CCAO-F Claude Model Fundamentals Practice Question
Which of the following is an effective technique for reducing hallucination in Claude when answering fact-based questions?
⚠ Common exam trap
Candidates often mistakenly choose 'increasing the temperature' or 'adding more examples', which can actually increase hallucination risk instead of using grounding techniques to force honesty.
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
✓
Provide the context and require the model to answer 'I don't know' if the answer is missing.
Grounding is the primary method to combat hallucination. By providing the model with a 'grounding document' and explicitly instructing it to state 'I don't know' if the answer isn't present in the provided text, you shift the model's behavior from generative to extractive. This pattern is essential for high-stakes enterprise applications, where accuracy and honesty about limitations are more valuable than a guess, protecting the application's integrity and user trust.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Instruct the model to always provide an answer, even if unsure.
Why it's wrong here
Forcing the model to answer when unsure is a direct recipe for hallucination. In enterprise settings, it is far safer and more accurate for the model to admit it lacks the information rather than fabricate an answer. This instruction promotes integrity and prevents the dissemination of incorrect information.
- ✗
Include a system instruction that explicitly allows the model to use external knowledge.
Why it's wrong here
Allowing the model to rely on its internal pre-training knowledge when a grounding document is provided defeats the purpose of RAG. It increases the risk of the model hallucinating facts that contradict the document. Restrictive prompts are much more effective at maintaining factual consistency in document-specific tasks.
- ✓
Provide the context and require the model to answer 'I don't know' if the answer is missing.
Why this is correct
This technique forces the model to treat the context as the sole source of truth. By explicitly providing a fallback strategy for missing information, you prevent the model from attempting to invent an answer, which is the most effective way to ensure factual reliability and reduce potential hallucinations.
- ✗
Run the same prompt three times and take the most frequent answer.
Why it's wrong here
While 'majority voting' can be a strategy for logic tasks, it is an inefficient and unreliable way to handle factual information. It significantly increases API costs and latency without addressing the root cause of the hallucination. Prompt engineering for grounding is a far more effective and sustainable solution.
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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 Anthropic exam blueprint
This CCAO-F practice question is part of Courseiva's free Anthropic 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 CCAO-F exam.