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1). What is a Parquet file??
A) A text-based data storage format.
B) A columnar storage format for efficient data processing.
C) A row-based storage format for efficient data processing.
D) A compressed image file format.
2). How does Parquet differ from CSV or JSON formats??
A) Parquet is row-oriented, while CSV and JSON are column-oriented.
B) Parquet is compressed, while CSV and JSON are not.
C) Parquet is column-oriented, while CSV and JSON are row-oriented.
D) There is no significant difference between Parquet, CSV, and JSON.
3). What are the primary benefits of using Parquet for data storage??
A) Efficient compression and query performance.
B) Simple format and easy to read.
C) Small file size and fast random access.
D) High compatibility with different data processing tools.
4). How is data organized in a Parquet file??
A) Data is stored in rows.
B) Data is stored in columns.
C) Data is stored in a hierarchical structure.
D) Data is stored randomly.
5). What is the role of compression in Parquet files??
A) To reduce file size and improve performance.
B) To encrypt data for security.
C) To optimize data access patterns.
D) To increase data redundancy.
6). Can Parquet files handle complex data structures??
A) No, only simple data types are supported.
B) Yes, Parquet can handle nested and complex data structures.
C) Parquet supports complex data structures with limitations.
D) It depends on the specific implementation.
7). Which data processing frameworks support Parquet files??
A) Apache Spark, Hadoop, and Hive.
B) Excel, Python, and SQL Server.
C) JavaScript, Node.js, and React.
D) None of the above.
8). What is the relationship between Parquet and Hadoop??
A) Parquet is a component of Hadoop.
B) Parquet is often used with Hadoop for data processing.
C) Parquet is a replacement for Hadoop.
D) There is no relationship between Parquet and Hadoop.
9). What are some common use cases for Parquet files??
A) Web development and e-commerce.
B) Data warehousing and analytics.
C) Real-time data processing and streaming.
D) Image and video processing.
10). How does Parquet compare to other columnar formats like ORC and Avro??
A) Parquet is generally considered more efficient than ORC and Avro.
B) ORC is better for complex data structures, while Parquet is better for simple data.
C) Avro is more widely used than Parquet and ORC.
D) There are no significant differences between the three formats.
11). What is the role of schema in Parquet files??
A) Schema is optional and can be inferred from data.
B) Schema defines the structure of the data.
C) Schema is used for compression.
D) Schema is used for data encryption.
12). How can you create Parquet files??
A) Using text editors.
B) Using data processing frameworks like Spark or Hadoop.
C) Using database management systems.
D) All of the above.
13). What are some challenges associated with using Parquet files??
A) Large file sizes and complex schema.
B) Lack of support for different data types.
C) Difficulty in reading and writing Parquet files.
D) High computational overhead.
14). How can you optimize Parquet file performance??
A) By increasing file size.
B) By using appropriate compression codecs.
C) By storing data in a single large Parquet file.
D) By ignoring data quality.
15). What is the future of Parquet as a data storage format??
A) Parquet is becoming obsolete.
B) Parquet is expected to grow in popularity due to its efficiency.
C) There are no significant advancements expected in Parquet.
D) Parquet will be replaced by newer formats.
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