5.1 Describe AI's Role Across the Software Development Lifecycle (requirements, Prototyping, Implementation, Testing, Deployment) AI-TECHNICAL-PRACTITIONER Practice Questions (Page 1)

Questions 1–5

1. 1foundation · easy

   How does AI enhance the testing phase of the SDLC?

   • ABy automatically rolling back failed deployments
   • BBy automatically generating test cases and predicting likely failure areas
   • CBy analyzing user feedback for new requirements
   • DBy generating code for new features
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2. 2application · medium

   A software team wants to adopt AI across the entire SDLC to improve efficiency and quality. They have a limited budget and need to choose where to start. Which approach would provide the most immediate and broad benefit?

   • AImplement AI in deployment only, since that is the final and most critical phase.
   • BImplement AI only in testing, since that is where most bugs are caught.
   • CImplement AI in all phases simultaneously with no prioritization.
   • DImplement AI in requirements analysis first, then gradually extend to prototyping, testing, and deployment.
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4. 3application · medium

   A product team is starting a new project and has collected hundreds of pages of legacy documentation, user forum posts, and support tickets. They need to extract and validate functional requirements quickly, but they are concerned about conflicting or ambiguous statements. Which AI-assisted approach best addresses this need?

   • AUse an AI code generator to create a prototype from the documentation and infer requirements from the prototype's behavior.
   • BManually create a requirements traceability matrix and use AI only to format it into a standard template.
   • CFeed all documents into a generative model and ask it to produce a final requirements document without further review.
   • DUse NLP to extract candidate requirements, then cluster them by topic and flag contradictions for human review.
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5. 4foundation · easy

   What is a key contribution of AI to the deployment phase of the SDLC?

   • ANatural language processing for requirements
   • BAutomated release management and predictive monitoring
   • CAutomated test case generation
   • DAutomated code review
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6. 5application · medium

   A startup is building a mobile app and wants to show investors a clickable prototype within a week. They have a list of user stories but no UI designer on staff. Which AI-driven approach would most effectively accelerate prototyping?

   • AUse AI to generate unit tests for the user stories, then manually design the UI based on the test cases.
   • BWrite detailed technical specifications and send them to an offshore development team to build a production-ready app.
   • CHire a freelance UI designer and ask them to create a static mockup from the user stories.
   • DUse an AI design tool to generate screen layouts from the user stories, then iterate based on stakeholder feedback.
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