- A
Launch an Amazon SageMaker notebook instance with an attached EBS volume large enough to hold the data
Why wrong: 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.
- B
Use Amazon Athena Federated Query to run SQL queries against Redshift and retrieve aggregated results
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.
- C
Use a SQLAlchemy connection to read the entire table into a pandas DataFrame and sample it
Why wrong: 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.
- D
Export the Redshift table to Amazon S3 in Parquet format, then use pandas to read the Parquet files
Why wrong: 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.
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 |
What to study next
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Amazon Athena Federated Query to run SQL queries against Redshift and retrieve aggregated results — Option B is correct because 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.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jun 20, 2026
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