Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
A financial services firm wants to deploy a generative AI assistant that summarizes earnings call transcripts for its analysts. The firm's risk committee requires that the assistant never produce investment recommendations and that all outputs be traceable to source transcript passages. Which design choice most directly enforces both constraints?
⚠ Common exam trap
The trap here is treating traceability and policy enforcement as model-tuning problems, when they are better solved through retrieval grounding and explicit generative instructions.
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
✓
Use retrieval-augmented generation over the transcripts and apply a system instruction that prohibits recommendations, returning cited source chunks.
Grounding responses in retrieved transcript passages with RAG gives analysts verifiable citations, while a system instruction establishes a hard behavioral boundary against investment recommendations. This combination enforces both the provenance and the policy constraint at generation time, rather than relying on post-processing or broad content filters that cannot distinguish summary from advice.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Store all prompts and responses in BigQuery and run a nightly batch job to flag outputs that look like recommendations.
Why it's wrong here
Post-hoc batch review detects violations only after analysts may have already seen the output, so it does not prevent the assistant from producing recommendations in the first place. It also does not provide passage-level traceability at the moment of generation. The risk committee asked for enforcement, not after-the-fact monitoring.
- ✗
Deploy the assistant with a content filter that blocks any output containing financial terminology.
Why it's wrong here
Blocking financial terminology would suppress the very content analysts need, since earnings discussions are inherently full of financial language. It also does not distinguish between factual summary and a recommendation, so it fails to enforce the policy precisely. This is an overbroad filter rather than a targeted control.
- ✓
Use retrieval-augmented generation over the transcripts and apply a system instruction that prohibits recommendations, returning cited source chunks.
Why this is correct
RAG grounds every response in retrieved transcript passages and can return citations to those passages, satisfying traceability. A system instruction that explicitly forbids investment recommendations constrains the model's behavior at generation time. Together they directly address both the no-recommendation policy and the requirement that outputs map back to source text.
- ✗
Fine-tune a Gemini model on historical earnings transcripts and deploy it with a low temperature setting.
Why it's wrong here
Fine-tuning on historical transcripts may improve domain fluency, and low temperature reduces randomness, but neither mechanism prevents the model from generating recommendations nor guarantees passage-level traceability. The risk committee's requirements are policy and provenance constraints, not stylistic ones, so tuning and temperature alone cannot enforce them reliably.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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