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Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions

Exhibit

Refer to the exhibit.

```
Model Evaluation Metrics:
- Accuracy: 0.92
- Precision: 0.88
- Recall: 0.95
- F1 Score: 0.91
- Latency (p95): 450ms
- Cost per 1K requests: $0.12

Business Requirements:
- Latency must be <500ms for p95
- Cost target: <$0.10 per 1K requests
- Accuracy must be >90%
```

A team has developed a generative AI model for real-time translation. The evaluation metrics and business requirements are shown. Which business decision is most appropriate given the trade-offs?

⚠ Common exam trap

Google Cloud often tests the ability to prioritize business constraints over model perfection, and the trap here is assuming that accuracy must be preserved at all costs, when in fact cost efficiency is the binding requirement and a small accuracy trade-off is acceptable.

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

✓

Optimize the model for cost efficiency, even if accuracy drops slightly to 90%.

The business requirements prioritize cost efficiency as the primary constraint, and the model currently exceeds the cost target. A slight accuracy drop to 90% (still within acceptable limits) allows cost to be reduced, aligning with the core business goal. The trade-off is acceptable since latency and other metrics remain within bounds, and accuracy at 90% still meets the minimum threshold for real-time translation quality.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Accept the model as-is because all other metrics are within limits.
Why it's wrong here
Cost exceeds the target by 20%.
✓
Optimize the model for cost efficiency, even if accuracy drops slightly to 90%.
Why this is correct
Cost is the only metric out of range; minor accuracy loss is acceptable.
✗
Prioritize latency reduction even if it increases cost.
Why it's wrong here
Latency is already acceptable; increasing cost worsens the problem.
✗
Reduce accuracy to 85% to achieve both latency and cost targets.
Why it's wrong here
This would violate the accuracy requirement of >90%.

Go deeper

Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions

Exhibit

Why each option matters

Option-by-option breakdown

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