MLA-C01 ML Model Development Practice Question
A data scientist wants to train a binary classification model using Amazon SageMaker with a built-in algorithm that performs well on tabular data. Which algorithm should they choose?
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 popular built-in algorithm in SageMaker for classification and regression on tabular data. Linear Learner is also for tabular data but XGBoost often performs better for complex patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Image Classification
Why it's wrong here
Image Classification is for image data, not tabular.
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DeepAR
Why it's wrong here
DeepAR is for time-series forecasting, not classification.
- ✓
XGBoost
Why this is correct
XGBoost is a gradient boosting algorithm that works well for classification and regression on tabular data.
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BlazingText
Why it's wrong here
BlazingText is for text classification and word2vec, not general tabular classification.
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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.