SageMaker Data Wrangler for Large Dataset EDA: Handling Out-of-Memory in Studio
A data scientist is analyzing a dataset stored in Amazon S3 (100 GB, CSV format) using Amazon SageMaker Studio. The dataset contains 500 columns and 10 million rows. The data scientist wants to understand the distribution of each column, detect missing values, and identify outliers. However, the SageMaker Studio notebook instance runs out of memory when loading the entire dataset into a pandas DataFrame. The data scientist needs to complete the EDA efficiently without modifying the source data. What should the data scientist do?
Quick Answer
The answer is to use Amazon SageMaker Data Wrangler to create a data flow that samples and profiles the data. This is correct because Data Wrangler operates within SageMaker Studio and processes large datasets in a distributed, memory-efficient manner, allowing you to perform exploratory data analysis (EDA) on a 100 GB CSV without loading the entire file into a pandas DataFrame. It handles out-of-memory issues by automatically sampling and profiling column distributions, missing values, and outliers directly from Amazon S3. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of SageMaker’s native tools for large-scale EDA versus costly or complex alternatives. A common trap is choosing to increase the instance size, which is expensive and may still fail, or using AWS Glue ETL, which is less integrated with Studio. Remember the memory tip: “Data Wrangler wrangles without strangling memory—sample first, profile fast.”
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 Data Wrangler to create a data flow that samples and profiles the data.
SageMaker Data Wrangler is purpose-built for EDA on large datasets; it automatically samples data and profiles columns without requiring the entire dataset to be loaded into memory. Option A (sampling 10% of rows) could work but risks missing critical patterns or outliers, and is less integrated than Data Wrangler. Option B (converting to Parquet with AWS Glue) adds complexity and still requires memory to load into pandas. Option C (larger instance) may still be insufficient and is more expensive. Data Wrangler provides a seamless, integrated experience within SageMaker Studio for efficient EDA.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Write a script that loads only a random 10% sample of rows to reduce memory usage.
Why it's wrong here
Sampling may miss outliers and patterns.
- ✗
Use AWS Glue ETL to transform the data into Parquet format and then load into pandas.
Why it's wrong here
Complex and not necessary.
- ✗
Launch a larger notebook instance with more memory (e.g., ml.r5.24xlarge) and reload the data.
Why it's wrong here
May still fail and is costly.
- ✓
Use Amazon SageMaker Data Wrangler to create a data flow that samples and profiles the data.
Why this is correct
Data Wrangler can handle large datasets efficiently.
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
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Same concept, more angles
1 more way this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data analyst is using Amazon SageMaker Studio to perform exploratory data analysis on a dataset stored in S3. The analyst wants to generate summary statistics and visualizations quickly. Which built-in feature of SageMaker Studio should the analyst use?
easy- A.SageMaker Ground Truth
- ✓ B.SageMaker Data Wrangler
- C.SageMaker Autopilot
- D.SageMaker Clarify
Why B: SageMaker Data Wrangler is a built-in visual data preparation tool in SageMaker Studio that provides summary statistics, histograms, and correlation matrices without writing code. Option A (SageMaker Ground Truth) is for data labeling, not EDA. Option C (SageMaker Autopilot) automates machine learning model building. Option D (SageMaker Clarify) is for bias detection and model explainability.
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.