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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

Fine-tuning on a curated domain-specific corpus directly aligns the model with the factual patterns and terminology of the target domain. This supervised learning process adjusts the model's weights to reduce the probability of generating incorrect or hallucinated content by reinforcing ground-truth examples from the domain.

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

    Extending context beyond training length can degrade performance and increase hallucinations.

  • Fine-tune the model on a curated domain-specific corpus

    Why this is correct

    Fine-tuning adapts the model's knowledge to the domain, improving accuracy.

  • Use a higher temperature setting during generation

    Why it's wrong here

    Higher temperature increases randomness, potentially increasing hallucinations.

  • Apply chain-of-thought prompting for complex queries

    Why this is correct

    Chain-of-thought encourages step-by-step reasoning, reducing factual errors.

  • Implement Retrieval-Augmented Generation (RAG)

    Why this is correct

    RAG retrieves relevant documents to ground the model's output, reducing hallucinations.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.