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Question 1,190 of 1,755

Exploratory Data Analysis →hardMultiple ChoiceObjective-mapped

How to Handle Large CSV Datasets in Pandas for EDA – AWS ML Specialty

This MLS-C01 practice question tests your understanding of exploratory data analysis. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 data scientist is performing exploratory data analysis on a large dataset stored in Amazon S3 (100 GB, CSV format, 500 columns). The dataset contains customer transaction records with features such as transaction amount, timestamp, customer ID, and numerous categorical variables (e.g., product category, payment method, location). The scientist wants to understand the distribution of transaction amounts across different product categories and identify any outliers. They have an Amazon SageMaker notebook instance with a ml.t3.medium instance and are using pandas. However, when trying to load the entire dataset into a DataFrame using pd.read_csv('s3://bucket/data.csv'), the notebook crashes with a memory error. Additionally, the scientist suspects that some categorical columns have high cardinality (e.g., product category has thousands of unique values), and there are missing values in several columns. What is the MOST efficient approach to perform the EDA without modifying the original dataset or using additional AWS services? Options: A) Use the SageMaker SDK to launch a parallel processing job with PySpark and read the data into a Spark DataFrame, then compute statistics and visualize with matplotlib. B) Use pandas with chunksize parameter to iterate through the dataset in chunks, compute per-chunk statistics, and aggregate results; for high-cardinality columns, use value_counts() with dropna=False and then plot the top 20 categories. C) Use the S3 Select API to filter rows and columns before loading into pandas, reducing the data size; then use pandas for EDA. D) Use SageMaker Data Wrangler to import the dataset, create a flow to handle missing values and reduce cardinality, and export a sample to the notebook for analysis.

A
Use the SageMaker SDK to launch a parallel processing job with PySpark and read the data into a Spark DataFrame, then compute statistics and visualize with matplotlib.
Why wrong: Requires setting up PySpark which is not available on the current instance; adds unnecessary complexity and cost.
B
Use the S3 Select API to filter rows and columns before loading into pandas, reducing the data size; then use pandas for EDA.
Why wrong: S3 Select can reduce data but cannot compute distribution across categories without retrieving all rows; still may exceed memory if many categories.
C
Use SageMaker Data Wrangler to import the dataset, create a flow to handle missing values and reduce cardinality, and export a sample to the notebook for analysis.
Why wrong: Data Wrangler is a separate service requiring setup; exporting a sample may lose information needed for full distribution analysis.
D
Use pandas with chunksize parameter to iterate through the dataset in chunks, compute per-chunk statistics, and aggregate results; for high-cardinality columns, use value_counts() with dropna=False and then plot the top 20 categories.
Directly solves memory issue by chunking; handles high cardinality by limiting to top categories; no extra services needed.

Full breakdown with real-world context →

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 pandas with chunksize parameter to iterate through the dataset in chunks, compute per-chunk statistics, and aggregate results; for high-cardinality columns, use value_counts() with dropna=False and then plot the top 20 categories.

Option D is correct because it addresses the memory issue by reading the data in chunks using the chunksize parameter, allowing processing without loading the entire dataset into memory. It computes per-chunk statistics and aggregates them, which is efficient for EDA. For high-cardinality categorical columns, it uses value_counts() with dropna=False to capture missing values, and then plots the top 20 categories, which is manageable and insightful. This approach stays within pandas and the existing SageMaker notebook without requiring additional services or changing the dataset. Option A is incorrect because launching a separate PySpark job adds complexity and extra cost, and is not the most efficient for an ad-hoc EDA. Option B (S3 Select) can reduce the data volume but cannot natively perform complex aggregations like distribution across categories without pulling all rows; it is more suited for simple filtering. Option C (SageMaker Data Wrangler) is a separate service that requires additional setup and is overkill for this simple EDA task; it also modifies the workflow and is not the most efficient for immediate analysis.

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.

✗
Use the SageMaker SDK to launch a parallel processing job with PySpark and read the data into a Spark DataFrame, then compute statistics and visualize with matplotlib.
Why it's wrong here
Requires setting up PySpark which is not available on the current instance; adds unnecessary complexity and cost.
✗
Use the S3 Select API to filter rows and columns before loading into pandas, reducing the data size; then use pandas for EDA.
Why it's wrong here
S3 Select can reduce data but cannot compute distribution across categories without retrieving all rows; still may exceed memory if many categories.
✗
Use SageMaker Data Wrangler to import the dataset, create a flow to handle missing values and reduce cardinality, and export a sample to the notebook for analysis.
Why it's wrong here
Data Wrangler is a separate service requiring setup; exporting a sample may lose information needed for full distribution analysis.
✓
Use pandas with chunksize parameter to iterate through the dataset in chunks, compute per-chunk statistics, and aggregate results; for high-cardinality columns, use value_counts() with dropna=False and then plot the top 20 categories.
Why this is correct
Directly solves memory issue by chunking; handles high cardinality by limiting to top categories; no extra services needed.
Related concept
Read the scenario before looking for a memorised answer.

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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.

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 pandas with chunksize parameter to iterate through the dataset in chunks, compute per-chunk statistics, and aggregate results; for high-cardinality columns, use value_counts() with dropna=False and then plot the top 20 categories. — Option D is correct because it addresses the memory issue by reading the data in chunks using the chunksize parameter, allowing processing without loading the entire dataset into memory. It computes per-chunk statistics and aggregates them, which is efficient for EDA. For high-cardinality categorical columns, it uses value_counts() with dropna=False to capture missing values, and then plots the top 20 categories, which is manageable and insightful. This approach stays within pandas and the existing SageMaker notebook without requiring additional services or changing the dataset. Option A is incorrect because launching a separate PySpark job adds complexity and extra cost, and is not the most efficient for an ad-hoc EDA. Option B (S3 Select) can reduce the data volume but cannot natively perform complex aggregations like distribution across categories without pulling all rows; it is more suited for simple filtering. Option C (SageMaker Data Wrangler) is a separate service that requires additional setup and is overkill for this simple EDA task; it also modifies the workflow and is not the most efficient for immediate analysis.

What should I do if I get this MLS-C01 question wrong?

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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