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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

2. You are using RAPIDS and Dask-cuDF to process a large-scale ETL pipeline. The workflow involves multiple join and groupby operations, which are causing excessive shuffling.
How can you best optimize caching to reduce shuffle overhead?

A) Split the dataset into multiple smaller Pandas DataFrames and store them in memory for quick retrieval.
B) Force every operation to be recomputed from the raw dataset to ensure accurate results.
C) Use dask.persist() to store frequently accessed cuDF DataFrames in GPU memory, reducing recomputation and shuffle operations.
D) Cache data using Apache Arrow's in-memory format, but process all operations on CPU.

3. You are a data scientist working with a large dataset containing millions of records. You want to perform exploratory data analysis (EDA) efficiently using NVIDIA RAPIDS on a GPU-accelerated system.
Which of the following approaches is the most efficient way to handle large-scale EDA using RAPIDS?

A) Perform all EDA using NumPy and SciPy for optimized array computations.
B) Load the dataset into an Apache Spark DataFrame and run .show() to inspect the data.
C) Use Pandas directly for data manipulation and visualization.
D) Convert the dataset into a cuDF DataFrame and perform operations like .describe() and
.value_counts() on the GPU.

4. 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) Build a container from an Ubuntu base image and manually install all dependencies without specifying versions.
C) Use the nvidia/cuda base image and specify the required RAPIDS and deep learning libraries in a Dockerfile.
D) Install NVIDIA drivers manually inside a Docker container every time it runs.

5. You are tasked with designing an ETL workflow for a large-scale data processing pipeline using NVIDIA technologies. You need to ensure that the extraction, transformation, and loading phases are optimized for performance using hardware acceleration.
Which of the following NVIDIA technologies would be most suitable for accelerating the ETL process?

A) CUDA
B) TensorFlow
C) TensorRT
D) RAPIDS

Solutions: