- A
Principal component analysis
Why wrong: PCA is a dimensionality reduction technique, not a classification algorithm.
- B
K-means clustering
Why wrong: K-means is an unsupervised clustering algorithm, not suitable for classification.
- C
Linear regression
Why wrong: Linear regression is used for continuous target variables, not binary classification.
- D
Logistic regression
Logistic regression is designed for binary classification and handles large datasets efficiently.
Question 88 of 1,000
easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data scientist is building a model to predict…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 building a model to predict customer churn based on historical data. The dataset has 10 features and 100,000 records, and the target is binary. Which algorithm is most appropriate for this binary classification problem?
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
✓
Logistic regression
Logistic regression is the most appropriate algorithm for this binary classification problem because it directly models the probability of the binary target variable using a logistic (sigmoid) function, making it a natural fit for predicting customer churn (yes/no). It is efficient with 100,000 records and 10 features, providing interpretable coefficients that indicate feature importance, which is crucial for understanding churn drivers.
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.
- ✗
Principal component analysis
Why it's wrong here
PCA is a dimensionality reduction technique, not a classification algorithm.
- ✗
K-means clustering
Why it's wrong here
K-means is an unsupervised clustering algorithm, not suitable for classification.
- ✗
Linear regression
Why it's wrong here
Linear regression is used for continuous target variables, not binary classification.
- ✓
Logistic regression
Why this is correct
Logistic regression is designed for binary classification and handles large datasets efficiently.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between supervised and unsupervised learning, and the trap here is that candidates may confuse logistic regression with linear regression due to the similar name, or incorrectly choose PCA or K-means because they are familiar with them for feature reduction or segmentation, ignoring that the task is explicitly binary classification.
Detailed technical explanation
How to think about this question
Logistic regression uses the logistic function (sigmoid) to map linear combinations of features to a probability between 0 and 1, with the decision boundary defined by a threshold (typically 0.5). It is trained via maximum likelihood estimation (MLE) rather than ordinary least squares, making it robust for binary outcomes. In a real-world churn scenario, logistic regression can handle imbalanced classes by adjusting the threshold or using class weights, and its coefficients provide odds ratios that directly quantify the impact of each feature on churn likelihood.
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
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 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: Logistic regression — Logistic regression is the most appropriate algorithm for this binary classification problem because it directly models the probability of the binary target variable using a logistic (sigmoid) function, making it a natural fit for predicting customer churn (yes/no). It is efficient with 100,000 records and 10 features, providing interpretable coefficients that indicate feature importance, which is crucial for understanding churn drivers.
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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