- 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.
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. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
Option A is correct because Amazon SageMaker Model Monitor can be configured to send notifications via Amazon SNS when monitoring violations are detected. This allows the team to proactively respond to data drift or quality issues without manually polling the monitoring results.
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 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
Read the scenario before looking for a memorised answer.
- ✗
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
Read the scenario before looking for a memorised answer.
- ✓
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
Read the scenario before looking for a memorised answer.
- ✗
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: answer the scenario, not the keyword
The trap here is that candidates assume monitoring must run daily (Option B) because of common best practices, but the exam tests that the schedule should be based on data arrival patterns, not a fixed minimum frequency.
Detailed technical explanation
How to think about this question
SageMaker Model Monitor uses a baseline statistics and constraints file generated from a training dataset to detect drift. The monitoring schedule is defined using a cron expression, and the job analyzes captured inference data against these constraints. If violations exceed a threshold, an SNS notification is triggered, enabling automated remediation workflows.
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: The monitoring job can be configured to send notifications via Amazon SNS. — Option A is correct because Amazon SageMaker Model Monitor can be configured to send notifications via Amazon SNS when monitoring violations are detected. This allows the team to proactively respond to data drift or quality issues without manually polling the monitoring results.
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
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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