AI0-001 Implementing AI Solutions Practice Question
A media company fine-tunes a large language model on Azure Machine Learning to generate sports recaps. After deployment, the model occasionally emits statistics that were never in the source game data. The team wants a systematic way to reduce these unsupported claims without retraining the base model. Which approach BEST addresses this?
⚠ Common exam trap
The trap here is treating hallucination as a sampling-temperature problem, when it is fundamentally a grounding problem that persists even under greedy decoding.
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 a retrieval-augmented generation pipeline that retrieves verified game statistics and constrains the model to cite retrieved passages.
Unsupported claims in generated text are best mitigated by grounding generation in a verifiable source. A retrieval-augmented generation pipeline supplies authoritative game statistics at inference time and instructs the model to rely on them, which reduces fabrication without altering the base model's weights. Deterministic decoding, more fine-tuning data, and regex filtering either miss the root cause or violate the no-retraining constraint.
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 fine-tuning dataset size by adding more sports articles and repeat the fine-tuning job.
Why it's wrong here
More training data may improve stylistic fluency but does not guarantee factual accuracy, and it directly violates the constraint of not retraining the base model. Fine-tuning teaches patterns, not a verified fact store, so the model can still generate plausible numbers that never appeared in any source. This approach also requires a full retraining cycle, which the team explicitly wants to avoid.
- ✗
Lower the temperature parameter to 0 and rely on greedy decoding to eliminate fabricated statistics.
Why it's wrong here
Greedy decoding reduces randomness and makes outputs more deterministic, but it does not prevent a model from confidently generating a plausible but unsupported statistic. The hallucination originates from the model's learned associations, not from sampling variance, so temperature zero can still produce the same fabricated number every time. This option addresses variability rather than factual grounding, so it fails to solve the underlying problem.
- ✗
Apply a post-processing regex filter that removes any numeric token not present in the prompt.
Why it's wrong here
A regex filter would strip legitimate statistics that the model correctly recalled from context, and it cannot distinguish supported from unsupported numbers semantically. It also breaks the natural language of the recap and does not prevent the model from fabricating non-numeric claims such as player actions or quotes. This is a brittle heuristic rather than a grounding mechanism, so it does not systematically reduce unsupported claims.
- ✓
Implement a retrieval-augmented generation pipeline that retrieves verified game statistics and constrains the model to cite retrieved passages.
Why this is correct
Retrieval-augmented generation grounds generation in an external, verifiable corpus of game statistics. By retrieving relevant passages and instructing the model to base its recap only on those passages, unsupported claims are dramatically reduced because the model has authoritative context at inference time. This addresses the root cause without retraining the base model, matching the team's constraint and providing a systematic, auditable mechanism.
About these practice questions
This AI0-001 question is part of Courseiva's 962-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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 CompTIA exam blueprint
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.