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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. When deciding whether to use GPU acceleration or a traditional CPU approach for a machine learning task, which of the following factors should be considered to determine if the data qualifies as "big data" and whether GPU acceleration is beneficial? (Select two)

A) CPU-based machine learning methods are always more effective for small datasets, regardless of the algorithm used.
B) GPU acceleration is beneficial when the dataset can be divided into independent chunks that can be processed in parallel.
C) The complexity of the algorithm being used plays a crucial role in deciding whether to use GPU acceleration, with more complex algorithms benefiting from parallel computation.
D) The dataset must be over 100GB in size to qualify as big data and warrant GPU acceleration.
E) The size of the dataset in terms of rows and columns is irrelevant when determining if it qualifies as big data.

2. You need to set up an isolated, GPU-accelerated environment for a deep learning project that requires specific CUDA, cuDNN, and RAPIDS versions.
Which of the following best ensures a reproducible environment using Docker?

A) Use a system-wide CUDA installation and mount the /usr/local/cuda directory into the container to provide GPU support.
B) Install NVIDIA drivers manually inside a Docker container every time it runs.
C) Build a container from an Ubuntu base image and manually install all dependencies without specifying versions.
D) Use the nvidia/cuda base image and specify the required RAPIDS and deep learning libraries in a Dockerfile.

3. You are working with a dataset containing hundreds of millions of records, and you need to perform ETL operations such as filtering, joins, and aggregations. Given the dataset size, which NVIDIA- accelerated library should you use to achieve optimal performance?

A) cuPy, because it provides GPU-accelerated array operations, making it the best option for processing tabular data.
B) NumPy, because it is optimized for numerical computing and offers better performance for handling tabular data.
C) cuDF, as it provides GPU-accelerated DataFrame operations similar to Pandas, allowing for efficient processing of large datasets.
D) Pandas, as it is widely used and supports all common DataFrame operations, even for very large datasets.

4. You are working on a machine learning pipeline using NVIDIA RAPIDS cuML and need to standardize the dataset to ensure that all features have a mean of 0 and a standard deviation of 1.
Which of the following methods should you use to achieve this in cuML?

A) cuml.preprocessing.OneHotEncoder()
B) cuml.preprocessing.LabelEncoder()
C) cuml.preprocessing.MinMaxScaler()
D) cuml.preprocessing.StandardScaler()

5. You are using NVIDIA DLProf to analyze the performance of a deep learning model deployed on an A100 GPU. The report indicates that compute-bound operations are dominating execution time, and kernel execution efficiency is below 50%.
What is the best action to take based on this insight?

A) Use DLProf's Tensor Core Analysis to check if the model is leveraging Tensor Cores effectively.
B) Increase the batch size to fully utilize available GPU memory and reduce per-sample processing overhead.
C) Enable mixed precision training to improve computational efficiency.
D) Reduce the number of layers in the model to decrease computation time.

Solutions: