Describe Python, Reasons to Use, and Its Libraries DATA-ENGINEERING-OPTIMIZE Practice Questions (Page 2)

Questions 6–10

1. 6application · medium

   A data engineering team is building a new ETL pipeline to process semi-structured JSON logs from IoT devices. They need a language that allows rapid prototyping, has strong support for data manipulation, and can integrate with both relational and NoSQL databases. The team also wants to minimize the amount of boilerplate code required for file I/O and data transformation. Which language choice best satisfies these requirements?

   • APython, using its standard library and the pandas library for data manipulation
   • BJava, using the Spring Batch framework for enterprise-grade batch processing
   • CC++, using low-level memory management to maximize performance for large JSON files
   • DBash, using shell tools like jq and awk for JSON processing
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2. 7application · medium

   A data engineering team is building a pipeline to perform complex numerical transformations on a large, multi-dimensional array of sensor readings. The data is currently in a CSV file, and the team needs to load it, perform the calculations, and then output the results. Which combination of Python libraries is most appropriate for this task?

   • APySpark for loading the CSV and pandas for the numerical operations
   • Bpandas for loading the CSV and NumPy for the numerical operations on the resulting array
   • CNumPy for loading the CSV and pandas for the numerical operations
   • DThe standard library's csv module for loading and the math module for calculations
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4. 8expert · hard

   A data engineering team is building a real-time dashboard that requires processing a stream of data from a message queue. The team is considering using Python with the pandas library for the processing logic. The data volume is moderate, but the team is concerned about the Global Interpreter Lock (GIL) limiting performance. What is the most accurate assessment of this concern?

   • AThe GIL prevents true multi-threading in Python, so CPU-bound pandas operations will not scale across multiple cores using threads
   • BThe GIL is not a concern because pandas operations are entirely I/O-bound
   • CThe GIL is a problem only for I/O-bound operations, not for CPU-bound operations
   • DThe GIL can be easily bypassed by using the multiprocessing module, which is always the best solution
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5. 9foundation · easy

   A data engineering team is choosing a language for a new pipeline project. Which reason is a key advantage of Python that supports team collaboration and maintainability?

   • APython's execution speed is faster than compiled languages, making it the best choice for performance-critical pipelines.
   • BPython's syntax is concise and readable, which reduces the cognitive load required to understand and maintain code.
   • CPython is the only language that can connect to all major cloud providers' data services.
   • DPython is a statically typed language that catches all type errors at compile time, reducing runtime failures.
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6. 10foundation · easy

   Which Python library is best suited for processing very large datasets that require distributed computing across a cluster?

   • Apandas
   • BNumPy
   • Crequests
   • DPySpark
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