- A
Use a regression model to predict missing values based on other features
Model-based imputation can capture relationships and reduce bias under NMAR.
- B
Replace missing values with the median of the feature
Why wrong: Median imputation assumes MCAR, which is inappropriate for NMAR.
- C
Replace missing values with a constant, e.g., -1
Why wrong: A constant may distort the distribution and is not robust.
- D
Drop all rows with missing values
Why wrong: Dropping 15% of data may lose valuable information and cause selection bias.
Question 135 of 1,000
mediumMultiple ChoiceObjective-mapped
Missing Value Imputation for NMAR Data
This MLA-C01 practice question tests your understanding of needs to prepare data for a churn prediction model. 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.
An organization needs to prepare data for a churn prediction model. They observe missing values in 15% of the records for a numerical feature 'usage_minutes'. The data is not missing at random (NMAR). Which imputation strategy is MOST robust?
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
✓
Use a regression model to predict missing values based on other features
When data is NMAR, simple mean/median imputation can introduce bias. Using a model to predict missing values based on other features can account for the systematic missingness.
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.
- ✓
Use a regression model to predict missing values based on other features
Why this is correct
Model-based imputation can capture relationships and reduce bias under NMAR.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Replace missing values with the median of the feature
Why it's wrong here
Median imputation assumes MCAR, which is inappropriate for NMAR.
- ✗
Replace missing values with a constant, e.g., -1
Why it's wrong here
A constant may distort the distribution and is not robust.
- ✗
Drop all rows with missing values
Why it's wrong here
Dropping 15% of data may lose valuable information and cause selection bias.
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?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Use a regression model to predict missing values based on other features — When data is NMAR, simple mean/median imputation can introduce bias. Using a model to predict missing values based on other features can account for the systematic missingness.
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.
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 →
Same concept, more angles
1 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company uses AWS Glue ETL jobs to clean and transform data from S3 before training. The data contains a column with 40% missing values. The column is normally distributed. Which imputation strategy should the data engineer use?
medium- A.Impute with mode
- ✓ B.Impute with mean
- C.Impute with median
- D.Drop rows with missing values
Why B: For normally distributed data, imputing with the mean is a standard approach. Option B is correct. Option A (median) is robust but less efficient for normal data. Option C (mode) is for categorical data. Option D (drop) would lose 40% of data.
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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