Databricks-GenAI-Assoc Application Development Practice Question
A developer is building a RAG application. Which step is essential to prevent the model from hallucinating or providing outdated information?
⚠ Common exam trap
Candidates often choose manual static dataset uploads or model fine-tuning, overlooking that preventing hallucinations with fast-changing data specifically requires a real-time data ingestion pipeline into the vector database.
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
✓
Implement a real-time data ingestion pipeline for the vector database.
Ensuring data freshness and providing high-quality, relevant context to the LLM during the retrieval step is critical. By implementing a robust data ingestion pipeline that updates vector indices in near real-time, the application ensures that the model is always informed by the most recent documents. This practice mitigates the risk of hallucinations by grounding the model's responses in verified, up-to-date business data, which is essential for building trustworthy GenAI solutions.
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 temperature parameter of the model to 1.5.
Why it's wrong here
High temperature values increase the randomness of the model's output, which significantly increases the likelihood of hallucinations rather than decreasing them. For RAG applications requiring factual accuracy, a lower temperature is typically preferred to ensure that the model remains focused on the provided context and produces consistent, grounded results.
- ✗
Use a larger model to automatically detect and correct hallucinations.
Why it's wrong here
While larger models may be more capable, they are not immune to hallucinations and do not automatically 'detect and correct' them without specific prompting or guardrails. Relying on the model's inherent size to solve data freshness or accuracy issues is unreliable and does not replace the need for quality data grounding.
- ✓
Implement a real-time data ingestion pipeline for the vector database.
Why this is correct
Keeping the vector database up-to-date with the latest information is the most effective way to prevent hallucinations caused by outdated data. By ensuring that the RAG pipeline always retrieves the most current documents, the model is grounded in relevant, recent facts, which significantly improves the reliability and accuracy of the output.
- ✗
Force the model to always answer using a specific tone of voice.
Why it's wrong here
Enforcing a specific tone of voice affects the style of the output, not the factual accuracy or the grounding of the content. While important for user experience, it does nothing to prevent hallucinations or ensure that the information retrieved from the vector database is accurate, relevant, or up to date.
About these practice questions
One of 330 original Databricks-GenAI-Assoc practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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.