loading bottlenecks, causing the GPU to wait for input batches.
B) The batch size is too large, leading to excessive GPU memory utilization and slow processing.
C) The GPU is not powerful enough to process the deep learning model efficiently.
D) The CUDA cores are overheating, leading to automatic throttling of computations.

3. You are tasked with implementing data caching to reduce shuffle in an accelerated machine learning pipeline using NVIDIA technologies. You need to cache intermediate results after a shuffle operation in a distributed setting.
Which of the following is the best approach to minimize shuffle overhead and maximize performance?

A) Use RAPIDS cuDF to cache the shuffled data on disk and then re-load it from disk during subsequent stages.
B) Implement Spark's default disk caching to store shuffle results, allowing the GPU to access disk data directly.
C) Use DALI to perform preprocessing and cache the output before the shuffle operation, thereby eliminating the need for shuffle.
D) Cache the data in GPU memory using RAPIDS cuDF for faster access, and leverage GPU-based partitioning to reduce shuffle size.

4. A data engineering team is tasked with processing terabytes of log data every hour using an ETL pipeline. Due to the large data volume, they need a scalable GPU-accelerated solution that can distribute data processing across multiple GPUs.
Which approach best meets their needs?

A) Use cuDF alone for processing log data, as it provides optimal performance on a single GPU.
B) Use Dask-cuDF to distribute cuDF DataFrame operations across multiple GPUs, enabling parallel ETL processing.
C) Use NumPy for data transformations before converting the dataset into cuDF for final storage.
D) Process data using Pandas, then export the results to a CSV file for GPU-accelerated analytics.

5. You are processing a dataset of high-resolution images for deep learning training and want to optimize image loading, augmentation, and transformation on an NVIDIA GPU.
Which of the following statements correctly describes the role of NVIDIA DALI (Data Loading Library) in accelerating data preparation?

A) NVIDIA DALI only supports preprocessing images and does not include any functionality for video or text data.
B) NVIDIA DALI can offload image preprocessing tasks to the GPU, reducing CPU bottlenecks and improving training throughput.
C) NVIDIA DALI is an alternative to cuDF for GPU-accelerated tabular data processing.
D) NVIDIA DALI does not support data augmentation and is only used for raw data loading.

Solutions: