Databricks-GenAI-Assoc Evaluation and Monitoring Practice Question
A team is using MLflow LLM Evaluation with the built-in answer_correctness metric to compare two prompt templates for a question-answering application. They notice that answer_correctness scores are nearly identical, but manual review shows one template produces answers that are factually correct yet omit key supporting details. Which additional built-in metric should they add to their evaluation to surface this difference?
⚠ Common exam trap
The trap here is treating factual correctness and completeness as the same thing, when a response can be correct yet incomplete and needs a dedicated completeness metric to detect it.
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
✓
answer_completeness
When answers are factually correct but differ in how much supporting detail they include, a completeness metric is required. answer_completeness compares the response against the ground truth to check whether all key points are covered, making it the right built-in metric to expose the omission of supporting details that answer_correctness alone misses.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
groundedness
Why it's wrong here
groundedness checks whether the answer is supported by the provided context, which is critical for RAG but not for distinguishing answer completeness. An answer that omits details can still be fully grounded in the context. Therefore, adding groundedness would not highlight the difference in supporting detail between the two templates.
- ✓
answer_completeness
Why this is correct
answer_completeness evaluates whether the response covers all the key information present in the ground truth answer. Since the issue is that one template omits supporting details while remaining factually correct, this metric directly measures that gap and will differentiate the templates. It is the built-in metric designed to assess thoroughness against the reference.
- ✗
toxicity
Why it's wrong here
toxicity detects harmful or offensive language in the response. The scenario describes answers that are correct but incomplete, with no indication of inappropriate content. Adding toxicity would not reveal missing details and is irrelevant to the completeness problem the team is trying to diagnose.
- ✗
answer_similarity
Why it's wrong here
answer_similarity measures semantic similarity between the model output and the ground truth, often using embeddings. It can reward answers that are topically close even if they lack specific details, so it may not distinguish a complete answer from a vague but semantically similar one. It would not reliably surface the missing supporting details described in the scenario.
About these practice questions
This Databricks-GenAI-Assoc question is part of Courseiva's 330-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-GenAI-Assoc practice question is part of Courseiva's free Databricks 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 Databricks-GenAI-Assoc exam.