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
Larger models require more resources and increase cost.
- ✓
Change to a lower-cost machine type
Why this is correct
Using a smaller machine type reduces per-request compute cost.
- ✗
Deploy to multiple regions
Why it's wrong here
Multi-region deployment increases overall cost.
- ✗
Increase traffic split
Why it's wrong here
Traffic split distributes load but doesn't change per-request cost.
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