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Generative AI Leader Adopt GenAI for contract analysis Practice Question
A company wants to adopt GenAI for contract analysis. They are evaluating build vs. buy. Which TWO factors are MOST important when deciding to build a custom fine-tuned model instead of using a pre-built API? (Choose 2)
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
✓
Desire to keep sensitive contract data within the company's VPC
Need for domain-specific terminology and data privacy requirements are key reasons to build. Cost and speed of deployment favor buy. Model accuracy can be high for both.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Need to deploy the solution within a week
Why it's wrong here
A one-week deployment timeline favours buying a pre-built API, since fine-tuning requires data curation, training, and evaluation cycles. It is tempting because speed is a legitimate selection criterion, but it points toward the opposite choice — a hosted model delivers immediate capability without training overhead.
- ✗
Low budget for AI development
Why it's wrong here
A low budget favours a pre-built API, as fine-tuning incurs compute, data-labelling, and MLOps costs. It is tempting because cost is a valid build-versus-buy factor, but constrained funding argues against the capital and operational expense of custom training, not for it.
- ✓
Desire to keep sensitive contract data within the company's VPC
Why this is correct
Keeping sensitive contract data inside the company's VPC requires a self-hosted or privately deployed model, since third-party pre-built APIs transmit prompts and documents to external endpoints. Building and fine-tuning in-house satisfies this data-residency and confidentiality constraint directly, which a vendor-hosted API cannot guarantee without separate private networking arrangements.
- ✗
Inability to evaluate model accuracy
Why it's wrong here
Inability to evaluate accuracy is a blocker for any deployment, not a driver toward building; you would still need evaluation regardless of build or buy. It is tempting because evaluation difficulty is a genuine risk in GenAI projects, but it argues against adoption entirely rather than favouring a custom fine-tuned model.
- ✓
Requirement for high accuracy on domain-specific legal terminology
Why this is correct
Fine-tuning adjusts a pre-trained model's weights on your own labelled contracts, embedding legal terminology and clause patterns that a general pre-built API cannot capture. This directly satisfies the stem's accuracy constraint on domain-specific legal language, where generic models misclassify or overlook specialised terms.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This Generative AI Leader practice question is part of Courseiva's free Google Cloud 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 Generative AI Leader exam.