- A
Create a SageMaker endpoint with an Application Auto Scaling target tracking policy based on the SageMakerVariantInvocationsPerInstance metric
Correct. This combination provides automatic scaling based on invocations per instance, which correlates with latency and handles spikes without manual intervention.
- B
Deploy the model on a multi-model endpoint and manually adjust the number of instances via the AWS Management Console
Why wrong: Wrong. Manual adjustment does not provide automatic scaling and cannot handle traffic spikes without intervention.
- C
Deploy the model on an Elastic Inference accelerator and use AWS Auto Scaling with a scheduled policy
Why wrong: Wrong. Elastic Inference accelerates model inference but does not scale; scheduled policy is not based on real-time latency.
- D
Create a batch transform job with a scheduled Lambda function to trigger scaling
Why wrong: Wrong. Batch transform job is for offline inference, not real-time, and scheduled Lambda does not scale based on latency.
SageMaker Auto Scaling on Request Latency with Target Tracking
This MLA-C01 practice question tests your understanding of sagemakervariantinvocationsperinstance. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. A key principle to apply: sageMakerVariantInvocationsPerInstance. 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 data science team has trained a model using SageMaker and wants to deploy it for real-time inference with automatic scaling based on request latency. The deployment must handle unpredictable traffic spikes without manual intervention. Which combination of SageMaker features should the team use?
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
Create a SageMaker endpoint with an Application Auto Scaling target tracking policy based on the SageMakerVariantInvocationsPerInstance metric
Option A is correct because it uses a SageMaker endpoint with an Application Auto Scaling target tracking policy based on the SageMakerVariantInvocationsPerInstance metric. While this metric measures invocations per instance rather than latency directly, it serves as a proxy: high invocations per instance often lead to increased latency, so scaling on this metric helps maintain low latency by distributing load. This approach handles unpredictable traffic spikes automatically, meeting the requirement for latency-aware scaling better than any other option, which lack automatic or latency-based scaling.
Key principle: SageMakerVariantInvocationsPerInstance
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a SageMaker endpoint with an Application Auto Scaling target tracking policy based on the SageMakerVariantInvocationsPerInstance metric
Why this is correct
Correct. This combination provides automatic scaling based on invocations per instance, which correlates with latency and handles spikes without manual intervention.
Related concept
SageMakerVariantInvocationsPerInstance
- ✗
Deploy the model on a multi-model endpoint and manually adjust the number of instances via the AWS Management Console
Why it's wrong here
Wrong. Manual adjustment does not provide automatic scaling and cannot handle traffic spikes without intervention.
- ✗
Deploy the model on an Elastic Inference accelerator and use AWS Auto Scaling with a scheduled policy
Why it's wrong here
Wrong. Elastic Inference accelerates model inference but does not scale; scheduled policy is not based on real-time latency.
- ✗
Create a batch transform job with a scheduled Lambda function to trigger scaling
Why it's wrong here
Wrong. Batch transform job is for offline inference, not real-time, and scheduled Lambda does not scale based on latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates may expect a metric that directly measures latency (e.g., SageMakerVariantLatency), but such a metric is not available as a built-in target tracking metric. The SageMakerVariantInvocationsPerInstance metric is the standard choice for latency scaling and is designed to maintain performance under variable load.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker integrates with Application Auto Scaling to adjust the number of instances in a production variant based on a target tracking policy. The SageMakerVariantInvocationsPerInstance metric is emitted by CloudWatch, and the policy works by continuously monitoring this metric and scaling out when the metric exceeds the target value (e.g., 1000 invocations per instance) or scaling in when it drops. In a real-world scenario, this is critical for e-commerce platforms where traffic spikes occur during flash sales, ensuring low-latency predictions without over-provisioning resources.
KKey Concepts to Remember
- SageMakerVariantInvocationsPerInstance
- Target Tracking Scaling
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
SageMakerVariantInvocationsPerInstance
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. SageMakerVariantInvocationsPerInstance 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.
Review sageMakerVariantInvocationsPerInstance, then practise related MLA-C01 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
SageMakerVariantInvocationsPerInstance
What is the correct answer to this question?
The correct answer is: Create a SageMaker endpoint with an Application Auto Scaling target tracking policy based on the SageMakerVariantInvocationsPerInstance metric — Option A is correct because it uses a SageMaker endpoint with an Application Auto Scaling target tracking policy based on the SageMakerVariantInvocationsPerInstance metric. While this metric measures invocations per instance rather than latency directly, it serves as a proxy: high invocations per instance often lead to increased latency, so scaling on this metric helps maintain low latency by distributing load. This approach handles unpredictable traffic spikes automatically, meeting the requirement for latency-aware scaling better than any other option, which lack automatic or latency-based scaling.
What should I do if I get this MLA-C01 question wrong?
Review sageMakerVariantInvocationsPerInstance, then practise related MLA-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
SageMakerVariantInvocationsPerInstance
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Last reviewed: Jun 24, 2026
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.
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