The answer is a combination of scheduled scaling for predictable peaks and simple scaling for additional bursts. This hybrid approach is most cost-effective for spiky traffic because scheduled scaling pre-provisions capacity for known high-load periods, while simple scaling (based on a target metric like invocation count or latency) dynamically adds instances to absorb unexpected 10x bursts, ensuring availability without the waste of always-on large instances. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of SageMaker’s automatic scaling options and the trade-off between proactive and reactive policies—a common trap is choosing only simple scaling, which can lag behind sudden spikes, or only scheduled scaling, which misses unpredictable surges. Remember the memory tip: “Schedule the known, scale the unknown” to pair predictable timing with reactive capacity.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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 is deploying a model for real-time inference with SageMaker. The endpoint receives spiky traffic, with occasional bursts of 10x normal load. Which scaling policy is MOST cost-effective while maintaining availability?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a combination of scheduled scaling for predictable peaks and simple scaling for additional bursts.
Option C is correct because it combines scheduled scaling for predictable traffic patterns (e.g., known peak hours) with simple scaling to handle unexpected bursts, ensuring availability during 10x load spikes without over-provisioning. This hybrid approach is more cost-effective than always-on large instances, as it dynamically adjusts capacity only when needed, aligning with SageMaker's automatic scaling capabilities.
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.
✗
Provision a large instance type that can handle the peak load at all times.
Why it's wrong here
This is wasteful during low traffic periods.
✗
Manually scale the endpoint based on historical traffic patterns.
Why it's wrong here
Manual scaling is not automated and may miss spikes.
✓
Use a combination of scheduled scaling for predictable peaks and simple scaling for additional bursts.
Why this is correct
Scheduled scaling handles known patterns, while simple scaling provides reactive capacity for bursts.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a target tracking scaling policy based on average latency.
Why it's wrong here
Target tracking may not respond quickly enough to sudden bursts.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume target tracking (Option D) is always optimal for cost, but it fails for spiky traffic because it reacts to post-burst metrics like latency, not preemptively scaling for sudden load changes.
Detailed technical explanation
How to think about this question
SageMaker's automatic scaling uses CloudWatch metrics (e.g., InvocationsPerInstance) to trigger scaling actions; scheduled scaling pre-warms instances via cron expressions, while simple scaling uses step adjustments based on threshold breaches. Under the hood, scaling cooldown periods (default 300 seconds) prevent rapid oscillations, but during extreme bursts, a combination of scheduled and simple scaling ensures capacity is added before latency degrades, leveraging the 'warm-up' mechanism for new instances.
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a combination of scheduled scaling for predictable peaks and simple scaling for additional bursts. — Option C is correct because it combines scheduled scaling for predictable traffic patterns (e.g., known peak hours) with simple scaling to handle unexpected bursts, ensuring availability during 10x load spikes without over-provisioning. This hybrid approach is more cost-effective than always-on large instances, as it dynamically adjusts capacity only when needed, aligning with SageMaker's automatic scaling capabilities.
What should I do if I get this MLS-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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This MLS-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 MLS-C01 exam.
Question Discussion
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