MLS-C01 Modeling Practice Question
Which TWO of the following are valid Amazon SageMaker built-in algorithms for regression tasks? (Select TWO.)
⚠ Common exam trap
Watch out — 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.
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
✓
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.
Answer analysis
Option-by-option breakdown
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.
- ✗
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.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.