Describe Python, Reasons to Use, and Its Libraries DATA-ENGINEERING-OPTIMIZE Practice Questions (Page 3)

Questions 11–15

1. 11expert · hard

   A data engineer is building a pipeline that must process a 5 GB CSV file daily. The current single-threaded Python script using the standard csv module takes over an hour to complete, which is too slow. The engineer needs to reduce processing time to under 15 minutes. The data is too large to fit into a single machine's RAM. Which approach is the most practical and cost-effective solution?

   • AUse PySpark on a small, multi-node cluster to distribute the processing
   • BUse the multiprocessing module to parallelize the existing csv module script across all CPU cores
   • CUse NumPy to load the entire file into a multi-dimensional array for faster processing
   • DUse pandas with the chunksize parameter to process the file in memory-efficient chunks
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2. 12application · easy

   A data engineer is writing a script to automate a data quality check. The script needs to read a configuration file, connect to a database, run a query, and send an email alert if the query returns unexpected results. Which aspect of Python is most helpful for this task?

   • AIts extensive standard library and third-party modules for various tasks like config parsing, database connectivity, and email
   • BIts lack of support for third-party libraries, ensuring a secure and stable environment
   • CIts strict compile-time checking that catches all errors before the script runs
   • DIts requirement to use a single, monolithic framework for all tasks
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4. 13foundation · easy

   Which factor contributes to Python's strong community support and makes it a reliable choice for data engineering projects?

   • AA community that relies solely on commercial vendors for all library maintenance and support.
   • BA small, exclusive community that limits access to libraries to maintain code quality.
   • CA community that only supports data science use cases and ignores other applications like web development or automation.
   • DA large, active community that develops libraries, provides documentation, and offers support through forums and conferences.
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5. 14application · medium

   A data engineering team is building a pipeline to process data from a legacy system that outputs data in a proprietary binary format. The team needs to write a custom parser to read this data. They also need to integrate the parsed data with their existing Python-based data processing workflow. What is the most significant advantage of using Python for this task?

   • APython's inability to integrate with other languages, ensuring a pure Python solution
   • BPython's requirement to compile the parser into a standalone executable
   • CPython's ability to easily interface with C libraries for low-level binary parsing
   • DPython's lack of support for binary data, which forces the use of external tools
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6. 15application · medium

   A data engineer is tasked with selecting a language for a new data pipeline that will be maintained by a team of analysts who have strong SQL skills but limited programming experience. The pipeline must handle data cleaning, aggregation, and integration with a cloud data warehouse. The engineer needs a language that is easy for the analysts to learn and review. Which characteristic of Python is the primary reason it would be the best choice here?

   • AIt has a smaller standard library than other languages, forcing the use of external tools
   • BIts syntax is clear and readable, reducing the learning curve and making code reviews easier
   • CIt is the only language that can connect to a cloud data warehouse
   • DIts compiled nature ensures high runtime performance for large datasets
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