- A
Prior probability shift
Why wrong: Prior probability shift is a specific case of concept drift where class proportions change.
- B
Concept drift
Concept drift is a change in the statistical properties of the target variable.
- C
Data drift
Why wrong: Data drift is a broad term; concept drift is more specific.
- D
Covariate shift
Why wrong: Covariate shift refers to changes in input features, not the target.
Question 729 of 1,000
MLA-C01 Concept drift Practice Question
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. A key principle to apply: concept drift. 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 engineer is troubleshooting a model that is producing unexpectedly low accuracy in production. The engineer examines the model's training data and finds that the distribution of the target variable in production is significantly different from the training set. What type of drift is the model experiencing?
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
Option B is correct because concept drift refers to any change in the statistical relationship between input features and the target variable, including changes in the target variable distribution. The scenario describes a change in the target variable distribution, which is a form of concept drift, specifically prior probability shift. Option A (Prior probability shift) is indeed a subtype of concept drift, but it is more specific; the question asks for the general drift type, making concept drift the best answer. Option C (Data drift) refers to changes in the distribution of input features, not the target. Option D (Covariate shift) is a form of data drift where the input distribution changes while the conditional distribution P(Y|X) remains unchanged.
Key principle: Concept drift
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Prior probability shift
Why it's wrong here
Prior probability shift is a specific case of concept drift where class proportions change.
- ✓
Concept drift
Why this is correct
Concept drift is a change in the statistical properties of the target variable.
Related concept
Concept drift
- ✗
Data drift
Why it's wrong here
Data drift is a broad term; concept drift is more specific.
- ✗
Covariate shift
Why it's wrong here
Covariate shift refers to changes in input features, not the target.
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
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Concept drift
- Prior probability shift
- Data drift
- Covariate shift
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
Concept drift
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. Concept drift 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 concept drift, then practise related MLA-C01 questions on the same topic to reinforce the concept.
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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 — Concept drift.
What is the correct answer to this question?
The correct answer is: Concept drift — Option B is correct because concept drift refers to any change in the statistical relationship between input features and the target variable, including changes in the target variable distribution. The scenario describes a change in the target variable distribution, which is a form of concept drift, specifically prior probability shift. Option A (Prior probability shift) is indeed a subtype of concept drift, but it is more specific; the question asks for the general drift type, making concept drift the best answer. Option C (Data drift) refers to changes in the distribution of input features, not the target. Option D (Covariate shift) is a form of data drift where the input distribution changes while the conditional distribution P(Y|X) remains unchanged.
What should I do if I get this MLA-C01 question wrong?
Review concept drift, then practise related MLA-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Concept drift
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Last reviewed: Jun 22, 2026
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