WorksheetsSources & Sinks: TextIO & FileIO
Total questions: 9
Worksheet time: 5mins
What is the primary purpose of Text IO in Apache Beam?
To monitor a file directory for new files.
To read and write text files in a pipeline.
To perform complex operations on binary data.
To dynamically change the file destinations at runtime.
What feature does File IO in Apache Beam offer when working with files?
The ability to monitor a location for new files based on a pattern.
The ability to deduplicate messages from a stream.
The ability to transform binary data into text.
The ability to automatically compress large files.
How can Apache Beam dynamically determine the sink destination at runtime?
By using a fixed file name provided in the code.
By using dynamic destinations that adapt based on data characteristics.
By reading from a static file pattern.
By monitoring the system clock to trigger writes.
What is a key advantage of using dynamic destinations in a pipeline?
It allows for processing binary files in real-time.
It enables writing to multiple file systems without altering the code.
It ensures data is written in a specific format.
It compresses the output data before writing.
What is the benefit of using contextual IO in Apache Beam?
It enhances the ability to read and write binary data.
It simplifies the reading of multi-line CSV records.
It automatically monitors file directories for changes.
It allows for data deduplication within a stream.
How does Apache Beam handle errors when writing data using Text IO?
Apache Beam automatically retries the write operation indefinitely.
Apache Beam logs the errors and skips the problematic records.
Text IO does not handle errors; users must implement custom error handling.
Apache Beam triggers a pipeline failure if any error occurs during the write operation.
When should you prefer using File IO over Text IO in Apache Beam?
When you need to read or write data with complex metadata and structure.
When processing very small files where simplicity is more important than performance.
When you are working with binary data formats.
When you want to monitor directories for new files continuously.
Which of the following is true about performance optimization when using File IO in Apache Beam?
File IO automatically compresses data to improve performance.
Performance is optimized by manually configuring the file splitting logic to better parallelize work.
File IO performance is independent of the file size or format.
File IO is slower than Text IO and should be avoided for large datasets.
How does Apache Beam compare Text IO with BigQueryIO in terms of scalability?
Text IO is generally more scalable than BigQueryIO for large datasets.
BigQueryIO offers better scalability and performance for handling large datasets.
Both Text IO and BigQueryIO are equally scalable for all dataset sizes
Text IO is preferable for scalability when dealing with large text-based datasets.
