The answer is Linear Learner. This built-in SageMaker algorithm is the most appropriate choice for regression tasks like predicting house prices because it is specifically designed to model a continuous numeric target variable by minimizing mean squared error, fitting a linear relationship between features such as square footage and location and the price output. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your ability to match the algorithm to the problem type—regression versus classification—and a common trap is confusing Linear Learner with XGBoost or K-Nearest Neighbors, which are also used for regression but are tree-based or distance-based rather than linear. The exam often presents a straightforward regression scenario to see if you recognize that Linear Learner handles automatic feature scaling and model tuning out of the box. Memory tip: think “linear for continuous line”—if the target is a continuous number, start with Linear Learner.
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. 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 company wants to build a machine learning model to predict house prices based on features like square footage, number of bedrooms, and location. The target variable is a continuous numeric value. Which Amazon SageMaker built-in algorithm is most appropriate for this task?
Why wrong: Object2Vec is used for learning embeddings of pairs of objects, not for regression on tabular data.
B
XGBoost
Why wrong: XGBoost can be used for regression, but Linear Learner is more straightforward for a linear regression scenario.
C
Linear Learner
Linear Learner is designed for regression and classification, and is the most direct choice for predicting a continuous value with linear relationships.
D
BlazingText
Why wrong: BlazingText is for text classification and word embeddings, not tabular regression.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Linear Learner
Linear Learner is the most appropriate built-in algorithm for this regression task because it is specifically designed for predicting continuous numeric values (house prices) using linear models. It supports both regression and classification, and for regression, it minimizes mean squared error (MSE) to fit a linear relationship between features and the target variable. The algorithm also offers automatic feature scaling and model tuning, making it a direct fit for this use case.
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.
✗
Object2Vec
Why it's wrong here
Object2Vec is used for learning embeddings of pairs of objects, not for regression on tabular data.
✗
XGBoost
Why it's wrong here
XGBoost can be used for regression, but Linear Learner is more straightforward for a linear regression scenario.
✓
Linear Learner
Why this is correct
Linear Learner is designed for regression and classification, and is the most direct choice for predicting a continuous value with linear relationships.
Related concept
Read the scenario before looking for a memorised answer.
✗
BlazingText
Why it's wrong here
BlazingText is for text classification and word embeddings, not tabular regression.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose XGBoost (Option B) because it is a popular and powerful algorithm for tabular data, but the question specifically asks for the most appropriate built-in algorithm for a linear regression task, and Linear Learner is the direct, optimized choice for that purpose.
Trap categories for this question
Scenario analysis trap
XGBoost can be used for regression, but Linear Learner is more straightforward for a linear regression scenario.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker's Linear Learner uses stochastic gradient descent (SGD) with an adaptive learning rate schedule and supports L1 and L2 regularization to prevent overfitting. It automatically handles feature normalization and can train multiple models in parallel with different hyperparameters, selecting the best based on validation loss. In a real-world scenario, if the relationship between features and house prices is approximately linear, Linear Learner will converge quickly and provide interpretable coefficients, which is valuable for stakeholders who need to understand feature impact.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Linear Learner — Linear Learner is the most appropriate built-in algorithm for this regression task because it is specifically designed for predicting continuous numeric values (house prices) using linear models. It supports both regression and classification, and for regression, it minimizes mean squared error (MSE) to fit a linear relationship between features and the target variable. The algorithm also offers automatic feature scaling and model tuning, making it a direct fit for this use case.
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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Question Discussion
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