20+ practice questions focused on Evaluation and Monitoring — one of the most tested topics on the Databricks Certified Generative AI Engineer Associate exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Evaluation and Monitoring PracticeAn enterprise team is preparing to deploy a custom Large Language Model for a customer-facing support application on Databricks. They need to evaluate the model's responses for hallucination and groundedness using MLflow. Which specific evaluation utility should the ML Engineer utilize?
Explanation: MLflow provides specialized GenAI evaluation functions, notably mlflow.evaluate(), which natively integrates LLM-judges for metrics like groundedness and hallucination using context passages. This is crucial for production deployments to ensure reliability before user exposure. Ensuring systematic metric tracking prevents deployment regressions.
An AI engineer is configuring MLflow LLM Evaluation on Databricks to assess a Retrieval-Augmented Generation (RAG) pipeline. Which TWO evaluation metrics require both the retrieved context and the ground truth answer to compute accurately? (Select TWO)
Explanation: Evaluating RAG pipelines requires distinguishing between retrieval quality and generation quality. Certain metrics rely specifically on the retrieved context chunks, whereas others evaluate answer relevance against the provided ground truth or user query. Selecting the right combination ensures comprehensive diagnostic visibility.
Refer to the exhibit. The engineer notices that while inference logs are being captured, the standard Databricks monitoring dashboard for the endpoint is not populating any quality metrics. What is the most likely cause?
Explanation: The JSON configuration indicates that while `inference_tables` are enabled, the `monitoring_enabled` flag is set to false. Databricks Model Serving requires explicit configuration to bridge inference tables with automated monitoring dashboards. Without this flag, the platform does not trigger the internal evaluation processes that calculate and display quality metrics such as drift scores or response accuracy trends within the dashboard interface.
An organization is using 'LLM-as-a-judge' to evaluate their RAG application. What is the primary risk associated with this approach if the judge model is not carefully selected?
Explanation: Using an LLM to evaluate another LLM introduces bias and potential self-reinforcement loops, especially if the judge model is less capable or shares the same training biases as the model being evaluated. If the judge model lacks the required reasoning capability, it may systematically misgrade outputs. This creates a false sense of security where metrics look good but user experience remains poor due to flawed evaluation logic.
Refer to the exhibit. An engineer evaluates a RAG pipeline and sees the provided results. What is the most reasonable conclusion regarding the system's performance?
Explanation: The evaluation shows high faithfulness (0.95) but low answer relevance (0.60). This indicates the model is very good at using the retrieved information correctly but is struggling to align that information with the actual user query. The retriever might be returning accurate, high-quality documents that are simply not relevant to the user's question. The engineer should investigate the retrieval logic or the embedding strategy, not the generative model itself.
+15 more Evaluation and Monitoring questions available
Practice all Evaluation and Monitoring questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of Evaluation and Monitoring. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
Evaluation and Monitoring questions on the Databricks-GenAI-Assoc frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. Evaluation and Monitoring is tested as part of the Databricks Certified Generative AI Engineer Associate blueprint. Practicing with targeted Evaluation and Monitoring questions ensures you can handle any format or difficulty that appears.
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Difficulty is subjective, but Evaluation and Monitoring is a high-priority exam concept tested in multiple ways — direct recall, scenario analysis, and command-output interpretation. Consistent practice is the best way to build confidence.
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