- A
The monitoring job can be configured to send notifications via Amazon SNS.
SNS notifications can alert teams when violations are detected.
- B
The frequency of monitoring should be at least daily.
Why wrong: Monitoring frequency depends on data cadence and business needs; daily may be too frequent or not frequent enough.
- C
The monitoring job should analyze a sufficient sample size to be statistically significant.
Adequate sample size is critical for reliable drift detection.
- D
The monitoring job should run on a schedule that aligns with data arrival patterns.
Aligning the schedule with data arrival ensures timely detection of drift.
- E
The constraints file must be updated after each monitoring run.
Why wrong: Constraints are typically updated only when the baseline changes, not after every run.
Quick Answer
The answer is that the three factors to consider when configuring a SageMaker Model Monitor schedule are ensuring a sufficient sample size for statistical significance, aligning the monitoring job schedule with data arrival patterns, and setting up SNS notifications for alerts. This is correct because Model Monitor relies on statistical baselines to detect drift; without a representative sample size, the comparison becomes unreliable, and scheduling the job to match when new inference data actually arrives ensures drift is caught promptly rather than missed between runs. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding that monitoring is not a one-size-fits-all process—common traps include assuming higher frequency is always better (it depends on data volume) or that constraints update automatically after each run (they require manual or baseline job updates). A useful memory tip is "Sample, Schedule, SNS"—the three S's for a robust monitoring configuration.
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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 machine learning team is setting up Model Monitor for a deployed model. Which THREE factors should they consider when configuring the monitoring schedule? (Select three.)
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 monitoring job can be configured to send notifications via Amazon SNS.
Options B, D, and E are correct. B: Sufficient sample size ensures statistical significance. D: Schedule should align with data arrival patterns to detect drift promptly. E: SNS notifications can be set up for alerts. A is not necessarily correct; frequency depends on data volume. C is incorrect because constraints are updated manually or via baseline jobs, not automatically after each run.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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 monitoring job can be configured to send notifications via Amazon SNS.
Why this is correct
SNS notifications can alert teams when violations are detected.
Related concept
Static NAT maps one inside address to one outside address.
- ✗
The frequency of monitoring should be at least daily.
Why it's wrong here
Monitoring frequency depends on data cadence and business needs; daily may be too frequent or not frequent enough.
- ✓
The monitoring job should analyze a sufficient sample size to be statistically significant.
Why this is correct
Adequate sample size is critical for reliable drift detection.
Related concept
Static NAT maps one inside address to one outside address.
- ✓
The monitoring job should run on a schedule that aligns with data arrival patterns.
Why this is correct
Aligning the schedule with data arrival ensures timely detection of drift.
Related concept
Static NAT maps one inside address to one outside address.
- ✗
The constraints file must be updated after each monitoring run.
Why it's wrong here
Constraints are typically updated only when the baseline changes, not after every run.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. 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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLA-C01 NAT questions on configuration and troubleshooting.
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ML Solution Monitoring, Maintenance and Security — study guide chapter
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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 — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: The monitoring job can be configured to send notifications via Amazon SNS. — Options B, D, and E are correct. B: Sufficient sample size ensures statistical significance. D: Schedule should align with data arrival patterns to detect drift promptly. E: SNS notifications can be set up for alerts. A is not necessarily correct; frequency depends on data volume. C is incorrect because constraints are updated manually or via baseline jobs, not automatically after each run.
What should I do if I get this MLA-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLA-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 23, 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.
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