MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A company deploys a real-time inference endpoint with auto-scaling using a target tracking policy based on average Invocations per instance. They notice that during a traffic spike, the endpoint scales out too late, causing increased latency. They want to scale proactively before the spike. Which strategy should they implement?
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 scheduled scaling action to add capacity before the expected spike
Scheduled scaling with Application Auto Scaling can anticipate traffic spikes. Pre-warming endpoints or using provisioned concurrency are not native SageMaker features. Step scaling reacts to deviations but still after the fact.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable provisioned concurrency on the endpoint
Why it's wrong here
Provisioned concurrency is a Lambda feature, not available for SageMaker endpoints.
- ✗
Pre-warm the endpoint by sending dummy requests
Why it's wrong here
Pre-warming is not a supported SageMaker feature; dummy requests would incur cost and may not trigger auto-scaling effectively.
- ✓
Use a scheduled scaling action to add capacity before the expected spike
Why this is correct
Scheduled scaling can increase the desired capacity in advance of known traffic patterns, reducing latency during the spike.
- ✗
Switch to a step scaling policy with a higher cooldown period
Why it's wrong here
Step scaling still reacts to metric changes; it may not be fast enough for sudden spikes and a higher cooldown could delay further scaling.
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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.