Databricks-GenAI-Assoc Application Development Practice Question
A developer is building a RAG application and notices that the retrieval step often returns irrelevant context. Which step in the pipeline should be improved to address this?
⚠ Common exam trap
Candidates often jump to increasing the number of retrieved chunks or changing the LLM model. These address symptoms rather than the root cause of poor semantic retrieval quality.
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
✓
Refine the chunking strategy and embedding model quality.
Improving retrieval accuracy often involves enhancing the quality of the embeddings or the text chunking strategy. By adjusting how data is partitioned into chunks (e.g., overlapping chunks, semantic chunking) or refining the embedding model, the vector search index can better capture the semantic meaning of the documents. This is a common and critical improvement path in RAG development, as the quality of the retrieval is fundamentally limited by the index's representational capability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of API calls to the LLM.
Why it's wrong here
Adding more API calls increases latency and cost without solving the root cause of irrelevant context. Retrieval quality is a data-processing challenge that happens before the LLM is involved, and increasing the volume of LLM interactions does nothing to improve the quality of the information retrieved by the search engine.
- ✓
Refine the chunking strategy and embedding model quality.
Why this is correct
The quality of retrieval is heavily dependent on how the source data is chunked and represented as vectors. Refining these parameters—such as using smaller, more meaningful segments or a higher-quality embedding model—is the standard approach to ensuring that the search engine finds the most relevant information for any given query.
- ✗
Enable auto-scaling on the serving endpoint.
Why it's wrong here
Auto-scaling is an infrastructure feature that handles traffic fluctuations; it has no impact on the quality of the information retrieved. Even with infinite compute resources, a sub-optimal retrieval configuration will continue to return irrelevant data, making this a completely irrelevant fix for the identified problem of poor search quality.
- ✗
Switch to a larger, more expensive LLM.
Why it's wrong here
While a larger model might be better at reasoning, it cannot fix the fundamental issue of poor information retrieval from the vector store. If the context passed to the model is irrelevant or incorrect, even the most advanced model will struggle to produce a useful or accurate response for the user.
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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
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.