Key Architectural Components and Concepts of LLM-Powered Test Infrastructure CT-GENAI Practice Questions (Page 1)

Questions 1–5

1. 1expert · hard

   A QA team is integrating an LLM into their test execution pipeline to generate test scripts. They have a strict requirement that all generated scripts must be reviewed by a human before being used. However, they also need to maintain a fast release cycle. What is the best way to balance these constraints?

   • AGenerate scripts in parallel and have human reviewers approve them in batches before the next release.
   • BAutomate the review process using a second LLM to approve scripts without human involvement.
   • CSkip human review for low-risk scripts and only review high-risk ones.
   • DUse the LLM to generate scripts only after the release, so there is no time pressure.
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2. 2expert · hard · select all that apply

   A QA team is using an LLM to automate test activities. They want to leverage the LLM for multiple roles. Which of the following are valid roles for the LLM in test automation? (Select all that apply.)

   • AExecuting test cases and comparing actual vs expected outcomes.
   • BAnalyzing test results to identify root causes of failures.
   • CCreating synthetic test data that mimics production.
   • DProvisioning test environments and managing infrastructure.
   • EGenerating test scripts from acceptance criteria.
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4. 3application · medium

   A QA team is using an LLM to automate the creation of test data for their test cases. They want the LLM to generate realistic data that matches the production environment. Which role is the LLM playing?

   • ATest execution, where the LLM runs the test cases.
   • BResult analysis, where the LLM interprets test outcomes.
   • CTest environment management, where the LLM provisions resources.
   • DTest data generation, where the LLM creates synthetic data based on production patterns.
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5. 4application · medium

   A QA team is building an LLM-powered test infrastructure. They want to generate test cases from user stories, execute them, and automatically analyze failures. The team needs to ensure that the test data used for generation is consistent and traceable. Which architectural component should they prioritize to manage the test data lifecycle?

   • AA monitoring dashboard that tracks LLM token usage and response times.
   • BA CI/CD pipeline that triggers test generation on every code commit.
   • CA dedicated test data management service that stores, versions, and provisions test data for LLM prompts and test execution.
   • DA load balancer that distributes test execution requests across multiple LLM endpoints.
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6. 5application · medium

   A team is deploying an LLM-powered test infrastructure. They expect variable workloads, with peak demand during release cycles. They need to manage resources efficiently without over-provisioning. Which infrastructure approach is most suitable?

   • AProvision a fixed number of GPU instances to handle the peak load at all times.
   • BUse auto-scaling to adjust the number of LLM inference instances based on current demand.
   • CRun all LLM inference on a single powerful server to simplify management.
   • DUse only serverless functions for all test execution, including LLM calls.
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