MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing EDA on a dataset of 1 million images stored in Amazon S3. Each image is 100x100 pixels in RGB format. The data scientist wants to compute the mean pixel value per channel across the entire dataset. Which approach is most efficient?
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
✓
Use Amazon SageMaker Processing with a custom Python script that iterates over S3 objects and aggregates pixel values.
(Amazon SageMaker Processing with a custom Python script) is the most efficient because it can distribute the computation across multiple instances, processing images in parallel without loading all into memory at once. This is ideal for a large dataset of 1 million images. Option B (Athena) is designed for querying structured data, not image processing. Option C (AWS Glue ETL) is for ETL on tabular data, not image processing. Option D (SageMaker notebook with large instance) would require loading all images into memory, which is not feasible for 1 million 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.
- ✓
Use Amazon SageMaker Processing with a custom Python script that iterates over S3 objects and aggregates pixel values.
Why this is correct
SageMaker Processing can distribute the workload across multiple instances for efficient computation.
- ✗
Use Amazon Athena with a SQL query on the image metadata stored in a CSV file.
Why it's wrong here
Athena cannot process image pixel data directly.
- ✗
Use AWS Glue ETL to read images and compute the mean.
Why it's wrong here
AWS Glue is optimized for structured data, not image processing.
- ✗
Use a SageMaker notebook instance with a large instance type to load all images into memory and compute the mean.
Why it's wrong here
1 million images would exceed memory of even large instances.
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
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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.