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Implementing AI SolutionshardMultiple SelectObjective-mapped

AI0-001 Implementing AI Solutions Practice Question

A team is deploying a fine-tuned LLM for generating code snippets. They want to test the system thoroughly before production. Which THREE testing types should they include in their test plan? (Select THREE)

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Integration tests for API calls between the application and the LLM endpoint

Unit tests (E) validate data pipelines for training and inference data quality. Integration tests (A) verify that API calls between the application and the LLM endpoint function correctly. An evaluation framework (D) assesses LLM output quality, e.g., correctness or syntax using metrics like BLEU. Load testing (C) is for performance and scalability, not for testing correctness; regression testing (B) is important but typically performed after model updates, not as a primary testing type. Therefore, the three core testing types are integration tests, evaluation framework, and unit tests (A, D, E).

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Integration tests for API calls between the application and the LLM endpoint

    Why this is correct

    Integration tests verify that the application correctly communicates with the LLM service, handling requests and responses.

  • Regression testing only after model updates

    Why it's wrong here

    Regression testing is important but typically performed after changes; the question asks for testing types to include in the plan, and the three selected (A, C, D) are core to a comprehensive test strategy.

  • Load testing to determine maximum concurrent users

    Why it's wrong here

    Load testing is for performance and scalability, not for functional correctness or output quality.

  • Evaluation framework for LLM output quality, such as BLEU or custom correctness metrics

    Why this is correct

    Evaluation frameworks measure the quality of generated code, ensuring it meets requirements and is syntactically correct.

  • Unit tests for data pipelines that prepare training and inference data

    Why this is correct

    Unit tests catch data issues early, ensuring that data transformations are correct before model training or inference.

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