The answer is to repartition the data by product ID in addition to date. This approach directly addresses the slow query performance because Athena’s pricing and speed are based on the amount of data scanned; by partitioning on product ID, Athena can perform partition pruning and only read the specific subdirectories matching the requested product ID, drastically reducing scan volume. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of Athena’s architecture and the principle that effective partitioning is the most cost-efficient way to improve query performance without migrating data. A common trap is assuming that converting to a columnar format like Parquet alone solves the problem—while Parquet reduces I/O, it does not eliminate scanning all partitions. Remember: partition pruning beats compression every time for selective queries. Memory tip: “Partition by what you filter, not just by what you store.”
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. 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.
An e-commerce company uses Amazon Kinesis Data Firehose to deliver clickstream data to an Amazon S3 bucket. The data is then queried using Amazon Athena. The marketing team wants to run daily reports that aggregate click events by product ID. However, the reports are slow because Athena scans the entire dataset each time. The data is partitioned by date (e.g., s3://bucket/clickstream/2023/01/01/). The product ID is a column within the data. The data engineering team wants to improve query performance without moving the data to another service. Which approach should the team take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Repartition the data by product ID in addition to date
Partitioning by product ID would allow Athena to prune partitions for queries filtering by product ID. Option A is wrong because converting to Parquet alone may improve but does not eliminate full scan. Option C is wrong because creating a view does not change physical storage. Option D is wrong because Redshift Spectrum still requires scanning.
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.
✗
Convert the data from JSON to Parquet format
Why it's wrong here
Parquet reduces scan size but still scans all partitions if no partition pruning.
✗
Use Amazon Redshift Spectrum to query the data
Why it's wrong here
Spectrum still scans the data in S3; partitioning helps but not addressed.
✗
Create a view in Athena that filters by product ID
Why it's wrong here
A view does not change underlying data organization; still full scan.
✓
Repartition the data by product ID in addition to date
Why this is correct
Partitioning by product ID allows Athena to skip irrelevant partitions.
Related concept
Static NAT maps one inside address to one outside address.
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 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.
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: Repartition the data by product ID in addition to date — Partitioning by product ID would allow Athena to prune partitions for queries filtering by product ID. Option A is wrong because converting to Parquet alone may improve but does not eliminate full scan. Option C is wrong because creating a view does not change physical storage. Option D is wrong because Redshift Spectrum still requires scanning.
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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