MLA-C01 ML Model Development Practice Question
A data scientist wants to train a binary classification model using Amazon SageMaker. The dataset has 10,000 rows and 50 features. Which SageMaker built-in algorithm is MOST appropriate 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
✓
XGBoost
XGBoost is a popular algorithm for classification and regression tasks. Linear Learner is more suited for linear models, K-Means is for clustering, and DeepAR is for time series forecasting.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
XGBoost
Why this is correct
XGBoost is a gradient boosting algorithm that works well for classification and regression on tabular data.
- ✗
DeepAR
Why it's wrong here
DeepAR is for time series forecasting, not classification.
- ✗
K-Means
Why it's wrong here
K-Means is an unsupervised algorithm for clustering, not classification.
- ✗
Linear Learner
Why it's wrong here
Linear Learner is suitable for linear models but may not capture complex patterns as well as XGBoost.
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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.