Quick Answer
The answer is a target tracking scaling policy. This is the correct choice because it automatically adjusts the number of instances in your SageMaker endpoint based on a target metric like InvocationsPerInstance or ModelLatency, dynamically scaling resources up or down to maintain that target without manual intervention. For a model with a large memory footprint handling real-time predictions, this policy is ideal because it proactively adds capacity during traffic spikes while avoiding over-provisioning during lulls, ensuring consistent performance and cost efficiency. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this concept tests your understanding of automatic scaling for real-time inference endpoints, often appearing as a scenario where you must choose between simple, step, and target tracking policies. A common trap is selecting a step scaling policy, which requires predefined thresholds and can lag behind sudden bursts, whereas target tracking continuously adjusts based on a live metric. Memory tip: think “track the target, don’t step on the spikes.”
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. 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. 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 use SageMaker to serve real-time predictions with a model that has a large memory footprint. They need to ensure the endpoint can handle traffic spikes. Which scaling policy should they use?
Simple scaling policy
Why wrong: Simple scaling requires manual configuration of alarm thresholds.
Scheduled scaling policy
Why wrong: Scheduled scaling is for predictable traffic patterns, not spikes.
Target tracking policy
Target tracking automatically adjusts capacity to maintain a target metric value.
Step scaling policy
Why wrong: Step scaling is more complex but can be used; target tracking is simpler and recommended.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Target tracking policy
Target tracking scaling policy is the correct choice because it automatically adjusts the number of instances in the SageMaker endpoint based on a target metric, such as InvocationsPerInstance or ModelLatency, to handle traffic spikes without manual intervention. This policy is ideal for real-time inference with large memory models because it dynamically scales resources up or down to maintain the target metric, ensuring consistent performance during unpredictable traffic bursts.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Simple scaling policy
Why it's wrong here
Simple scaling requires manual configuration of alarm thresholds.
Scheduled scaling policy
Why it's wrong here
Scheduled scaling is for predictable traffic patterns, not spikes.
Target tracking policy
Why this is correct
Target tracking automatically adjusts capacity to maintain a target metric value.
Related concept
Read the scenario before looking for a memorised answer.
Step scaling policy
Why it's wrong here
Step scaling is more complex but can be used; target tracking is simpler and recommended.
Common exam traps
The trap here is that candidates often confuse step scaling with target tracking, assuming step scaling is more responsive for spikes, but target tracking is actually the recommended and simpler approach for handling unpredictable traffic in SageMaker real-time endpoints.
Detailed technical explanation
Target tracking scaling in SageMaker uses a built-in or custom CloudWatch metric (e.g., SageMakerVariantInvocationsPerInstance) and automatically creates the necessary CloudWatch alarms and scaling adjustments to keep the metric close to the target value. Under the hood, it applies a proportional-integral-derivative (PID) control algorithm to smooth out scaling actions, preventing oscillations that can occur with step scaling. In real-world scenarios, for a large memory model like a transformer with billions of parameters, target tracking ensures that the endpoint scales out quickly when invocations spike, while also scaling in during low traffic to minimize costs.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
The correct answer is: Target tracking policy — Target tracking scaling policy is the correct choice because it automatically adjusts the number of instances in the SageMaker endpoint based on a target metric, such as InvocationsPerInstance or ModelLatency, to handle traffic spikes without manual intervention. This policy is ideal for real-time inference with large memory models because it dynamically scales resources up or down to maintain the target metric, ensuring consistent performance during unpredictable traffic bursts.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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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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