- A
Replace the model with a more complex algorithm such as a gradient-boosted tree.
Why wrong: Algorithm change is a premature solution; the model's performance drop is likely due to data changes, not model capacity.
- B
Retrain the model using the most recent 30 days of transaction data with automated retraining pipelines.
Why wrong: Retraining without understanding the cause may still use incorrect labels or miss a shift in the label distribution; it's better to first investigate ground truth.
- C
Increase the data capture sampling percentage from 10% to 100% for more detailed analysis.
Why wrong: Increasing data capture will provide more data but does not address the need for ground truth labels to diagnose the degradation.
- D
Investigate recent ground truth labels to check for label drift or changes in the fraud definition.
Label drift occurs when the underlying relationship between features and labels changes. Collecting and analyzing recent labels can confirm if the fraud criteria have shifted.
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 financial services company operates a real-time inference endpoint for a fraud detection model on Amazon SageMaker. The model was trained on historical transaction data from 2023. Over the past month, the model's precision has dropped from 92% to 78%, while recall remains high at 95%. The data science team suspects data drift and has already enabled SageMaker Model Monitor with data capture and a baseline from the training data. The latest monitoring report indicates no statistically significant drift in any of the input features. The team also verified that the inference code and model artifact have not changed. Despite the stable feature distributions, the model is misclassifying an increasing number of legitimate transactions as fraudulent (false positives). The business is concerned about the impact on customer experience. What is the best course of action?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
Investigate recent ground truth labels to check for label drift or changes in the fraud definition.
The scenario describes a drop in precision without feature drift, which indicates label drift – the relationship between features and labels has changed. The most effective next step is to collect and analyze recent ground truth labels to confirm label drift. Retraining on recent data without addressing the root cause may not help if the new labels are also stale or incorrect. Increasing data capture rate will not diagnose the issue. Changing the algorithm is unlikely to help without understanding the cause.
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.
- ✗
Replace the model with a more complex algorithm such as a gradient-boosted tree.
Why it's wrong here
Algorithm change is a premature solution; the model's performance drop is likely due to data changes, not model capacity.
- ✗
Retrain the model using the most recent 30 days of transaction data with automated retraining pipelines.
Why it's wrong here
Retraining without understanding the cause may still use incorrect labels or miss a shift in the label distribution; it's better to first investigate ground truth.
- ✗
Increase the data capture sampling percentage from 10% to 100% for more detailed analysis.
Why it's wrong here
Increasing data capture will provide more data but does not address the need for ground truth labels to diagnose the degradation.
- ✓
Investigate recent ground truth labels to check for label drift or changes in the fraud definition.
Why this is correct
Label drift occurs when the underlying relationship between features and labels changes. Collecting and analyzing recent labels can confirm if the fraud criteria have shifted.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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: Investigate recent ground truth labels to check for label drift or changes in the fraud definition. — The scenario describes a drop in precision without feature drift, which indicates label drift – the relationship between features and labels has changed. The most effective next step is to collect and analyze recent ground truth labels to confirm label drift. Retraining on recent data without addressing the root cause may not help if the new labels are also stale or incorrect. Increasing data capture rate will not diagnose the issue. Changing the algorithm is unlikely to help without understanding the cause.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jun 24, 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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