Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
Exhibit
Refer to the exhibit. ``` $ gcloud ai endpoints deploy-model \ --endpoint=projects/123/locations/us-central1/endpoints/456 \ --model=projects/123/locations/us-central1/models/789 \ --machine-type=n1-highmem-2 \ --traffic-split=0=100 Deployed model: projects/123/locations/us-central1/endpoints/456/deployedModels/789 Machine type: n1-highmem-2 Traffic split: 100% ```
An ML engineer sees the above deployment output. The business wants to reduce inference cost. Which action should they take?
⚠ Common exam trap
Google Cloud often tests the misconception that 'more resources' (larger model, more regions) always improves performance, but here the business goal is cost reduction, so the correct action is to downsize infrastructure while maintaining acceptable quality.
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
✓
Change to a lower-cost machine type
Switching to a lower-cost machine type directly reduces the per-request compute cost without altering the model architecture or inference logic. This is a common cost-optimization strategy in cloud-based ML deployments, where instance types (e.g., from GPU to CPU or from a larger to a smaller GPU) can be selected based on latency and throughput requirements, provided the model fits within the machine's memory and compute constraints.
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 a larger model
Why it's wrong here
A larger model consumes more compute per inference, raising cost rather than reducing it. It is tempting because larger models often improve accuracy, and would be correct when the requirement is higher output quality or reasoning capability rather than cost reduction.
- ✓
Change to a lower-cost machine type
Why this is correct
Inference cost scales with the compute SKU hosting the deployed model. Selecting a lower-cost machine type reduces the hourly rate charged for the endpoint while preserving the same model, directly satisfying the business goal of lowering inference cost.
- ✗
Deploy to multiple regions
Why it's wrong here
Multi-region deployment adds duplicate endpoints and cross-region data transfer charges, increasing cost without reducing per-inference compute. It is tempting because regional distribution improves latency and availability, and would be correct when the requirement is resilience or proximity to geographically dispersed users.
- ✗
Increase traffic split
Why it's wrong here
Traffic split distributes requests between deployment variants; raising it shifts volume but does not lower the per-token compute cost of the model serving those requests. It is tempting because it governs live deployment behaviour, and would be correct when the goal is canary testing or gradually promoting a new model version.
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