MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a large dataset of images stored in Amazon S3. The dataset is used to train a computer vision model. Which THREE EDA steps are appropriate for this image dataset?
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
✓
Compute the distribution of image dimensions (height and width).
The appropriate EDA steps for an image dataset include analyzing image dimensions (A) to understand size variability and potential resizing needs, checking for corrupted or unreadable files (B) to ensure data integrity, and visualizing sample images per class (D) to verify label accuracy and detect labeling errors. Option C (time series decomposition) is irrelevant because timestamps, while possibly present, are not a primary focus of standard image EDA; it would be relevant for time-series data. Option E (tokenization and stop word removal) applies to text data, not images.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Compute the distribution of image dimensions (height and width).
Why this is correct
Computing the distribution of image dimensions (height and width) helps identify variations in input size, which is important for resizing or padding decisions.
- ✓
Check for corrupted or unreadable image files.
Why this is correct
Checking for corrupted or unreadable image files is essential to ensure data integrity and avoid training on invalid data.
- ✗
Decompose the time series of image timestamps to detect seasonality.
Why it's wrong here
Decomposing the time series of image timestamps to detect seasonality is not a standard EDA step for images unless the dataset has a temporal dependency, which is not indicated.
- ✓
Visualize a sample of images from each class to verify labels.
Why this is correct
Visualizing a sample of images from each class helps verify that labels match the actual content and detect labeling errors.
- ✗
Perform tokenization and stop word removal on image filenames.
Why it's wrong here
Tokenization and stop word removal are text preprocessing techniques and are not applicable to image data.
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 |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-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 MLS-C01 exam.