- A
Step scaling based on CPU utilization
Why wrong: Step scaling is also reactive; for predictable patterns, scheduled scaling is more efficient.
- B
Manual scaling by the operations team
Why wrong: Manual scaling is not automated and may be error-prone.
- C
Scheduled scaling that increases instances before business hours and decreases after
Scheduled scaling proactively adjusts capacity, minimizing idle instances during low traffic.
- D
Target tracking with a custom metric for response time
Why wrong: Target tracking is reactive and may not align with predictable schedules as efficiently as scheduled scaling.
Question 552 of 1,000
MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance, and security. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
A company wants to reduce costs for a real-time inference endpoint that experiences predictable traffic spikes during business hours and low traffic at night. Which auto-scaling policy is MOST cost-effective while maintaining performance?
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
✓
Scheduled scaling that increases instances before business hours and decreases after
Option C is correct because scheduled scaling directly aligns capacity with the predictable traffic pattern (business hours vs. night), allowing you to proactively add instances before demand increases and remove them afterward. This avoids the cost of over-provisioning during low-traffic periods and the latency of reactive scaling, making it the most cost-effective approach for a known, recurring schedule.
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.
- ✗
Step scaling based on CPU utilization
Why it's wrong here
Step scaling is also reactive; for predictable patterns, scheduled scaling is more efficient.
- ✗
Manual scaling by the operations team
Why it's wrong here
Manual scaling is not automated and may be error-prone.
- ✓
Scheduled scaling that increases instances before business hours and decreases after
Why this is correct
Scheduled scaling proactively adjusts capacity, minimizing idle instances during low traffic.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Target tracking with a custom metric for response time
Why it's wrong here
Target tracking is reactive and may not align with predictable schedules as efficiently as scheduled scaling.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose reactive scaling options (like step scaling or target tracking) because they seem 'automated,' but they fail to recognize that for predictable, time-based traffic patterns, scheduled scaling is both more cost-effective and more performant than any reactive policy.
Detailed technical explanation
How to think about this question
Scheduled scaling in Amazon SageMaker endpoints uses CloudWatch Events (or EventBridge Scheduler) to invoke the UpdateEndpoint API at specified times, adjusting the InitialInstanceCount or the variant's desired instance count. This approach avoids the latency of metric-based auto-scaling (which requires at least 1-2 minutes of data collection and cooldown periods) and ensures zero idle instance cost during off-peak hours. A real-world scenario is a financial services company with a trading dashboard that sees heavy use from 9 AM to 5 PM EST—scheduled scaling can reduce the endpoint to a single instance at night, saving up to 70% on compute costs compared to always running peak capacity.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
ML Solution Monitoring, Maintenance, and Security — study guide chapter
Learn the concepts, then practise the questions
- →
ML Solution Monitoring, Maintenance, and Security practice questions
Targeted practice on this topic area only
- →
All MLA-C01 questions
1,000 questions across all exam domains
- →
AWS Certified Machine Learning Engineer Associate MLA-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLA-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
Deployment and Orchestration of ML Workflows practice questions
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
ML Solution Monitoring, Maintenance, and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance, and Security.
ML Solution Monitoring, Maintenance and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
MLA-C01 fundamentals practice questions
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
MLA-C01 scenario practice questions
Practise MLA-C01 questions linked to MLA-C01 scenario.
MLA-C01 troubleshooting practice questions
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Start a free MLA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this MLA-C01 question test?
ML Solution Monitoring, Maintenance, and Security — This question tests ML Solution Monitoring, Maintenance, and Security — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Scheduled scaling that increases instances before business hours and decreases after — Option C is correct because scheduled scaling directly aligns capacity with the predictable traffic pattern (business hours vs. night), allowing you to proactively add instances before demand increases and remove them afterward. This avoids the cost of over-provisioning during low-traffic periods and the latency of reactive scaling, making it the most cost-effective approach for a known, recurring schedule.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.