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MLA-C01 Practice Question: A machine learning engineer is configuring…
A machine learning engineer is configuring auto-scaling for a SageMaker real-time endpoint. The endpoint is expected to have steady traffic during business hours and low traffic at night. The engineer wants to minimize costs by scaling in during low traffic, but the model container has a long start-up time (about 5 minutes). Which scaling policy should the engineer use to prevent request drops during sudden traffic spikes?
⚠ Common exam trap
Many candidates choose a step scaling policy (Option A) because they think adding multiple instances at once handles spikes faster, but they overlook the critical need for a warm-up period to account for container start-up latency, which target tracking with warm-up explicitly addresses.
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
✓
Use a target tracking scaling policy based on average invocations per minute with a warm-up of 300 seconds.
Target tracking scaling policies in SageMaker automatically adjust capacity to maintain a target metric value, and the warm-up time of 300 seconds accounts for the 5-minute container start-up latency. This prevents request drops during sudden traffic spikes by ensuring new instances are fully initialized before they receive traffic, while still allowing the endpoint to scale in during low traffic to minimize costs.
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 a step scaling policy based on invocations per minute with a step that adds two instances at a time.
Why it's wrong here
Step scaling can be used but without warm-up, new instances may still receive traffic before being fully initialized. Warm-up is not a built-in parameter for step scaling.
- ✓
Use a target tracking scaling policy based on average invocations per minute with a warm-up of 300 seconds.
Why this is correct
Target tracking with a warm-up period ensures that newly launched instances are not included in the metric until they are ready, preventing traffic loss.
- ✗
Use a scheduled scaling action to add instances before business hours and remove them after.
Why it's wrong here
Scheduled scaling only handles predictable traffic; it does not react to sudden spikes outside the schedule.
- ✗
Use a simple scaling policy based on average CPU utilization with a cooldown period of 5 minutes.
Why it's wrong here
Simple scaling policies do not support warm-up; new instances may receive traffic before they are ready, causing errors.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.