- A
Concept drift; ignore the change as long as input distribution remains stable
Why wrong: Concept drift degrades model performance and must be addressed, not ignored.
- B
Data drift; update the baseline statistics and continue monitoring
Why wrong: Updating baseline does not address concept drift; the model itself needs retraining.
- C
Concept drift; retrain the model with newly collected labeled data
Concept drift is a change in P(y|x). Retraining with recent labeled data adjusts the model to the new relationship.
- D
Data drift; retrain the model with the latest training data
Why wrong: Data drift refers to change in input distribution P(x), which is not the case here.
Question 987 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. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 with SageMaker and wants to monitor for concept drift. They have noticed that the relationship between input features and the target variable has changed, causing model accuracy to degrade. However, the input data distribution remains stable. Which type of drift is this, and what is the most appropriate response strategy?
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
✓
Concept drift; retrain the model with newly collected labeled data
This is concept drift because the relationship between input features and the target variable has changed while the input data distribution remains stable. The most appropriate response is to retrain the model with newly collected labeled data that reflects the current relationship, as concept drift requires updating the model's learned mapping from features to labels.
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.
- ✗
Concept drift; ignore the change as long as input distribution remains stable
Why it's wrong here
Concept drift degrades model performance and must be addressed, not ignored.
- ✗
Data drift; update the baseline statistics and continue monitoring
Why it's wrong here
Updating baseline does not address concept drift; the model itself needs retraining.
- ✓
Concept drift; retrain the model with newly collected labeled data
Why this is correct
Concept drift is a change in P(y|x). Retraining with recent labeled data adjusts the model to the new relationship.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Data drift; retrain the model with the latest training data
Why it's wrong here
Data drift refers to change in input distribution P(x), which is not the case here.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse concept drift with data drift, assuming any drift requires updating baseline statistics, when in fact concept drift demands retraining with fresh labeled data to realign the model with the new feature-target relationship.
Detailed technical explanation
How to think about this question
Concept drift occurs when the conditional distribution P(Y|X) changes while P(X) remains constant, often due to evolving real-world phenomena such as changing customer preferences or economic conditions. In SageMaker, you can monitor for concept drift using SageMaker Model Monitor's ground truth ingestion and quality checks, but it does not automatically detect concept drift—you must compare predictions against actual labels over time. A real-world example is a credit scoring model where borrower behavior shifts after a regulatory change, causing the same input features to map to different default probabilities.
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.
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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: Concept drift; retrain the model with newly collected labeled data — This is concept drift because the relationship between input features and the target variable has changed while the input data distribution remains stable. The most appropriate response is to retrain the model with newly collected labeled data that reflects the current relationship, as concept drift requires updating the model's learned mapping from features to labels.
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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