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?
⚠ Common exam trap
MLA-C01 often tests algorithm-to-task mapping — candidates may pick DeepAR or BlazingText because they sound sophisticated, but the exam expects recognition that XGBoost is the go-to built-in for tabular binary classification.
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 built-in Amazon SageMaker algorithm optimized for tabular and structured data, and it consistently performs well on binary classification tasks. It implements a gradient-boosted decision tree framework that handles missing values, categorical features, and class imbalance reasonably well out of the box. For a binary classification problem on tabular data, XGBoost is the standard choice among SageMaker built-in algorithms.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Image Classification
Why it's wrong here
Image Classification trains convolutional neural networks on pixel data, so it cannot accept the tabular feature rows this scenario requires. It is tempting because it is a genuine SageMaker built-in supervised algorithm, and would be correct for labelling images rather than tabular records.
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DeepAR
Why it's wrong here
DeepAR is a supervised forecasting algorithm producing time-series predictions, not class labels, so it cannot output the binary outcome required. It is tempting because it handles structured numeric input well, and would be correct for predicting future values across related time series.
- ✓
XGBoost
Why this is correct
XGBoost is a gradient-boosted decision tree algorithm built into Amazon SageMaker, and it excels on tabular datasets for binary classification through boosted ensemble learning. It directly satisfies the stem's requirement for a built-in algorithm performing well on tabular data, unlike linear or deep learning alternatives.
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BlazingText
Why it's wrong here
BlazingText performs word embeddings and text classification on natural-language input, so it cannot consume the tabular feature set described. It is tempting because it is a supervised SageMaker built-in, and would be correct for sentiment analysis or topic tagging of documents.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.