hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: Your team manages a SageMaker real-time endpoint…
Your team manages a SageMaker real-time endpoint for a financial services application that requires low latency for fraud detection. The model is a 1 GB XGBoost model. The endpoint is deployed on two ml.m5.xlarge instances with target tracking auto-scaling based on average CPU utilization at 70%. During peak hours, the endpoint receives a sudden burst of traffic that increases from 500 requests per second to 2000 requests per second within 30 seconds. Many requests start failing with 503 errors. The CPU utilization metric shows that the instances are at 90% before the scaling policy launches new instances. However, by the time the new instances are added (approximately 3 minutes), the burst has subsided. You need to prevent these failures during future bursts while keeping costs reasonable. Which action would be MOST effective?
⚠ Common exam trap
AWS often tests the misconception that reducing the scaling metric threshold or changing scaling types (e.g., step scaling) alone can solve latency-related failures, when the real bottleneck is the time required for new instances to become fully operational (warm-up time).
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
✓
Reduce the target tracking scaling metric to 45% CPU utilization and set a warm-up time of 120 seconds.
Reducing the target tracking scaling metric to 45% CPU utilization triggers scaling actions earlier, before the burst pushes CPU to 90%. Setting a warm-up time of 120 seconds ensures new instances are fully initialized and ready to serve traffic, preventing the 503 errors caused by the 3-minute lag in instance availability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Reduce the target tracking scaling metric to 45% CPU utilization and set a warm-up time of 120 seconds.
Why this is correct
Lowering the threshold triggers scaling earlier, and warm-up ensures new instances are ready before receiving traffic.
- ✗
Change the scaling policy to step scaling with a lower cooldown (60 seconds) and add an alarm on invocation count.
Why it's wrong here
Step scaling with cooldown still has a delay before new instances launch; the total time to provision a new instance is still several minutes.
- ✗
Replace the two m5.xlarge instances with one m5.2xlarge instance and keep the same scaling policy.
Why it's wrong here
A single instance will have even higher per-instance load and cannot scale horizontally; the burst will still cause 503 errors.
- ✗
Implement scheduled scaling to add two instances 5 minutes before the expected peak hour.
Why it's wrong here
Bursts are unpredictable and not tied to a schedule; scheduled scaling would not catch the unexpected spike.
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JA
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.