AI-103 Implement Generative AI And Agentic Solutions Practice Question
An agent is prone to 'hallucinations' when responding to specific niche queries. What is the most effective way to reduce these hallucinations using RAG?
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
✓
Set the grounding threshold to a higher value in the agent configuration.
Increasing the 'top_k' or relevance threshold ensures the retrieved data is highly specific to the query, reducing ambiguity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Retrain the base LLM on the niche dataset.
Why it's wrong here
Retraining is expensive and ineffective compared to RAG for specific facts.
- ✗
Increase the system prompt length.
Why it's wrong here
Prompt length does not fix retrieval quality.
- ✓
Set the grounding threshold to a higher value in the agent configuration.
Why this is correct
A higher grounding threshold ensures only the most relevant documents are passed to the model.
- ✗
Lower the temperature setting to 0.0.
Why it's wrong here
Lowering temperature limits creativity but does not guarantee the model will use relevant data.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
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