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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
Adding only two instances per step cannot absorb a sudden spike while each new container takes five minutes to start, so requests drop. Step scaling suits gradual load growth where capacity increments track demand, not sharp bursts needing pre-warmed capacity.
- ✓
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 on average invocations per minute reacts to demand, while the 300-second warm-up keeps newly added instances out of the metric until the slow-starting container is ready, preventing premature scale-in and request drops during sudden spikes.
- ✗
Use a scheduled scaling action to add instances before business hours and remove them after.
Why it's wrong here
Scheduled actions follow the business-hours pattern but cannot react to unexpected spikes within the day, so requests drop until the next schedule. Scheduled scaling is correct for predictable, repeating traffic cycles, not for sudden surges requiring responsive capacity.
- ✗
Use a simple scaling policy based on average CPU utilization with a cooldown period of 5 minutes.
Why it's wrong here
A five-minute cooldown matches the container start-up, so new instances cannot serve traffic before the next scaling evaluation, dropping requests during spikes. Simple CPU scaling suits steady workloads without long cold starts, not bursty traffic with slow-booting containers.
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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.