AI0-001 Implementing AI Solutions Practice Question
A media company is deploying a generative AI assistant that drafts marketing copy. Legal requires that every generated draft be attributable to source material and that the system must not reproduce copyrighted passages verbatim. The team wants to enforce this at generation time rather than only reviewing outputs afterward. Which implementation approach BEST meets these requirements?
⚠ Common exam trap
The trap here is assuming that raising temperature or fine-tuning automatically prevents verbatim reproduction, when neither attaches source attribution or restricts the model to an approved corpus.
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
✓
Retrieve grounding passages from an approved internal corpus, pass them to the model as context, and attach source citations to the generated draft.
Grounding generation in an approved internal corpus with retrieval and attaching citations satisfies both legal requirements simultaneously: attributability and reduced verbatim reproduction. The other approaches either act after generation, rely on sampling randomness, or use fine-tuning, none of which guarantees source attribution or constrains output to licensed material at inference time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Retrieve grounding passages from an approved internal corpus, pass them to the model as context, and attach source citations to the generated draft.
Why this is correct
Retrieval-augmented generation with an approved corpus constrains the model to cite verifiable sources and lets you attach provenance metadata to each draft. Because grounding passages are supplied at inference time, attribution is enforced at generation time, matching the legal requirement, and the internal corpus reduces the risk of reproducing external copyrighted text.
- ✗
Add a post-generation plagiarism check that blocks drafts containing long matching n-grams against a public web index.
Why it's wrong here
A post-generation filter operates after the draft exists, which the scenario explicitly rules out, and a public web index misses internal or licensed copyrighted sources. It also provides no attribution metadata, so drafts that pass the check still cannot be traced to approved source material. It addresses detection, not generation-time enforcement.
- ✗
Fine-tune the base model on the company's existing marketing copy and rely on the fine-tuned weights to avoid verbatim reproduction.
Why it's wrong here
Fine-tuning changes style and domain behavior but does not guarantee attribution or prevent memorized verbatim output. The model can still reproduce training passages, and fine-tuned weights carry no citation metadata. This approach also requires retraining whenever the source corpus changes, so it does not enforce the legal requirement at generation time.
- ✗
Increase the model's temperature setting so the assistant paraphrases source material instead of reproducing it.
Why it's wrong here
Temperature controls sampling randomness, not provenance. Raising it makes wording more varied but does not attach citations, does not restrict the model to approved sources, and can actually increase hallucinated or misattributed content. It fails the requirement that every draft be attributable to source material at generation time.
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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
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