- A
Remove rows with missing values
Why wrong: Removing rows with missing values is not best practice because it can discard useful data and reduce sample size.
- B
Impute missing values using mean or median imputation
Imputing missing values using mean or median imputation is recommended because it preserves data and avoids bias.
- C
Set missing values to zero
Why wrong: Setting missing values to zero can introduce bias and is not a standard approach for Linear Learner.
- D
Use the `handle_missing` parameter in the algorithm
Why wrong: The Linear Learner algorithm does not have a `handle_missing` parameter; data preprocessing is required.
Question 1,554 of 1,755
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 uses Amazon SageMaker to train a model using the built-in Linear Learner algorithm. The training data contains missing values in some features. What is the best practice for handling missing values with this algorithm?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
✓
Impute missing values using mean or median imputation
Linear Learner expects dense input; it cannot handle missing values. The best practice is to impute missing values before training, such as using mean or median imputation. Removing rows with missing values (Option A) may lose valuable data. Setting missing values to zero (Option C) could bias the model. The algorithm does not have a built-in `handle_missing` parameter (Option D). Therefore, Option B (Impute missing values using mean or median imputation) is correct.
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.
- ✗
Remove rows with missing values
Why it's wrong here
Removing rows with missing values is not best practice because it can discard useful data and reduce sample size.
- ✓
Impute missing values using mean or median imputation
Why this is correct
Imputing missing values using mean or median imputation is recommended because it preserves data and avoids bias.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set missing values to zero
Why it's wrong here
Setting missing values to zero can introduce bias and is not a standard approach for Linear Learner.
- ✗
Use the `handle_missing` parameter in the algorithm
Why it's wrong here
The Linear Learner algorithm does not have a `handle_missing` parameter; data preprocessing is required.
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 MLS-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 MLS-C01 question test?
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Impute missing values using mean or median imputation — Linear Learner expects dense input; it cannot handle missing values. The best practice is to impute missing values before training, such as using mean or median imputation. Removing rows with missing values (Option A) may lose valuable data. Setting missing values to zero (Option C) could bias the model. The algorithm does not have a built-in `handle_missing` parameter (Option D). Therefore, Option B (Impute missing values using mean or median imputation) is correct.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
This MLS-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 MLS-C01 exam.
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