MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A team has deployed a real-time inference endpoint and wants to automatically scale based on CPU utilization. Which scaling policy type should they use with Application Auto Scaling for SageMaker endpoints?
⚠ Common exam trap
The trap is assuming that any scaling policy type works with SageMaker endpoints, when in fact only target tracking and step scaling are supported, and target tracking is the default recommendation for metric-based scaling.
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
✓
Target tracking scaling
Target tracking scaling is correct because it adjusts capacity to keep a specified metric, such as CPU utilization, at a target value. Application Auto Scaling for SageMaker endpoints supports target tracking, which automatically creates and manages the necessary CloudWatch alarms and scaling policies. This is the recommended approach for maintaining a utilization target without manually defining step adjustments.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Target tracking scaling
Why this is correct
Target tracking scaling adjusts capacity automatically to hold a chosen metric, such as CPU utilisation, at a specified target value. For SageMaker real-time endpoints, Application Auto Scaling creates the required CloudWatch alarms and scales instances in or out, directly satisfying the stem's requirement to scale on CPU utilisation without manually defining thresholds.
- ✗
Step scaling
Why it's wrong here
Step scaling adjusts capacity by defined increments when a CloudWatch alarm breaches thresholds; it does not track a target metric continuously. It is tempting for granular, alarm-driven responses, but CPU-based automatic scaling on SageMaker endpoints uses target tracking, which holds utilisation at a set value.
- ✗
Predictive scaling
Why it's wrong here
Predictive scaling forecasts future load from historical patterns, so it cannot react to the current CPU utilisation the team wants to track. It suits workloads with predictable, repeating demand cycles, where pre-emptive capacity avoids cold-start latency.
- ✗
Simple scaling
Why it's wrong here
Simple scaling applies a single adjustment with a cooldown, so it cannot track sustained CPU utilisation with the step adjustments target tracking provides. It suits basic, infrequent scaling where one fixed capacity change per alarm suffices.
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLA-C01 question from scratch — 665 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.