- A
Feature attribution drift
Why wrong: Feature attribution drift monitors SHAP values; while it can indicate changes, it is often used for concept drift detection with labels.
- B
Model quality drift
Why wrong: Model quality drift requires ground truth labels to compute metrics like accuracy or F1-score.
- C
Data drift
Data drift (input distribution change) can be monitored without labels; significant data drift may indicate potential concept drift.
- D
Concept drift
Why wrong: Concept drift detection typically requires ground truth labels to compare prediction accuracy over time; without labels, it is difficult to measure.
Question 406 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. 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 company deploys a model for fraud detection. They want to monitor if the model's predictions become less accurate over time due to changes in the underlying data distribution, but they do not have immediate access to ground truth labels. Which type of drift should they monitor as a proxy?
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
✓
Data drift
Data drift (option C) is the correct proxy to monitor when ground truth labels are unavailable because it detects changes in the input feature distribution over time. If the underlying data distribution shifts, the model's predictions are likely to become less accurate even if the relationship between features and labels remains stable. This allows teams to trigger retraining or investigation before model quality degrades.
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.
- ✗
Feature attribution drift
Why it's wrong here
Feature attribution drift monitors SHAP values; while it can indicate changes, it is often used for concept drift detection with labels.
- ✗
Model quality drift
Why it's wrong here
Model quality drift requires ground truth labels to compute metrics like accuracy or F1-score.
- ✓
Data drift
Why this is correct
Data drift (input distribution change) can be monitored without labels; significant data drift may indicate potential concept drift.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Concept drift
Why it's wrong here
Concept drift detection typically requires ground truth labels to compare prediction accuracy over time; without labels, it is difficult to measure.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between data drift and concept drift, and the trap here is that candidates confuse 'changes in data distribution' (data drift) with 'changes in the relationship between features and labels' (concept drift), assuming both require labels when only concept drift does.
Detailed technical explanation
How to think about this question
Data drift is typically quantified using statistical tests such as the Kolmogorov-Smirnov test for continuous features or the Chi-squared test for categorical features, comparing the training data distribution to recent inference data. In real-world fraud detection, a sudden spike in transaction amounts from a new demographic could cause data drift, leading to increased false negatives even if the fraud pattern itself hasn't changed. Monitoring data drift as a proxy allows teams to set alert thresholds (e.g., p-value < 0.05) and trigger automated retraining pipelines without waiting for label feedback.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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: Data drift — Data drift (option C) is the correct proxy to monitor when ground truth labels are unavailable because it detects changes in the input feature distribution over time. If the underlying data distribution shifts, the model's predictions are likely to become less accurate even if the relationship between features and labels remains stable. This allows teams to trigger retraining or investigation before model quality degrades.
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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