Question 1,267 of 1,672
MLS-C01 Modeling Practice Question
Which TWO of the following are appropriate use cases for Amazon SageMaker built-in algorithms?
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
✓
Classifying customer churn using tabular data
XGBoost is suitable for tabular classification. BlazingText is for text classification on word embeddings. Image classification using custom CNNs may use built-in but not necessarily. Time series forecasting is not a built-in algorithm (use DeepAR). Reinforcement learning is not a built-in algorithm.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Classifying customer churn using tabular data
Why this is correct
XGBoost or Linear Learner can be used.
- ✗
Reinforcement learning using Q-learning
Why it's wrong here
Reinforcement learning requires custom containers.
- ✓
Classifying text documents using word embeddings
Why this is correct
BlazingText can be used for text classification.
- ✗
Image classification using a custom CNN architecture
Why it's wrong here
Built-in algorithms do not support custom architectures; use Bring Your Own Model.
- ✗
Time series forecasting using ARIMA
Why it's wrong here
Built-in algorithm DeepAR is for time series, not ARIMA.
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Last reviewed: Jun 20, 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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