MLS-C01 Exploratory Data Analysis Practice Question
A company has a large dataset of customer transactions stored in Amazon Redshift. A data scientist wants to perform EDA using Python libraries like pandas and matplotlib. The dataset is too large to fit into memory on a single EC2 instance. What is the most efficient approach?
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 Athena Federated Query to run SQL queries against Redshift and retrieve aggregated results
Amazon Athena Federated Query can query data in Amazon Redshift directly, allowing the data scientist to run SQL queries that aggregate the data before returning results. This avoids moving the entire dataset and reduces memory usage. Option A is wrong because even with a large EBS volume, the data must still be loaded into memory (pandas DataFrame) on the notebook instance, which may not fit. Option C is wrong because using SQLAlchemy to read the entire table into a pandas DataFrame would require loading all data into memory, causing an out-of-memory error. Option D is wrong because exporting to S3 and then reading with pandas still requires loading the entire dataset into memory, which is inefficient for large datasets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Launch an Amazon SageMaker notebook instance with an attached EBS volume large enough to hold the data
Why it's wrong here
Launching a SageMaker notebook with a large EBS volume still requires loading the entire dataset into memory (pandas DataFrame), which may not fit. This is inefficient for large datasets.
- ✓
Use Amazon Athena Federated Query to run SQL queries against Redshift and retrieve aggregated results
Why this is correct
Amazon Athena Federated Query allows running SQL queries directly against Redshift, returning only aggregated results. This avoids moving the entire dataset and reduces memory usage on the notebook instance, making it the most efficient approach for EDA.
- ✗
Use a SQLAlchemy connection to read the entire table into a pandas DataFrame and sample it
Why it's wrong here
Using a SQLAlchemy connection to read the entire Redshift table into a pandas DataFrame would attempt to load all data into memory, likely causing an out-of-memory error on the EC2 instance. This is not efficient for large datasets.
- ✗
Export the Redshift table to Amazon S3 in Parquet format, then use pandas to read the Parquet files
Why it's wrong here
Exporting the Redshift table to S3 as Parquet and then reading with pandas still requires loading the entire dataset into memory. While Parquet is efficient for storage and columnar access, the full data must be read into a DataFrame, which may not fit in memory.
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.