MLA-C01 ML Model Development Practice Question
A data scientist needs to train a binary classification model on a large tabular dataset stored in Amazon S3. The team wants to minimize training time and cost while using a built-in SageMaker algorithm. Which algorithm should they use?
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
✓
Linear Learner
Linear Learner is a built-in SageMaker algorithm designed for binary classification and regression, and it scales efficiently on large datasets. XGBoost is better for structured data with non-linear relationships, DeepAR is for time series, and BlazingText is for text.
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 text classification and word embeddings, not tabular data.
- ✗
DeepAR
Why it's wrong here
DeepAR is for time series forecasting, not binary classification.
- ✓
Linear Learner
Why this is correct
Linear Learner is built for large-scale classification and regression, providing fast training and built-in distributed training support.
- ✗
XGBoost
Why it's wrong here
XGBoost is a tree-based ensemble method that requires iterative training on the full dataset, which increases both time and cost for large tabular data compared to a linear learner that uses stochastic gradient descent for faster convergence. It is tempting because XGBoost often achieves high accuracy on structured data and is a default choice for tabular classification problems where model performance, not training speed, is the primary constraint.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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