- A
Use a different tokenizer during fine-tuning.
Why wrong: Using a different tokenizer does not address the model's tendency to fabricate legal clauses; it only changes the encoding of input.
- B
Decrease the temperature parameter to 0.1 during inference.
Why wrong: Decreasing the temperature reduces randomness but does not prevent the model from generating false information; hallucinations persist.
- C
Implement retrieval-augmented generation (RAG) to provide factual context.
RAG provides relevant context from a knowledge base, allowing the model to base summaries on retrieved facts, which significantly reduces hallucinations.
- D
Set a maximum token limit of 50 for each summary.
Why wrong: Limiting output length may cut off hallucinated content but does not prevent it from occurring; the model still may produce false statements within the limit.
AI0-001 AI Implementation and Operations Practice Question
This AI0-001 practice question tests your understanding of ai implementation and operations. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
A data scientist fine-tunes a large language model for a legal document summarization task. After fine-tuning, the model performs well on test data but produces summaries that include hallucinated legal clauses. Which mitigation strategy is most effective?
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
Implement retrieval-augmented generation (RAG) to provide factual context.
The correct answer is C: Implement retrieval-augmented generation (RAG) to provide factual context. RAG reduces hallucinations by allowing the model to retrieve relevant, factual information from an external knowledge base during generation, grounding its output in verified data. Option A (different tokenizer) does not address the core issue of factual accuracy. Option B (decrease temperature) affects randomness but does not prevent the model from fabricating content. Option D (max token limit) truncates output but does not stop the model from including false information within that limit.
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.
- ✗
Use a different tokenizer during fine-tuning.
Why it's wrong here
Using a different tokenizer does not address the model's tendency to fabricate legal clauses; it only changes the encoding of input.
- ✗
Decrease the temperature parameter to 0.1 during inference.
Why it's wrong here
Decreasing the temperature reduces randomness but does not prevent the model from generating false information; hallucinations persist.
- ✓
Implement retrieval-augmented generation (RAG) to provide factual context.
Why this is correct
RAG provides relevant context from a knowledge base, allowing the model to base summaries on retrieved facts, which significantly reduces hallucinations.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set a maximum token limit of 50 for each summary.
Why it's wrong here
Limiting output length may cut off hallucinated content but does not prevent it from occurring; the model still may produce false statements within the limit.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Command / output trap
Limiting output length may cut off hallucinated content but does not prevent it from occurring; the model still may produce false statements within the limit.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Implementation and Operations — This question tests AI Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement retrieval-augmented generation (RAG) to provide factual context. — The correct answer is C: Implement retrieval-augmented generation (RAG) to provide factual context. RAG reduces hallucinations by allowing the model to retrieve relevant, factual information from an external knowledge base during generation, grounding its output in verified data. Option A (different tokenizer) does not address the core issue of factual accuracy. Option B (decrease temperature) affects randomness but does not prevent the model from fabricating content. Option D (max token limit) truncates output but does not stop the model from including false information within that limit.
What should I do if I get this AI0-001 question wrong?
Identify which AI0-001 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jun 22, 2026
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