The answer is enabling automatic batching on Vertex AI Prediction, along with setting min replicas to zero and enabling autoscaling, as these three strategies directly reduce costs without harming availability. Automatic batching works by grouping multiple inference requests into a single batch at the model server level, which dramatically increases throughput per instance and lowers the total compute resources required. On the Google Professional Machine Learning Engineer exam, this concept tests your understanding of Vertex AI’s built-in cost optimization features, often appearing in scenario-based questions where you must balance latency, cost, and availability. A common trap is assuming you must always keep a minimum number of replicas running, but with automatic batching and autoscaling, Vertex AI can scale down to zero when idle, then batch incoming requests efficiently during traffic spikes. Memory tip: think “batch and shrink” — batching boosts throughput, and scaling to zero shrinks idle costs.
PMLE Serving and scaling models Practice Question
This PMLE practice question tests your understanding of serving and scaling models. 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.
A company wants to reduce costs for serving a model on Vertex AI Prediction without sacrificing availability. Which THREE strategies should they consider?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable automatic batching to improve throughput per instance
Option C is correct because enabling automatic batching on Vertex AI Prediction allows the model server to group multiple inference requests into a single batch, which increases throughput per instance and reduces the total number of compute resources needed. This directly lowers serving costs without sacrificing availability, as the batching is handled transparently by the Vertex AI Prediction infrastructure.
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 larger machine types to reduce the number of replicas
Why it's wrong here
Larger machines are more expensive per hour.
✗
Switch to HTTP/2 to reduce network overhead
Why it's wrong here
HTTP/2 does not significantly reduce serving costs.
✓
Enable automatic batching to improve throughput per instance
Why this is correct
Batching increases efficiency, reducing number of instances needed.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use CPU instead of GPU for models that can run on CPU
Why this is correct
CPU instances are cheaper than GPU.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use min replicas=0 and enable autoscaling
Why this is correct
Scales down to zero when idle, saving cost.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that reducing replicas with larger machines is cost-effective, but the trap here is that larger machines increase per-unit cost and can lead to idle capacity, whereas autoscaling with min replicas=0 and batching optimizes cost without sacrificing availability.
Detailed technical explanation
How to think about this question
Automatic batching in Vertex AI Prediction works by collecting requests over a configurable timeout (e.g., 100ms) or until a batch size limit is reached, then sending them as a single batch to the model. This amortizes the per-request overhead (e.g., model loading, serialization) and improves GPU/TPU utilization, which is critical for cost-sensitive workloads. In practice, models with small input sizes (e.g., text classification) benefit most, while models with large inputs (e.g., image segmentation) may see diminishing returns.
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.
Serving and scaling models — This question tests Serving and scaling models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable automatic batching to improve throughput per instance — Option C is correct because enabling automatic batching on Vertex AI Prediction allows the model server to group multiple inference requests into a single batch, which increases throughput per instance and reduces the total number of compute resources needed. This directly lowers serving costs without sacrificing availability, as the batching is handled transparently by the Vertex AI Prediction infrastructure.
What should I do if I get this PMLE 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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