- A
Reduce the target tracking scaling metric to 45% CPU utilization and set a warm-up time of 120 seconds.
Lowering the threshold triggers scaling earlier, and warm-up ensures new instances are ready before receiving traffic.
- B
Change the scaling policy to step scaling with a lower cooldown (60 seconds) and add an alarm on invocation count.
Why wrong: Step scaling with cooldown still has a delay before new instances launch; the total time to provision a new instance is still several minutes.
- C
Replace the two m5.xlarge instances with one m5.2xlarge instance and keep the same scaling policy.
Why wrong: A single instance will have even higher per-instance load and cannot scale horizontally; the burst will still cause 503 errors.
- D
Implement scheduled scaling to add two instances 5 minutes before the expected peak hour.
Why wrong: Bursts are unpredictable and not tied to a schedule; scheduled scaling would not catch the unexpected spike.
Quick Answer
The answer is to reduce the target tracking scaling metric to 45% CPU utilization and set a warm-up time of 120 seconds. This works because lowering the threshold triggers the auto-scaling policy earlier, before the SageMaker real-time endpoint’s CPU utilization spikes to 90% during a burst, while the warm-up time ensures new instances are fully initialized and ready to serve traffic, eliminating the 3-minute lag that caused 503 errors. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this tests your understanding of how to tune SageMaker real-time endpoint auto-scaling for burst traffic, specifically the interplay between target tracking metrics and instance warm-up to maintain low latency. A common trap is assuming higher CPU thresholds or faster scaling policies alone will solve burst issues, but the real fix is proactive scaling with proper initialization. Memory tip: think “lower threshold, warmer start” to catch bursts before they burn.
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
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?
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.
Option A is correct because 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.
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.
- ✓
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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).
Detailed technical explanation
How to think about this question
SageMaker real-time endpoints use Amazon EBS-optimized instances, and the warm-up time parameter controls the period during which newly launched instances are considered 'InService' but not yet ready to receive traffic. By setting a warm-up time of 120 seconds, the scaling policy accounts for the time needed to download the 1 GB XGBoost model artifact from S3, load it into memory, and initialize the container, ensuring the instance is fully operational before traffic is routed to it. This is critical for large models where cold start latency can exceed the default health check grace period.
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
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Reduce the target tracking scaling metric to 45% CPU utilization and set a warm-up time of 120 seconds. — Option A is correct because 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.
What should I do if I get this MLA-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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Last reviewed: Jun 30, 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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