5.2 Describe the AI Capabilities for Code Generation and Rapid Prototyping AI-TECHNICAL-PRACTITIONER Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A developer uses an AI code generation tool to create a function that handles user authentication for a web application. The generated code appears to work in initial testing. What is the most critical consideration before deploying this code to production?

   • AThe code should be reformatted to match the team's style guide.
   • BThe AI tool's license must be verified to ensure the generated code can be used commercially.
   • CThe code must be thoroughly reviewed by a security expert for vulnerabilities and best practices.
   • DThe code should be run through a linter to check for syntax errors.
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2. 2application · medium

   A junior developer uses an AI tool to generate a script that parses log files. The script works on a sample log file, but the developer is concerned about its performance on much larger files. What is the most important limitation of AI-generated code that the developer should consider?

   • AThe generated code may not be optimized for performance and could be inefficient on large datasets.
   • BThe generated code will not be compatible with the developer's specific Python version.
   • CThe generated code will contain syntax errors that need to be fixed.
   • DThe AI tool will not be able to generate code for parsing log files.
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4. 3foundation · easy

   How does AI accelerate rapid prototyping?

   • ABy eliminating the need for user feedback during the prototyping phase.
   • BBy ensuring that the prototype is production-ready without further changes.
   • CBy automatically deploying prototypes to production environments.
   • DBy generating functional code quickly, allowing faster iteration and validation of ideas.
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5. 4application · medium

   A team is starting a new Python project for network automation. They need to set up a standard project structure with configuration files, a main script, and a README. Which set of tasks is the most appropriate use case for AI code generation?

   • AWriting the final, production-ready business logic for the entire automation suite.
   • BPerforming the final security audit and penetration testing of the generated code.
   • CDesigning the overall network architecture and selecting the appropriate protocols.
   • DGenerating the boilerplate project structure, configuration templates, and a basic README.
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6. 5expert · hard

   A developer is using an AI tool to generate code for a new feature. The developer notices that the AI is also generating documentation and unit tests for the feature. What is the most effective way to use these additional outputs?

   • ADiscard the tests and documentation, as they are likely to be inaccurate and a waste of time to review.
   • BIntegrate the tests and documentation without review, as the AI generated them from the same code.
   • CReview the generated tests and documentation for accuracy and completeness, and integrate them into the project.
   • DUse the tests to validate the code, but ignore the documentation as it is not critical for the feature.
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