Mlflow & Evaluation — Databricks Context Associate Practice Question
A representative Databricks Certified Context Engineer Associate (Databricks Context Associate) exam question on Mlflow & Evaluation. Work through it below, then read why each option is right or wrong.
This question is part of the free Databricks Certified Context Engineer Associate practice test.
Short answer
The correct answer is D. Running a RAGAS-style evaluation pipeline in MLflow that scores 'faithfulness' by comparing generated answers against retrieved context chunks.
To empirically diagnose hallucinations in RAG (Retrieval-Augmented Generation) systems, 'faithfulness' metrics are essential. These metrics evaluate whether the generated answer is strictly derived from the retrieved context. MLflow supports evaluation pipelines where frameworks like RAGAS can be used to compute faithfulness scores by comparing model output against the source chunks. Increasing temperature exacerbates hallucination; token counts measure cost not accuracy; and surveys provide qualitative feedback but do not isolate the retrieval vs. generation failure mode.
The Question
A team has deployed a customer service bot and is observing an increase in 'hallucinated' responses regarding product return policies. They need to quantitatively measure the accuracy of the retrieved context against the ground truth policy documents to identify if the issue lies with the retrieval quality or the generation model. Which evaluation method within the Databricks ecosystem provides the most direct empirical evidence for this specific gap?
Why D is correct
To empirically diagnose hallucinations in RAG (Retrieval-Augmented Generation) systems, 'faithfulness' metrics are essential. These metrics evaluate whether the generated answer is strictly derived from the retrieved context. MLflow supports evaluation pipelines where frameworks like RAGAS can be used to compute faithfulness scores by comparing model output against the source chunks. Increasing temperature exacerbates hallucination; token counts measure cost not accuracy; and surveys provide qualitative feedback but do not isolate the retrieval vs. generation failure mode.
Why the other options are wrong
Option A does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (D) is the only one that fully meets every constraint stated in the question.
Option B does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (D) is the only one that fully meets every constraint stated in the question.
Option C does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (D) is the only one that fully meets every constraint stated in the question.
Key idea: Mlflow & Evaluation
To empirically diagnose hallucinations in RAG (Retrieval-Augmented Generation) systems, 'faithfulness' metrics are essential. These metrics evaluate whether the generated answer is strictly derived from the retrieved context. MLflow supports evaluation pipelines where frameworks like RAGAS can be used to compute faithfulness scores by comparing model output against the source chunks. Increasing temperature exacerbates hallucination; token counts measure cost not accuracy; and surveys provide qualitative feedback but do not isolate the retrieval vs. generation failure mode. On the Databricks Context Associate exam, questions in the "Foundations of Context Engineering" domain test whether you can map a scenario's constraints to the right choice. Read the requirement carefully, eliminate options that violate any single constraint, and pick the one that satisfies all of them with the least operational overhead.
Ready to see how you'd score?
Take the free practice quiz and find out which Databricks Certified Context Engineer Associate domains you need to focus on. No signup required.
Practice 5 similar questions
Same cert, same or adjacent domain. Use these after reviewing the explanation.
Related Databricks Certified Context Engineer Associate Practice Questions
Official sources used
Verify program and exam details at the source before scheduling.