Describe Python, Reasons to Use, and Its Libraries DATA-ENGINEERING-OPTIMIZE Practice Questions (Page 1)

Questions 1–5

1. 1foundation · easy

   Which characteristic of Python makes it particularly suitable for data engineering tasks such as data extraction, transformation, and loading (ETL)?

   • AIt is a low-level language that gives fine-grained control over hardware resources.
   • BIt is a declarative language that requires no programming logic, allowing pipelines to be defined purely through configuration files.
   • CIt is a general-purpose language with a large ecosystem of libraries for data manipulation, connectivity, and automation.
   • DIt is a compiled language that produces highly optimized machine code for maximum performance.
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2. 2application · easy

   A data engineer is building a data pipeline that needs to perform a complex matrix multiplication on a large dataset. The dataset is currently stored in a pandas DataFrame. The engineer needs to extract the numerical data and perform the operation efficiently. Which library should the engineer use for the matrix multiplication?

   • ARequests
   • BPySpark
   • Cpandas
   • DNumPy
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4. 3application · medium

   A data engineer is evaluating Python for a new data pipeline. The engineer is concerned about the long-term maintainability of the codebase, which will be worked on by multiple teams. The engineer also needs to ensure that new hires can become productive quickly. Which two aspects of Python are the strongest arguments for its use in this situation?

   • AIts strong typing and complex syntax that enforce strict coding standards
   • BIts readability and the extensive support from a large, active community
   • CIts high execution speed compared to other interpreted languages
   • DIts ability to be compiled to machine code for standalone deployment
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5. 4expert · hard

   A data engineering team is deciding between Python and Java for a new mission-critical data pipeline. The team is highly proficient in Java and has moderate Python skills. The pipeline has strict latency requirements and will process a high volume of data. The team values the extensive ecosystem of Java libraries for enterprise integration. What is the most compelling reason to choose Python over Java in this scenario?

   • APython's stronger enterprise integration libraries will simplify connections to legacy systems
   • BPython's faster runtime performance will better meet the strict latency requirements
   • CPython's superior static typing will reduce runtime errors in the complex pipeline
   • DPython's rapid development and readability will allow the team to iterate faster on pipeline logic
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6. 5foundation · easy

   A data engineer needs to perform data manipulation and analysis on tabular data within a Python pipeline. Which library is specifically designed for this purpose?

   • AMatplotlib
   • Bpandas
   • CPySpark
   • DNumPy
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