MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is working on a predictive maintenance project for a manufacturing company. Sensor data is collected every second from 100 machines and stored in an Amazon S3 bucket as Parquet files, partitioned by machine_id and date. The dataset is massive (10 TB) and contains over 2000 features per machine. The data scientist needs to perform exploratory data analysis to identify which features are most predictive of machine failure. They have access to Amazon SageMaker Studio with a SageMaker Data Wrangler flow. The initial data exploration is taking too long due to the volume of data. The data scientist wants to speed up the analysis without losing accuracy in feature selection. Which course of action is most appropriate?
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 SageMaker Data Wrangler to create a stratified sample by machine_id and date, then analyze the sample
SageMaker Data Wrangler supports stratified sampling, which preserves the distribution of machine failure across machine_id and date, allowing for faster exploratory data analysis while maintaining representativeness for feature selection. Option A is incorrect because distributed processing with EMR on the full dataset may still be slow and is unnecessary when sampling can capture the signal. Option B is incorrect because using only one machine's data loses cross-machine variability and may bias feature selection. Option D is incorrect because random sampling does not guarantee preservation of time series order or failure distribution, potentially compromising analysis accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch to using Amazon EMR with Spark to perform distributed feature selection on the full dataset
Why it's wrong here
Using Amazon EMR with Spark for distributed feature selection on the full 10 TB dataset still processes all 2000 features across every second of data, which does not reduce the computational load during initial exploration — the bottleneck is the sheer volume of data and features, not the lack of parallelism. This option is tempting because Spark on EMR excels at scaling distributed processing across large datasets, and would be correct if the goal were to train a model on the full dataset rather than to accelerate exploratory analysis without reducing data volume.
- ✗
Reduce the data to a single partition by concatenating all files and use only one machine's data
Why it's wrong here
Incorrect: This loses cross-machine variability and may bias feature selection.
- ✓
Use SageMaker Data Wrangler to create a stratified sample by machine_id and date, then analyze the sample
Why this is correct
Correct: Stratified sampling preserves distribution of key variables and reduces data size.
- ✗
Use Amazon Athena to query a random sample of rows from the dataset
Why it's wrong here
Incorrect: Random sampling may break time series dependencies and is not stratified.
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
Related to this question
About these practice questions
This MLS-C01 question is part of Courseiva's 1,672-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.