Quick Answer
The answer is both Linear Learner and XGBoost, as these are the two built-in regression algorithms in Amazon SageMaker. Linear Learner is a straightforward choice because it directly models a linear relationship between features and a continuous target variable, supporting both regression and classification via a logistic loss function. XGBoost is equally valid for regression tasks because it offers built-in regression objectives like 'reg:squarederror' and 'reg:logistic', leveraging gradient boosting with an ensemble of decision trees to minimize residual errors. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish SageMaker’s built-in algorithms from third-party or custom ones; a common trap is assuming XGBoost is only for classification, but its regression objectives make it a dual-purpose tool. Remember the memory tip: “Linear for lines, XGBoost for trees—both predict continuous values with ease.”
This MLS-C01 practice question tests your understanding of modeling. 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.
Which TWO of the following are valid Amazon SageMaker built-in algorithms for regression tasks? (Select TWO.)
BlazingText
Why wrong: BlazingText is for word2vec and text classification.
XGBoost
XGBoost supports regression.
Image Classification
Why wrong: Image Classification is for classification, not regression.
Object Detection
Why wrong: Object Detection is for detection, not regression.
Linear Learner
Linear Learner supports both classification and regression.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
XGBoost
XGBoost is a valid Amazon SageMaker built-in algorithm for regression tasks because it supports regression objectives such as 'reg:squarederror' and 'reg:logistic'. It is a gradient boosting framework that builds an ensemble of decision trees, making it suitable for both regression and classification problems.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
BlazingText
Why it's wrong here
BlazingText is for word2vec and text classification.
XGBoost
Why this is correct
XGBoost supports regression.
Related concept
Read the scenario before looking for a memorised answer.
Image Classification
Why it's wrong here
Image Classification is for classification, not regression.
Object Detection
Why it's wrong here
Object Detection is for detection, not regression.
Linear Learner
Why this is correct
Linear Learner supports both classification and regression.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often confuse algorithms that can be used for regression (like XGBoost and Linear Learner) with those that are exclusively for classification or computer vision tasks, leading them to select BlazingText or Image Classification incorrectly.
Detailed technical explanation
Under the hood, XGBoost uses gradient boosting with regularization to prevent overfitting, and it can handle missing values automatically. In a real-world regression scenario like predicting house prices, XGBoost can capture non-linear relationships and interactions between features more effectively than linear models, especially when the dataset contains mixed data types and outliers.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: XGBoost — XGBoost is a valid Amazon SageMaker built-in algorithm for regression tasks because it supports regression objectives such as 'reg:squarederror' and 'reg:logistic'. It is a gradient boosting framework that builds an ensemble of decision trees, making it suitable for both regression and classification problems.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 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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