- A
The request payload size is too large.
Why wrong: Large payloads increase latency overall but are not specific to OverheadLatency.
- B
The SageMaker endpoint is not in the same VPC as the client.
Why wrong: Network latency would affect the overall latency but not specifically OverheadLatency.
- C
The endpoint is under-provisioned with insufficient instance count.
When the endpoint is under-provisioned, SageMaker overhead increases due to queuing and container startup, spiking OverheadLatency.
- D
The model inference code is inefficient.
Why wrong: Inefficient inference code would increase ModelLatency, not OverheadLatency.
Question 335 of 1,000
SageMaker OverheadLatency Spike Diagnosis
This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance and security. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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's SageMaker real-time endpoint is experiencing high latency under load. The CloudWatch metrics show that the ModelLatency is acceptable, but the OverheadLatency is spiking. What is the most likely cause?
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 endpoint is under-provisioned with insufficient instance count.
The correct answer is C because OverheadLatency measures the time spent on infrastructure overhead (e.g., request routing, network I/O, and container startup) rather than model inference. When the endpoint is under-provisioned with too few instances, requests queue up, causing the SageMaker front-end to wait for a free worker, which directly inflates OverheadLatency while ModelLatency (pure inference time) remains unaffected.
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 request payload size is too large.
Why it's wrong here
Large payloads increase latency overall but are not specific to OverheadLatency.
- ✗
The SageMaker endpoint is not in the same VPC as the client.
Why it's wrong here
Network latency would affect the overall latency but not specifically OverheadLatency.
- ✓
The endpoint is under-provisioned with insufficient instance count.
Why this is correct
When the endpoint is under-provisioned, SageMaker overhead increases due to queuing and container startup, spiking OverheadLatency.
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 inference code is inefficient.
Why it's wrong here
Inefficient inference code would increase ModelLatency, not OverheadLatency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse OverheadLatency with network latency or client-side delays, and assume VPC misconfiguration (Option B) is the cause, when in fact OverheadLatency is a server-side metric that spikes due to insufficient instance count causing request queuing.
Detailed technical explanation
How to think about this question
OverheadLatency in SageMaker is the time from when the endpoint receives a request until it is dispatched to the model container, plus the time after inference until the response is sent back; it includes queueing delays, serialization/deserialization overhead, and container management. Under-provisioning causes the auto-scaling group to lag behind traffic spikes, leading to request queuing in the load balancer or the SageMaker router, which manifests as elevated OverheadLatency even if each individual inference is fast. In practice, this metric is critical for diagnosing scaling issues because it isolates infrastructure bottlenecks from model performance problems.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
Got this wrong? Here's your next step.
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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: The endpoint is under-provisioned with insufficient instance count. — The correct answer is C because OverheadLatency measures the time spent on infrastructure overhead (e.g., request routing, network I/O, and container startup) rather than model inference. When the endpoint is under-provisioned with too few instances, requests queue up, causing the SageMaker front-end to wait for a free worker, which directly inflates OverheadLatency while ModelLatency (pure inference time) remains unaffected.
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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