MLA-C01 Data Preparation for Machine Learning Practice Question
An e-commerce company uses Amazon SageMaker to train a model that predicts click-through rates. The training data includes a timestamp column 'click_time' and a categorical feature 'device_type' (8 values). They notice that the model's performance degrades over time because the data distribution shifts. They want to ensure the training data represents the most recent behavior. The data is stored in a daily partitioned S3 bucket (e.g., s3://bucket/data/2024-01-01/). The total dataset size is 500 GB. Which approach should they take to prepare the training data while minimizing bias and cost?
⚠ Common exam trap
AWS often tests the misconception that more data always improves model performance, but in the presence of concept drift, recent data is more valuable than historical data, making a time-window selection the most cost-effective and bias-minimizing strategy.
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
✓
Select only the data from the last 30 days to train the model.
Selecting only the last 30 days of data directly addresses the data distribution shift by focusing on the most recent user behavior, which is critical for click-through rate prediction. This approach minimizes bias from outdated patterns and reduces training cost by using a smaller, relevant dataset (approximately 500 GB / 365 * 30 ≈ 41 GB). SageMaker training jobs benefit from this reduced volume through faster data loading and lower compute costs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Select only the data from the last 30 days to train the model.
Why this is correct
Using a recent window captures current patterns, reduces volume, and mitigates drift.
- ✗
Take a random sample of 10% of the rows from the entire dataset.
Why it's wrong here
Random sampling does not prioritize recent data; distribution may still shift.
- ✗
Use all historical data and let the model learn the temporal patterns.
Why it's wrong here
Including old data can bias the model toward past behavior that is no longer relevant.
- ✗
Downsample older data exponentially so that recent data is overrepresented.
Why it's wrong here
This still includes some old data and may not fully capture recent shifts.
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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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.