- A
ModelQuality
Why wrong: ModelQuality is not a built-in CloudWatch metric; it requires custom metrics from Model Monitor.
- B
ModelLatency
Increased model latency can indicate performance degradation due to inefficient code or resource pressure.
- C
CpuUtilization
Why wrong: CPU utilization reflects instance health, not directly model performance degradation.
- D
Invocation5XXErrors
An increase in 5XX errors can indicate model failures or overload, signaling degradation.
- E
InvocationCount
Why wrong: Invocation count shows traffic volume, not model quality or health.
Question 239 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. 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 data scientist wants to monitor a deployed model for performance degradation. Which TWO metrics from Amazon CloudWatch should they use to detect issues? (Select two.)
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
✓
ModelLatency
Option B (ModelLatency) is correct because it measures the time taken for the model to respond to inference requests, and a sudden increase in latency can indicate performance degradation due to resource contention, model drift, or infrastructure issues. Option D (Invocation5XXErrors) is correct because a rise in 5XX HTTP errors from the SageMaker endpoint signals that the model is failing to process requests, often due to out-of-memory errors, timeouts, or internal faults, directly reflecting degraded service health.
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.
- ✗
ModelQuality
Why it's wrong here
ModelQuality is not a built-in CloudWatch metric; it requires custom metrics from Model Monitor.
- ✓
ModelLatency
Why this is correct
Increased model latency can indicate performance degradation due to inefficient code or resource pressure.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
CpuUtilization
Why it's wrong here
CPU utilization reflects instance health, not directly model performance degradation.
- ✓
Invocation5XXErrors
Why this is correct
An increase in 5XX errors can indicate model failures or overload, signaling degradation.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
InvocationCount
Why it's wrong here
Invocation count shows traffic volume, not model quality or health.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse CloudWatch metrics with SageMaker-specific monitoring features, assuming `ModelQuality` is a standard CloudWatch metric when it is actually a custom metric generated by SageMaker Model Monitor, not automatically available for all deployed models.
Trap categories for this question
Command / output trap
Invocation count shows traffic volume, not model quality or health.
Detailed technical explanation
How to think about this question
Under the hood, Amazon SageMaker endpoints expose `ModelLatency` as the sum of `ModelSetupTime` and `InvocationTime` in milliseconds, measured at the container level; a gradual increase may indicate model bloat or inefficient inference code. `Invocation5XXErrors` counts HTTP 500-series responses from the endpoint, which can be triggered by model crashes, memory exhaustion, or unhandled exceptions in the inference script—often a sign of concept drift causing out-of-distribution inputs that break preprocessing logic. In a real-world scenario, a retail demand forecasting model might see latency spikes during flash sales due to request queuing, while 5XX errors could spike if the model receives unexpected categorical values not seen in training.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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?
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: ModelLatency — Option B (ModelLatency) is correct because it measures the time taken for the model to respond to inference requests, and a sudden increase in latency can indicate performance degradation due to resource contention, model drift, or infrastructure issues. Option D (Invocation5XXErrors) is correct because a rise in 5XX HTTP errors from the SageMaker endpoint signals that the model is failing to process requests, often due to out-of-memory errors, timeouts, or internal faults, directly reflecting degraded service health.
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.
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Last reviewed: Jul 4, 2026
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