The answer is to change to a lower-cost machine type. This directly reduces inference cost by swapping the underlying compute instance—such as moving from a high-end GPU to a CPU or a smaller GPU—without modifying the model architecture or inference logic, a core principle of cloud cost optimization. On the Google Cloud Generative AI Leader exam, this scenario tests your understanding of Vertex AI deployment strategies, where balancing latency, throughput, and cost is critical; a common trap is assuming you must retrain or quantize the model first. Remember, when the business goal is purely to reduce inference cost, the fastest lever is the machine type, not the model itself. Memory tip: “Swap the chip, not the model” to recall that instance selection is the first cost-cutting move.
Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
This Generative AI Leader practice question tests your understanding of business strategies for generative ai solutions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Inference cost is primarily driven by the compute time and memory usage per request, which are directly tied to the machine type's vCPU, GPU, and RAM specifications. For example, switching from an NVIDIA A100 GPU to a T4 GPU can reduce cost per inference by up to 60% while still meeting latency SLAs for many transformer-based models, as long as batch sizes and model precision (e.g., FP16 vs FP32) are adjusted accordingly. Cloud providers like AWS, GCP, and Azure offer instance families (e.g., AWS Inferentia, Google TPU v5e) optimized for inference at lower cost per token.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What does this Generative AI Leader question test?
Business Strategies for Generative AI Solutions — This question tests Business Strategies for Generative AI Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Change to a lower-cost machine type — Option B is correct because 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.
What should I do if I get this Generative AI Leader question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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