End-to-end data science workflow: Practice Questions — Foundations of Accelerated Data Science (NVIDIA-Certified Associate: Accelerated Data Science)

End-to-End Data Science Workflow – Practice Questions

These multiple-choice questions are designed to test your understanding of the end-to-end data science workflow as covered in the Foundations of Accelerated Data Science domain of the NVIDIA-Certified Associate: Accelerated Data Science exam.

1. Which of the following best describes the first step in a typical end-to-end data science workflow?

   • A. Model deployment
   • B. Data collection and preparation
   • C. Model evaluation
   • D. Hyperparameter tuning

   Correct answer: B

   Explanation: Data collection and preparation is the initial step where raw data is gathered and cleaned to ensure quality before analysis or modeling.

2. In GPU-accelerated data science workflows, why is minimizing data transfer between CPU and GPU important?

   • A. To reduce the risk of data corruption
   • B. To decrease latency and improve overall processing speed
   • C. To increase CPU workload
   • D. To simplify code complexity

   Correct answer: B

   Explanation: Data transfer between CPU and GPU memory is relatively slow; minimizing transfers reduces latency and maximizes GPU acceleration benefits.

3. Which Python library is most commonly used for handling tabular data during the data preparation phase?

   • A. Matplotlib
   • B. TensorFlow
   • C. pandas
   • D. OpenCV

   Correct answer: C

   Explanation: pandas provides powerful data structures and functions for cleaning, transforming, and analyzing tabular data.

4. What is a key advantage of using distributed data science frameworks over GPU-accelerated frameworks?

   • A. They require less memory
   • B. They can scale across multiple machines for very large datasets
   • C. They always run faster on a single node
   • D. They eliminate the need for data preprocessing

   Correct answer: B

   Explanation: Distributed frameworks enable scaling across clusters of machines, handling datasets too large for a single GPU or node.

5. During the model development phase, which environment is commonly used for interactive coding and visualization in an accelerated data science workflow?

   • A. Jupyter Notebook
   • B. Command-line shell
   • C. Microsoft Word
   • D. SQL Server Management Studio

   Correct answer: A

   Explanation: Jupyter Notebook supports interactive code execution, visualization, and documentation, making it ideal for iterative model development.

6. Which step in the end-to-end workflow involves assessing model performance on unseen data?

   • A. Data cleaning
   • B. Model training
   • C. Model evaluation
   • D. Data visualization

   Correct answer: C

   Explanation: Model evaluation tests how well the trained model generalizes to new, unseen data, often using metrics like accuracy or RMSE.

7. In the context of accelerated data science, what is the primary role of GPU acceleration during the workflow?

   • A. To replace data preprocessing
   • B. To speed up computationally intensive tasks such as model training and inference
   • C. To increase CPU usage
   • D. To store large datasets permanently

   Correct answer: B

   Explanation: GPUs accelerate tasks like matrix operations and parallel computations, significantly reducing model training and inference times.

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