Determine the Differences Between Jupyter Notebooks and Pipelines CERTIFIED-AI-EXPERT Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   How do notebooks typically handle scaling to large datasets compared to pipelines?

   • ANotebooks automatically scale out to a cluster of machines.
   • BNotebooks scale by running multiple kernels on the same machine.
   • CNotebooks can handle any dataset size without performance issues.
   • DNotebooks are limited to the resources of their kernel and are not designed for horizontal scaling.
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2. 17foundation · easy

   Which scenario is a typical use case for a Jupyter notebook?

   • ADeploying a trained model as a REST API for real-time inference.
   • BOrchestrating a multi-step workflow with dependencies and retries.
   • CExploring a new dataset to understand its structure and test hypotheses.
   • DRunning a nightly data transformation job that must run unattended.
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4. 18foundation · easy

   What is a common challenge when using Jupyter notebooks in version control?

   • ANotebooks are stored as JSON files that include outputs, making diffs noisy.
   • BNotebooks are binary files that cannot be diffed.
   • CNotebooks cannot be stored in a version control system at all.
   • DVersion control systems do not support the notebook format.
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5. 19foundation · easy

   Which factor would lead you to choose a pipeline over a notebook?

   • AThe need to run the same process daily with minimal human intervention.
   • BThe need to share analysis results with non-technical stakeholders.
   • CThe need for interactive visualization of intermediate results.
   • DThe need to quickly prototype a machine learning model.
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6. 20application · medium

   A team of data scientists is working on a shared codebase for a machine learning project. They are using Git for version control. The team wants to ensure that code reviews are meaningful and that changes are easy to track. Which practice would best support this goal?

   • AKeep the core logic in Python modules and use notebooks only for exploratory analysis and visualization.
   • BUse a shared network drive for the codebase to avoid Git merge conflicts.
   • CStore all code in Jupyter notebooks and use a notebook diffing tool for reviews.
   • DHave each team member work on a separate branch and merge changes manually.
   Reveal Solution Discussion Select an answer first