- A
Imputation
Why wrong: Imputation handles missing values, not dimensionality reduction.
- B
Principal Component Analysis (PCA)
Why wrong: PCA reduces dimensionality but transforms features, losing interpretability.
- C
StandardScaler
Why wrong: StandardScaler normalizes features but does not reduce dimensionality.
- D
Feature Selection (correlation-based)
Data Wrangler's feature selection transform can remove highly correlated features, preserving interpretability.
Question 355 of 1,000
hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data engineer is using Amazon SageMaker Data…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 engineer is using Amazon SageMaker Data Wrangler to create a data preparation flow for a dataset with 500 columns, many of which are highly correlated. The goal is to reduce dimensionality while preserving interpretability. Which built-in transform in Data Wrangler should be applied?
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
✓
Feature Selection (correlation-based)
Option D is correct because the goal is to reduce dimensionality while preserving interpretability. SageMaker Data Wrangler's built-in Feature Selection (correlation-based) transform identifies and removes highly correlated columns, directly reducing the number of features without transforming the original variables into new, uninterpretable components. This preserves the meaning of each selected column, which is essential when interpretability is a priority.
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.
- ✗
Imputation
Why it's wrong here
Imputation handles missing values, not dimensionality reduction.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA reduces dimensionality but transforms features, losing interpretability.
- ✗
StandardScaler
Why it's wrong here
StandardScaler normalizes features but does not reduce dimensionality.
- ✓
Feature Selection (correlation-based)
Why this is correct
Data Wrangler's feature selection transform can remove highly correlated features, preserving interpretability.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse dimensionality reduction with PCA, assuming it is always the best choice, but the question explicitly requires preserving interpretability, which PCA inherently sacrifices.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker Data Wrangler's correlation-based feature selection computes pairwise Pearson correlation coefficients between all numeric columns and then removes one column from each pair exceeding a user-defined threshold (default 0.95). This is a filter-based method that does not require training a model, making it fast for large datasets with 500 columns. In real-world scenarios, this approach is critical for tabular data in regulated industries (e.g., finance, healthcare) where model explainability is legally required, and PCA would be unacceptable because its components lack direct business meaning.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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: Feature Selection (correlation-based) — Option D is correct because the goal is to reduce dimensionality while preserving interpretability. SageMaker Data Wrangler's built-in Feature Selection (correlation-based) transform identifies and removes highly correlated columns, directly reducing the number of features without transforming the original variables into new, uninterpretable components. This preserves the meaning of each selected column, which is essential when interpretability is a priority.
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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