Databricks-GenAI-Assoc Evaluation and Monitoring Practice Question
Which THREE components are critical to include in a comprehensive evaluation strategy for a RAG-based Generative AI application?
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
✓
Retrieval metrics (e.g., Hit Rate, MRR)
A robust RAG evaluation requires a holistic view that covers the entire pipeline. Evaluating just the generation or just the retrieval is insufficient because the quality of the final response is dependent on both stages. By measuring retrieval performance, generation faithfulness, and end-to-end answer quality, engineers obtain a full picture of where the application is succeeding and where it needs tuning to meet user requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Retrieval metrics (e.g., Hit Rate, MRR)
Why this is correct
Retrieval metrics are essential because they confirm whether the system is successfully finding the correct documents. If retrieval fails, no amount of prompt engineering or fine-tuning will lead the generator to provide a correct, factual answer.
- ✓
Generation metrics (e.g., Faithfulness, Answer Relevance)
Why this is correct
Generation metrics assess how well the LLM utilizes the provided context to answer the user query. This helps isolate whether the model is hallucinating or ignoring context, allowing for targeted improvements in system prompts or model parameters.
- ✓
End-to-end performance metrics (e.g., Answer Correctness)
Why this is correct
End-to-end metrics measure the final outcome for the user. Even if retrieval and generation components appear healthy individually, the final answer must still be factually correct and useful to satisfy the business requirements of the application.
- ✗
Hardware temperature monitoring for GPUs
Why it's wrong here
While GPU temperature is important for data center operations, it is an infrastructure metric that does not provide any insight into the effectiveness of the RAG pipeline or the quality of the generative AI outputs.
- ✗
Network packet loss percentage
Why it's wrong here
Network packet loss percentage measures transport reliability, not retrieval or generation quality, so it cannot assess grounding, relevance or faithfulness in a RAG pipeline. It is tempting because packet loss genuinely degrades distributed inference throughput, making it a valid metric for infrastructure capacity planning rather than application evaluation.
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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
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