This MLA-C01 practice question tests your understanding of data preparation for machine learning. 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 data scientist is working with a dataset that contains missing values in several numeric features. The data scientist wants to impute the missing values with the median of each feature. Which Amazon SageMaker Data Wrangler transformation should be used?
Replace missing with constant
Why wrong: Constant replacement does not use median.
Custom transform with Python
Why wrong: Possible but not the native median imputation method.
Drop missing rows
Why wrong: Drop missing rows removes data, not imputation.
Handle missing values (with median strategy)
This transform allows imputation with median.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Handle missing values (with median strategy)
Option D is correct because Amazon SageMaker Data Wrangler includes a built-in 'Handle missing values' transformation that supports imputation with the median strategy. This directly matches the requirement to replace missing numeric values with the median of each feature without writing custom code.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Replace missing with constant
Why it's wrong here
Constant replacement does not use median.
Custom transform with Python
Why it's wrong here
Possible but not the native median imputation method.
Drop missing rows
Why it's wrong here
Drop missing rows removes data, not imputation.
Handle missing values (with median strategy)
Why this is correct
This transform allows imputation with median.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates may confuse the 'Replace missing with constant' option (which uses a fixed value) with the median strategy, or they may overcomplicate the solution by choosing a custom Python transform when a built-in option exists.
Detailed technical explanation
Under the hood, the 'Handle missing values' transformation in SageMaker Data Wrangler computes the median for each numeric column from the non-missing values and then fills the missing entries with that computed median. This is a robust imputation method for skewed distributions, as the median is less sensitive to outliers than the mean. In real-world scenarios, this approach is preferred when the data contains outliers that would distort the mean, ensuring the imputed values better represent the central tendency.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
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FAQ
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
The correct answer is: Handle missing values (with median strategy) — Option D is correct because Amazon SageMaker Data Wrangler includes a built-in 'Handle missing values' transformation that supports imputation with the median strategy. This directly matches the requirement to replace missing numeric values with the median of each feature without writing custom code.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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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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