AI0-001 Implementing AI Solutions Practice Question
A team is evaluating an LLM-based code generation assistant. They want to measure the quality of generated code for correctness, security, and efficiency. Which evaluation framework is BEST suited for this task?
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
✓
Pass@k metric using unit tests from benchmarks like HumanEval
HumanEval and similar frameworks (e.g., MBPP) use unit tests to automatically assess functional correctness of generated code, which is the most objective measure for code generation tasks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Human evaluation by a panel of experienced developers
Why it's wrong here
Human evaluation is subjective, expensive, and not scalable for continuous evaluation.
- ✓
Pass@k metric using unit tests from benchmarks like HumanEval
Why this is correct
Pass@k measures the probability that any of k generated samples pass a set of unit tests, directly assessing correctness.
- ✗
Perplexity of the model on a code corpus
Why it's wrong here
Perplexity measures language modeling ability but does not directly evaluate correctness or security of generated code.
- ✗
BLEU score comparing generated code to reference code
Why it's wrong here
BLEU measures n-gram overlap, which is poor for code; syntactically different but functionally correct code would score low.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.