The answer is an Amazon SageMaker endpoint configured with Application Auto Scaling. This is the correct choice because SageMaker’s real-time endpoints natively integrate with Application Auto Scaling to adjust instance count based on a target metric like the number of incoming requests per instance, enabling automatic scaling for your TensorFlow model without additional orchestration. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of how to operationalize models for production traffic—a common trap is confusing SageMaker’s built-in scaling with third-party tools like AWS Auto Scaling Plans or Elastic Load Balancing, which are not required here. Remember that Application Auto Scaling is the direct service that manages the scaling policy for the endpoint variant. Memory tip: think “SageMaker + App Scaling = request-based auto scaling.”
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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 data scientist is using Amazon SageMaker to deploy a model for real-time inference. The model is a TensorFlow neural network. The scientist wants to use automatic scaling based on the number of incoming requests. Which service integration is required?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Amazon SageMaker endpoint configured with Application Auto Scaling
Amazon SageMaker endpoints natively integrate with Application Auto Scaling to adjust the number of instances based on a target metric, such as the number of incoming requests per instance. This allows the TensorFlow model to scale automatically in response to traffic, without needing additional orchestration services.
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.
✗
Amazon ECS with service auto scaling
Why it's wrong here
ECS is not the standard way to deploy SageMaker models.
✓
Amazon SageMaker endpoint configured with Application Auto Scaling
Why this is correct
SageMaker integrates with Application Auto Scaling to scale endpoints based on demand.
Related concept
Read the scenario before looking for a memorised answer.
✗
AWS Lambda with provisioned concurrency
Why it's wrong here
Lambda is not used to host SageMaker models directly.
✗
AWS Auto Scaling plans
Why it's wrong here
Auto Scaling plans are used for multiple resources but not specifically for SageMaker endpoints.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse SageMaker's built-in auto scaling with external services like ECS or Lambda, not realizing that SageMaker endpoints directly integrate with Application Auto Scaling for request-based scaling.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker uses Application Auto Scaling to register a scalable target (the endpoint variant) and apply a target tracking scaling policy based on the `SageMakerVariantInvocationsPerInstance` metric. This metric is emitted by CloudWatch every minute, and the scaling cooldown periods (300 seconds for scale-out, 600 seconds for scale-in) prevent rapid fluctuations. In a real-world scenario, if traffic spikes during a promotion, the endpoint can automatically add instances to maintain low latency, then scale down when demand subsides.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Amazon SageMaker endpoint configured with Application Auto Scaling — Amazon SageMaker endpoints natively integrate with Application Auto Scaling to adjust the number of instances based on a target metric, such as the number of incoming requests per instance. This allows the TensorFlow model to scale automatically in response to traffic, without needing additional orchestration services.
What should I do if I get this MLS-C01 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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This MLS-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 MLS-C01 exam.
Question Discussion
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