hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A company runs a real-time inference endpoint…
A company runs a real-time inference endpoint with an auto-scaling policy based on average CPU utilization. During a traffic spike, the endpoint scales out but takes several minutes to become healthy, causing increased latency. The endpoint uses a large instance type. Which change would MOST effectively reduce the time to scale out?
⚠ Common exam trap
Many candidates confuse scaling policies (like target tracking) with deployment strategies (like canary or blue/green), or assume that reducing instance size or cooldown periods will solve initialization delays, when the core issue is the cold-start time of large instances.
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 pre-warmed endpoint with a target tracking scaling policy.
A pre-warmed endpoint with a target tracking scaling policy ensures that a baseline number of instances are always ready to handle traffic, eliminating the cold-start delay during scale-out. The target tracking policy dynamically adjusts the number of instances to maintain a target average CPU utilization, which reduces the time to scale out by avoiding the need to provision and initialize new instances from scratch during a spike.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to a smaller instance type.
Why it's wrong here
Smaller instances may scale faster but could be underpowered for the workload.
- ✓
Use a pre-warmed endpoint with a target tracking scaling policy.
Why this is correct
Correct. Pre-warmed endpoints keep a minimum number of instances ready, and target tracking proactively scales based on metrics.
- ✗
Enable SageMaker Inference Recommender to optimize instance type.
Why it's wrong here
Inference Recommender helps choose instance type but does not directly reduce scaling time.
- ✗
Implement a canary deployment with a blue/green strategy.
Why it's wrong here
Canary deployments are for updating models, not scaling latency.
- ✗
Set a lower scaling cooldown period.
Why it's wrong here
Lower cooldown can help scale in faster but does not reduce the time for new instances to become healthy.
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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.