The answer is to convert the data to Apache Parquet format and use Amazon Athena for SQL-based summary statistics. This approach is the most cost-effective and time-efficient because Parquet’s columnar storage allows Athena to scan only the numeric columns needed for mean, median, and standard deviation, drastically reducing data scanned and query costs compared to scanning the entire 10 TB of CSV data. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of optimizing large-scale exploratory data analysis by pairing columnar formats with serverless query engines, a common pattern for efficient summary statistics on large datasets. A frequent trap is choosing AWS Glue ETL or SageMaker Data Wrangler for simple statistics, but those services add unnecessary overhead and cost for straightforward aggregations. Memory tip: think “Parquet + Athena = pay only for the columns you query,” which minimizes both time and expense.
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 engineer is performing exploratory data analysis on a large dataset stored in Amazon S3 (10 TB in CSV format). The dataset has 2000 columns and 50 million rows. The engineer needs to compute summary statistics (mean, median, standard deviation) for each numeric column and identify missing values. Which approach is MOST cost-effective and time-efficient?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Convert the data to Apache Parquet format, then use Amazon Athena to run SQL queries for statistics.
Using Amazon Athena with columnar formats like Parquet after converting from CSV reduces query costs and improves performance. Option A (SageMaker Data Wrangler) may struggle with 2000 columns. Option B (AWS Glue ETL) is more expensive and slower for simple statistics. Option D (Redshift Spectrum) requires setting up a Redshift cluster, which is overkill.
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 Amazon Redshift Spectrum to query the data directly from S3.
Why it's wrong here
Redshift Spectrum requires a Redshift cluster, adding cost and complexity.
✗
Load the data into Amazon SageMaker Data Wrangler and compute statistics interactively.
Why it's wrong here
Data Wrangler has limitations with very wide datasets and interactive use may be slow.
✓
Convert the data to Apache Parquet format, then use Amazon Athena to run SQL queries for statistics.
Why this is correct
Parquet reduces data scanned, and Athena is cost-effective for ad-hoc queries.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use AWS Glue ETL to compute statistics and write results to S3.
Why it's wrong here
Glue ETL is more expensive and complex than query-based approaches.
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.
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.
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: Convert the data to Apache Parquet format, then use Amazon Athena to run SQL queries for statistics. — Using Amazon Athena with columnar formats like Parquet after converting from CSV reduces query costs and improves performance. Option A (SageMaker Data Wrangler) may struggle with 2000 columns. Option B (AWS Glue ETL) is more expensive and slower for simple statistics. Option D (Redshift Spectrum) requires setting up a Redshift cluster, which is overkill.
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.
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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.
Question Discussion
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