- A
The inference script is not using batch processing.
Why wrong: Batch processing is not applicable for real-time endpoints.
- B
The SageMaker endpoint auto scaling is not configured to scale out quickly enough under increasing traffic.
If auto scaling policies are too conservative, the endpoint may not add instances fast enough during traffic spikes, leading to increased latency.
- C
The model size is too large for the instance type.
Why wrong: The model size hasn't changed, so this cannot explain the gradual increase.
- D
The endpoint has data capture enabled, causing additional overhead.
Why wrong: Data capture overhead is constant and does not increase over time.
Question 672 of 1,000
easyMultiple ChoiceObjective-mapped
Diagnosing Gradual Inference Latency Increase: Auto Scaling Issues
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 deployed a machine learning model on an Amazon SageMaker real-time endpoint. Over several weeks, they notice that inference latency has been gradually increasing, especially during peak business hours. The model and instance type have remained unchanged. What is the most likely cause of the increased latency?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
The SageMaker endpoint auto scaling is not configured to scale out quickly enough under increasing traffic.
The gradual increase in latency during peak hours, with no change to the model or instance type, strongly indicates that the endpoint is not scaling out fast enough to handle increased traffic. SageMaker real-time endpoints rely on auto scaling policies to add instances based on metrics like invocation count or CPU utilization; if the scale-out step is too slow or the cooldown period is too long, requests queue up and latency rises. This matches the symptom of latency growing over weeks as traffic patterns evolve, rather than a sudden spike.
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.
- ✗
The inference script is not using batch processing.
Why it's wrong here
Batch processing is not applicable for real-time endpoints.
- ✓
The SageMaker endpoint auto scaling is not configured to scale out quickly enough under increasing traffic.
Why this is correct
If auto scaling policies are too conservative, the endpoint may not add instances fast enough during traffic spikes, leading to increased latency.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The model size is too large for the instance type.
Why it's wrong here
The model size hasn't changed, so this cannot explain the gradual increase.
- ✗
The endpoint has data capture enabled, causing additional overhead.
Why it's wrong here
Data capture overhead is constant and does not increase over time.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse a gradual latency increase with a model size or code issue, but the key clue is the unchanged model and instance type, pointing to a scaling configuration problem rather than a static resource limitation.
Detailed technical explanation
How to think about this question
SageMaker auto scaling uses the Application Auto Scaling service with target tracking policies based on metrics like SageMakerVariantInvocationsPerInstance. The scale-out cooldown period (default 300 seconds) can delay adding new instances, causing latency to climb as traffic ramps up. In a real-world scenario, if the endpoint's initial instance count is too low and the scale-out step adjustment is set to add only one instance at a time, the endpoint can become overwhelmed during peak hours, leading to the observed gradual latency increase.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: The SageMaker endpoint auto scaling is not configured to scale out quickly enough under increasing traffic. — The gradual increase in latency during peak hours, with no change to the model or instance type, strongly indicates that the endpoint is not scaling out fast enough to handle increased traffic. SageMaker real-time endpoints rely on auto scaling policies to add instances based on metrics like invocation count or CPU utilization; if the scale-out step is too slow or the cooldown period is too long, requests queue up and latency rises. This matches the symptom of latency growing over weeks as traffic patterns evolve, rather than a sudden spike.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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