easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: Build a machine learning model to predict house…
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?
⚠ Common exam trap
Many exam-takers 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.
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
✓
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.
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.
- ✗
BlazingText
Why it's wrong here
BlazingText is for text classification and word embeddings, not tabular regression.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.