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Architecting low-code ML solutions →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is an endpoint with autoscaling based on request count. This is correct because the configuration uses a target tracking metric—specifically, the number of invocations per instance—to dynamically adjust the number of compute instances. When the request count rises above the target threshold, the autoscaler adds instances; when it drops, it removes them, ensuring consistent performance without over-provisioning. On the Google Professional Machine Learning Engineer exam, this concept tests your understanding of how Vertex AI endpoints manage traffic spikes versus scheduled scaling, and a common trap is confusing request-based scaling with CPU or memory utilization metrics. Remember that request count scaling is ideal for unpredictable workloads, while resource-based scaling suits steady-state models. A quick memory tip: think "per instance invocations" as the heartbeat of request-driven autoscaling—if the heart beats faster, add more runners.

PMLE Architecting low-code ML solutions Practice Question

This PMLE practice question tests your understanding of architecting low-code ml solutions. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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

{
  "name": "projects/my-project/locations/us-central1/endpoints/my-endpoint",
  "displayName": "my-endpoint",
  "deployedModels": [
    {
      "model": "projects/my-project/locations/us-central1/models/12345",
      "displayName": "mymodel",
      "autoscalingMetricSpecs": [
        {
          "metricName": "aiplatform.googleapis.com/prediction/online/requests",
          "target": 100
        }
      ]
    }
  ],
  "trafficSplit": {
    "12345": 100
  }
}

Refer to the exhibit. What is being configured?

Question 1hardmultiple choice

Full question →

Exhibit

{
  "name": "projects/my-project/locations/us-central1/endpoints/my-endpoint",
  "displayName": "my-endpoint",
  "deployedModels": [
    {
      "model": "projects/my-project/locations/us-central1/models/12345",
      "displayName": "mymodel",
      "autoscalingMetricSpecs": [
        {
          "metricName": "aiplatform.googleapis.com/prediction/online/requests",
          "target": 100
        }
      ]
    }
  ],
  "trafficSplit": {
    "12345": 100
  }
}

A
A model training pipeline
Why wrong: Training pipelines are defined differently.
B
A batch prediction job
Why wrong: Batch prediction jobs have a different configuration.
C
An endpoint with autoscaling based on request count
The autoscaling metric is 'prediction/online/requests'.
D
An endpoint with autoscaling based on CPU utilization
Why wrong: The metric is based on requests, not CPU.

Full breakdown with real-world context →

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

✓

An endpoint with autoscaling based on request count

The exhibit shows the configuration of an Amazon SageMaker endpoint with a scaling policy that uses 'InvocationsPerInstance' as the target metric. This is the standard method for enabling autoscaling based on request count, where the scaling policy adjusts the number of instances to maintain a target number of invocations per instance. Option C is correct because the configuration explicitly sets the target tracking metric to 'SageMakerVariantInvocationsPerInstance', which triggers scaling based on request count.

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.

✗
A model training pipeline
Why it's wrong here
Training pipelines are defined differently.
✗
A batch prediction job
Why it's wrong here
Batch prediction jobs have a different configuration.
✓
An endpoint with autoscaling based on request count
Why this is correct
The autoscaling metric is 'prediction/online/requests'.
Related concept
Read the scenario before looking for a memorised answer.
✗
An endpoint with autoscaling based on CPU utilization
Why it's wrong here
The metric is based on requests, not CPU.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Google Cloud often tests the distinction between request-count-based and CPU-based autoscaling; the trap here is that candidates see 'autoscaling' and assume CPU utilization is the default metric, but the exhibit explicitly shows the invocation-based metric, making Option D a distractor for those who do not read the configuration details carefully.

Detailed technical explanation

How to think about this question

Under the hood, Amazon SageMaker endpoint autoscaling uses Application Auto Scaling with a target tracking policy that continuously adjusts the desired instance count based on the 'SageMakerVariantInvocationsPerInstance' metric emitted by CloudWatch. The scaling policy maintains the target value (e.g., 100 invocations per instance) by adding or removing instances, which is more responsive to traffic patterns than simple threshold-based scaling. In real-world scenarios, this prevents over-provisioning during low traffic and ensures latency stays low during spikes by keeping each instance's request load within the target range.

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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

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.

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FAQ

Questions learners often ask

What does this PMLE question test?

Architecting low-code ML solutions — This question tests Architecting low-code ML solutions — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: An endpoint with autoscaling based on request count — The exhibit shows the configuration of an Amazon SageMaker endpoint with a scaling policy that uses 'InvocationsPerInstance' as the target metric. This is the standard method for enabling autoscaling based on request count, where the scaling policy adjusts the number of instances to maintain a target number of invocations per instance. Option C is correct because the configuration explicitly sets the target tracking metric to 'SageMakerVariantInvocationsPerInstance', which triggers scaling based on request count.

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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Last reviewed: Jun 30, 2026

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