The answer is to reorganize the data by partitioning on customer_id first, then transaction_date, and convert the data to Parquet format. This combination works because partitioning on the most frequently filtered column—customer_id—maximizes partition pruning, drastically reducing the amount of data Athena scans, while Parquet’s columnar storage and compression further shrink scanned bytes and speed up queries. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of Athena optimization strategies within a data pipeline context, often disguised as a performance or cost-reduction question. A common trap is choosing to increase workers (Athena is serverless) or converting to JSON, which inflates file size. Remember the memory tip: “Filter first, then time—Parquet makes the data climb.”
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. 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 uses Amazon S3 to store historical transaction data in CSV format. The data is partitioned by transaction_date. A data analyst runs Amazon Athena queries that frequently filter on customer_id and transaction_date. The queries are slow and expensive. The team needs to improve query performance and reduce cost. Which combination of actions should the team take? (Choose TWO.)
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 from CSV to Parquet format.
Option B and D are correct. Converting to Parquet reduces data scanned due to columnar storage and compression. Partitioning by customer_id (which is frequently filtered) improves partition pruning. Option A is wrong because increasing workers is not applicable to Athena (serverless). Option C is wrong because converting to JSON increases size. Option E is wrong because using S3 Select may not integrate with Athena directly.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Enable S3 Select pushdown in Athena to reduce data transfer.
Why it's wrong here
Athena does not use S3 Select pushdown for query optimization.
✗
Convert the data to JSON format for better query performance.
Why it's wrong here
JSON is row-based and larger, increasing scan size.
✓
Convert the data from CSV to Parquet format.
Why this is correct
Parquet is columnar and compressed, reducing data scanned.
Related concept
Static NAT maps one inside address to one outside address.
✓
Reorganize the data by partitioning on customer_id first, then transaction_date.
Why this is correct
Partition pruning on customer_id reduces the data scanned.
Related concept
Static NAT maps one inside address to one outside address.
✗
Increase the number of Athena query workers.
Why it's wrong here
Athena automatically scales; there is no worker configuration.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Data Engineering — This question tests Data Engineering — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Convert the data from CSV to Parquet format. — Option B and D are correct. Converting to Parquet reduces data scanned due to columnar storage and compression. Partitioning by customer_id (which is frequently filtered) improves partition pruning. Option A is wrong because increasing workers is not applicable to Athena (serverless). Option C is wrong because converting to JSON increases size. Option E is wrong because using S3 Select may not integrate with Athena directly.
What should I do if I get this MLS-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Question Discussion
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