Efficient processing and storage with Parquet: Practice Questions — Data Manipulation and Preparation (NVIDIA-Certified Associate: Accelerated Data Science)

Practice Questions: Efficient Processing and Storage with Parquet

These multiple-choice questions focus on the use of Parquet for efficient data processing and storage, a key topic in the NVIDIA-Certified Associate: Accelerated Data Science exam.

1. What is a primary advantage of using Parquet format for storing large datasets in GPU-accelerated data workflows?

   • A) It stores data in plain text for easy readability.
   • B) It uses a columnar storage format that enables efficient compression and faster queries.
   • C) It automatically converts data into JSON format.
   • D) It requires no schema definition.

   Correct Answer: B

   Explanation: Parquet is a columnar storage format that optimizes compression and query performance, which is essential for efficient GPU-accelerated data processing.

2. Which library is commonly used alongside Parquet files for GPU-accelerated data manipulation in the RAPIDS ecosystem?

   • A) cuDF
   • B) NumPy
   • C) Matplotlib
   • D) TensorFlow

   Correct Answer: A

   Explanation: cuDF is a GPU DataFrame library in RAPIDS designed to efficiently read and write Parquet files for accelerated data manipulation.

3. When reading Parquet files in a distributed environment using Dask, what is a key benefit?

   • A) Dask converts Parquet files to CSV automatically.
   • B) It allows parallel reading of column chunks, improving performance.
   • C) It requires data to be loaded into memory entirely before processing.
   • D) It disables compression to speed up reading.

   Correct Answer: B

   Explanation: Dask leverages Parquet’s columnar format to read data in parallel across partitions, enabling scalable and efficient distributed processing.

4. What metadata feature of Parquet files helps ensure data governance and schema consistency?

   • A) Embedded JSON schemas
   • B) Schema evolution and embedded schema metadata
   • C) Automatic data encryption
   • D) Row-level data versioning

   Correct Answer: B

   Explanation: Parquet files include embedded schema metadata that supports schema evolution and helps maintain data consistency and governance.

5. Which of the following is a best practice when writing Parquet files for accelerated data science pipelines?

   • A) Writing many small files to optimize parallelism
   • B) Using a single large file to minimize overhead
   • C) Balancing file size to optimize read/write performance and parallel processing
   • D) Avoiding compression to reduce CPU usage

   Correct Answer: C

   Explanation: Balancing file size is important to optimize both I/O performance and parallel processing capabilities in GPU-accelerated workflows.

6. How does Parquet support efficient handling of categorical variables in data storage?

   • A) By storing categories as strings without encoding
   • B) By using dictionary encoding to reduce storage size
   • C) By converting categories to floating-point numbers
   • D) By ignoring categorical variables during compression

   Correct Answer: B

   Explanation: Parquet uses dictionary encoding for categorical data, which reduces storage size and speeds up processing.

7. Which command in cuDF can be used to write a DataFrame to a Parquet file efficiently on GPUs?

   • A) cudf.read_csv()
   • B) cudf.to_parquet()
   • C) cudf.to_json()
   • D) cudf.write_text()

   Correct Answer: B

   Explanation: The cudf.to_parquet() function writes a cuDF DataFrame to a Parquet file, leveraging GPU acceleration for efficient storage.

8. What is a key benefit of using Parquet format over CSV in GPU-accelerated ETL pipelines?

   • A) Parquet files are human-readable.
   • B) Parquet supports schema and compression, reducing I/O and memory usage.
   • C) CSV files support nested data structures better.
   • D) Parquet files require no metadata.

   Correct Answer: B

   Explanation: Parquet’s columnar format with embedded schema and compression reduces I/O overhead and memory usage, which is critical for GPU-accelerated ETL.

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