Generative AI Leader Fundamentals of Generative AI Practice Question
A company is deploying a large language model (LLM) for customer support using Vertex AI. Which TWO best practices should they follow to ensure high-quality and cost-effective responses?
⚠ Common exam trap
Generative AI Leader often tests the misconception that infrastructure choices like Spot VMs or a single large model are cost-saving best practices, when the real cost levers are prompt optimization and model routing.
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 prompt optimization techniques to tailor responses
Option C is correct because prompt optimization techniques (such as prompt engineering, few-shot examples, and tuning) directly improve the relevance and quality of LLM responses while reducing token usage, which lowers cost per request. Option D is correct because Vertex AI Model Monitoring tracks input drift and response quality metrics, enabling the team to detect degradation in customer support answers and retrain or adjust prompts before quality and cost efficiency suffer. Option A is not appropriate because Spot VMs are preemptible and unsuitable for a production customer support LLM endpoint that requires high availability and low latency. Option B is not a best practice because storing prompts in plain text files lacks structured versioning, testing, and deployment controls; prompts should be managed with proper prompt management or CI/CD practices. Option E is incorrect because using a single large model for all query types increases cost and latency; routing simple queries to smaller, cheaper models while reserving the large model for complex cases is more cost-effective.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy the model on Spot VMs to reduce infrastructure costs
Why it's wrong here
Spot VMs can be preempted with little notice, interrupting inference and degrading customer support availability. They suit fault-tolerant batch workloads, not latency-sensitive serving; Vertex AI endpoints or autoscaling predictible resources meet the availability requirement instead.
- ✗
Store prompts in plain text files for easy version control
Why it's wrong here
Plain text files lack structured metadata, access controls and audit trails needed for governed prompt management. It is tempting because version control is familiar, but Vertex AI prompt management or a structured store provides the tracking and governance the scenario requires.
- ✓
Implement prompt optimization techniques to tailor responses
Why this is correct
Prompt optimisation refines instructions and context sent to the LLM, reducing token consumption and unnecessary retries. On Vertex AI this directly lowers inference cost while improving response relevance, satisfying the cost-effectiveness and quality constraints in the scenario.
- ✓
Use Vertex AI Model Monitoring to track input drift and response quality
Why this is correct
Model Monitoring continuously samples live traffic to detect input drift and degradation in response quality, triggering alerts when distributions shift from training baselines. This satisfies the stem's demand for sustained high-quality, cost-effective responses by catching quality decay early, before it escalates into rework or customer churn.
- ✗
Use a single large model for all query types to maintain consistency
Why it's wrong here
Routing every query to one large model inflates cost and latency for simple requests. It is tempting for consistency, but the best practise is model selection or routing, sending straightforward queries to smaller models and reserving the large model for complex ones.
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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 Google Cloud exam blueprint
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.