Databricks-GenAI-Assoc Evaluation and Monitoring Practice Question
When monitoring a RAG application, you notice a high discrepancy between the retrieved context and the generated answer. Which metric would specifically help identify if the model is ignoring the provided context?
⚠ Common exam trap
Candidates often confuse faithfulness with context relevance, failing to realize that faithfulness specifically evaluates whether the answer is derived directly from the retrieved context.
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
✓
Faithfulness
Faithfulness measures whether the generated answer is derived exclusively from the retrieved context. If a model generates information not supported by the source text, it is 'hallucinating' or ignoring the context. Monitoring faithfulness is crucial for RAG systems because it directly detects when the model drifts away from the ground truth provided by the internal knowledge base, which is the primary value proposition of a RAG architecture.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Context Precision
Why it's wrong here
Context precision measures the quality of the retrieved chunks relative to the user query, focusing on whether the retriever found relevant info. It does not measure the generative model's behavior or its ability to stick to the provided context when formulating an answer to the user.
- ✓
Faithfulness
Why this is correct
Faithfulness specifically assesses whether the answer is logically derived from the provided context. High faithfulness indicates the model is respecting the context; low faithfulness suggests the model is generating responses based on its own training data, which leads to hallucinations and incorrect information in RAG systems.
- ✗
Retrieval Recall
Why it's wrong here
Retrieval recall measures the proportion of relevant information successfully retrieved from the knowledge base. While important for RAG, it does not assess how the generative model uses that information. Even with perfect recall, the generative model could still choose to ignore the context and hallucinate.
- ✗
Semantic Similarity
Why it's wrong here
Semantic similarity measures how close the generated answer is to a reference ground truth. It is a coarse-grained metric that does not specifically reveal whether the model ignored the context. An answer could be semantically similar to a reference but still hallucinated, failing the faithfulness requirement.
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.