Databricks-GenAI-Assoc Design Applications Practice Question
When designing a production RAG application, which technique is most effective for preventing the LLM from hallucinating based on outdated information?
⚠ Common exam trap
Candidates often assume that simply increasing the LLM's context window or using a more advanced model will solve hallucinations, ignoring that the root cause is the outdated source data itself.
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
✓
Enforcing a streaming data pipeline to keep the vector index updated.
Implementing a robust data pipeline that refreshes the vector index in real-time or near-real-time ensures that the retrieved context is current. When coupled with source attribution, this allows the system to verify findings against the latest data. This approach is essential for maintaining accuracy, as stale information in the vector database directly leads to hallucinations that can damage user trust in the AI application.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increasing the temperature parameter of the model to maximum.
Why it's wrong here
Increasing the temperature makes the model more creative and random, which actually increases the likelihood of hallucinations. In RAG applications, a lower temperature is typically preferred to ensure that the model sticks strictly to the retrieved context, minimizing the risk of generating inaccurate or off-topic information.
- ✓
Enforcing a streaming data pipeline to keep the vector index updated.
Why this is correct
Keeping the vector index synchronized with the source data via a streaming pipeline ensures that the context retrieved during RAG is always current. This minimizes the risk of the model using outdated information, which is a major source of hallucinations in production systems that rely on rapidly changing business data.
- ✗
Restricting the LLM to a specific list of keywords for its output.
Why it's wrong here
Restricting output to keywords negates the natural language capabilities of an LLM. While it might prevent hallucinations, it results in an inflexible and unusable application. The goal of RAG is to leverage the generative power of the LLM while grounding it in relevant, up-to-date, and accurate source data.
- ✗
Adding a long system prompt instructing the model not to lie.
Why it's wrong here
While system prompts are important, they are insufficient to prevent hallucinations if the provided context is inherently stale or incorrect. Reliance on a prompt alone provides a false sense of security; the underlying data quality and the freshness of the retrieval index are far more critical for mitigating hallucination risk.
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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.