AI0-001 AI Concepts and Techniques Practice Question
An AI engineer is fine-tuning a transformer-based language model for a domain-specific task. They want to improve the model's factual accuracy and reduce hallucinations. Which THREE strategies should they consider? (Select THREE)
⚠ Common exam trap
The CompTIA AI+ exam often tests the misconception that increasing randomness (higher temperature) or extending context windows beyond training limits can improve factual accuracy, when in fact these techniques degrade reliability.
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
✓
Fine-tune the model on a curated domain-specific corpus
Option B is correct because fine-tuning on a curated, high-quality domain-specific corpus injects accurate, task-relevant knowledge into the model's weights, which directly improves factual grounding and reduces the likelihood of fabricating domain facts. Option D is correct because chain-of-thought prompting encourages the model to decompose complex queries into intermediate reasoning steps, which empirically reduces errors and unsupported claims on multi-step or reasoning-heavy tasks. Option E is correct because Retrieval-Augmented Generation grounds generation in externally retrieved, up-to-date documents at inference time, so the model conditions its output on verifiable evidence rather than relying solely on parametric memory, which is the standard technique for reducing hallucinations. Option A is not appropriate because simply increasing the context window beyond the training limit is not a supported or effective strategy—models cannot attend beyond their trained positional range without architectural changes or extrapolation techniques, and a larger window alone does not improve factual accuracy. Option C is incorrect because raising the temperature increases sampling randomness and diversity, which typically increases hallucination rather than reducing it; lower temperature is preferred for factual accuracy.
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 model's context window size beyond the training limit
Why it's wrong here
A transformer's context window is fixed by its positional encoding and attention design; setting it beyond the trained limit degrades output rather than improving factual grounding. Extending context is tempting because larger windows genuinely help tasks needing retrieval of more source material at inference time.
- ✓
Fine-tune the model on a curated domain-specific corpus
Why this is correct
Fine-tuning on a curated domain-specific corpus updates the model's weights toward accurate, in-domain factual patterns, directly reducing hallucination on that task. This satisfies the stem's goal of improving factual accuracy by grounding the model in verified domain content rather than relying on broad pretraining data.
- ✗
Use a higher temperature setting during generation
Why it's wrong here
Raising temperature increases sampling randomness, producing more varied and less deterministic output, which worsens hallucination rather than reducing it. Higher temperature is tempting because it is genuinely useful for creative writing or brainstorming tasks where diverse, novel responses are wanted.
- ✓
Apply chain-of-thought prompting for complex queries
Why this is correct
Chain-of-thought prompting elicits intermediate reasoning steps, which improves factual accuracy on complex queries by making the model derive answers rather than guess. It satisfies the stem's hallucination-reduction goal for multi-step reasoning, though it complements rather than replaces domain grounding.
- ✓
Implement Retrieval-Augmented Generation (RAG)
Why this is correct
Retrieval-Augmented Generation grounds each response in retrieved external documents, so factual claims come from a verifiable corpus rather than parametric memory. This directly satisfies the stem's requirement to improve factual accuracy and reduce hallucinations by supplying evidence at inference time.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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