MLA-C01 ML Model Development Practice Question
A company needs to perform time-series forecasting on historical sales data. Which SageMaker built-in algorithm is BEST suited for this task?
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
✓
DeepAR
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 NLP tasks.
- ✗
Linear Learner
Why it's wrong here
Linear Learner is for regression/classification, not time-series.
- ✗
XGBoost
Why it's wrong here
XGBoost can be used for time-series with feature engineering, but DeepAR is purpose-built.
- ✓
DeepAR
Why this is correct
DeepAR is a built-in algorithm for time-series forecasting.
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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.