Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
Which TWO actions are recommended best practices for cost optimization when deploying generative AI models on Vertex AI?
⚠ Common exam trap
Google Cloud often tests the misconception that single-region deployment always reduces costs, when in reality it can increase network egress charges and latency penalties for global users, making multi-region strategies with traffic management more cost-effective.
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 batch prediction for non-real-time workloads
Batch prediction processes predictions asynchronously in large batches, which is significantly more cost-effective than online (real-time) prediction for workloads that do not require immediate responses. Vertex AI batch prediction jobs automatically scale down to zero when not in use, eliminating idle compute costs, and you only pay for the resources consumed during the job execution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use batch prediction for non-real-time workloads
Why this is correct
Batch prediction uses preemptible VMs, reducing cost.
- ✓
Set up autoscaling with a minimum number of replicas to avoid excessive scaling
Why this is correct
Autoscaling with appropriate min replicas prevents over-provisioning and reduces cost.
- ✗
Deploy the model in a single region to reduce network costs
Why it's wrong here
Single region may reduce network costs but is not a primary cost optimization strategy.
- ✗
Store all model prediction logs indefinitely for auditing
Why it's wrong here
Storing logs indefinitely increases storage costs; define retention policies.
- ✗
Always use GPU instances for inference
Why it's wrong here
GPU instances are more expensive; use CPU for latency-tolerant workloads.
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