- A
Increase the model's context window size beyond the training limit
Why wrong: Extending context beyond training length can degrade performance and increase hallucinations.
- B
Fine-tune the model on a curated domain-specific corpus
Fine-tuning adapts the model's knowledge to the domain, improving accuracy.
- C
Use a higher temperature setting during generation
Why wrong: Higher temperature increases randomness, potentially increasing hallucinations.
- D
Apply chain-of-thought prompting for complex queries
Chain-of-thought encourages step-by-step reasoning, reducing factual errors.
- E
Implement Retrieval-Augmented Generation (RAG)
RAG retrieves relevant documents to ground the model's output, reducing hallucinations.
Question 126 of 1,000
AI0-001 AI Concepts and Techniques Practice Question
This AI0-001 practice question tests your understanding of ai concepts and techniques. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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)
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 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Implement Retrieval-Augmented Generation (RAG)
Why this is correct
RAG retrieves relevant documents to ground the model's output, reducing hallucinations.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco 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.
Detailed technical explanation
How to think about this question
Fine-tuning uses backpropagation on domain-specific data to adjust the transformer's attention weights and feed-forward layers, effectively biasing the model toward domain-valid token sequences. RAG (Option E) complements this by retrieving external documents at inference time, grounding generation in verifiable sources without altering model weights. Chain-of-thought prompting (Option D) improves reasoning step fidelity for complex queries, reducing logical hallucinations by forcing intermediate reasoning steps.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Concepts and Techniques — This question tests AI Concepts and Techniques — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Fine-tune the model on a curated domain-specific corpus — Option B is correct because 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.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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.
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