5.2 Describe the AI Capabilities for Code Generation and Rapid Prototyping AI-TECHNICAL-PRACTITIONER Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   Which of the following is a common use case for AI code generation?

   • AGenerating unit tests for existing functions.
   • BManaging user authentication and authorization.
   • CMonitoring application performance in real time.
   • DDeploying applications to production environments.
   Reveal Solution Discussion Select an answer first
2. 17application · medium

   A developer uses an AI tool to generate a script that interacts with a network device's API. The script works as expected in a test environment. What is the most important consideration before running this script against production devices?

   • AThe script must be reviewed for potential security vulnerabilities and error handling.
   • BThe script's comments should be removed to reduce its file size.
   • CThe AI tool should be asked to generate a second version of the script for comparison.
   • DThe script should be obfuscated to protect the API keys it contains.
   Reveal Solution Discussion Select an answer first
4. 18application · medium

   A developer is exploring a new idea for a network monitoring tool. They want to quickly test if the concept of using WebSockets to push real-time metrics to a web dashboard is feasible. What is the most efficient first step using AI code generation?

   • AGenerate a minimal proof-of-concept with a WebSocket server and a simple client to test the data flow.
   • BGenerate a complete, production-ready monitoring tool with all features.
   • CGenerate a marketing plan for the new monitoring tool.
   • DGenerate a detailed design document for the full monitoring tool.
   Reveal Solution Discussion Select an answer first
5. 19expert · hard

   A team is using AI to rapidly prototype a new feature. The prototype is successful and the team decides to turn it into a production feature. What is the most important step the team should take?

   • ARefactor the prototype code to meet production standards, including error handling, security, and performance.
   • BDeploy the prototype code as-is to save time, since it has already been validated.
   • CAsk the AI to generate a new, production-ready version of the code.
   • DDiscard the prototype and start the development from scratch.
   Reveal Solution Discussion Select an answer first
6. 20expert · hard

   A small team needs to build a prototype for a new customer-facing portal. They have a tight deadline and limited development resources. They are considering using AI code generation to speed up the process. What is the most significant trade-off they should consider?

   • AThe AI tool will require a significant upfront investment in training and configuration.
   • BThe speed of generating the prototype may come at the cost of code quality and maintainability, requiring additional refactoring later.
   • CThe prototype will not be able to handle real user traffic and will need to be completely rewritten.
   • DThe team will become too dependent on the AI tool and lose their coding skills.
   Reveal Solution Discussion Select an answer first