MLS-C01 Exploratory Data Analysis Practice Question
A machine learning engineer is performing exploratory data analysis on a large dataset stored in S3 using Amazon Athena. The dataset contains a timestamp column 'event_time' of type string. The engineer wants to analyze daily trends. Which approach is the most cost-effective and efficient?
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
✓
Partition the table by date derived from the event_time string and query using partition filtering.
Converting the string to a date type in the query allows Athena to use partition pruning if the table is partitioned by date, reducing scanned data. Option A is wrong because creating a view does not reduce data scanned; CAST still processes all rows. Option B is wrong because using CAST in the SELECT statement still scans all data. Option C is wrong because converting to Parquet is beneficial but not the most direct for the given task.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a view that casts the column to timestamp and query the view.
Why it's wrong here
Creating a view does not reduce the amount of data scanned. All data is still read from the underlying table.
- ✗
Use the CAST function in the SELECT statement to convert the string to timestamp.
Why it's wrong here
Using CAST in the SELECT statement still scans all rows in the table, so it is not efficient for analyzing daily trends.
- ✗
Convert the data to Parquet format with a timestamp column and re-query.
Why it's wrong here
While converting to Parquet format can improve performance and reduce costs, it is not the most direct solution for the given task and requires additional processing and storage costs.
- ✓
Partition the table by date derived from the event_time string and query using partition filtering.
Why this is correct
Partitioning the table by date derived from the event_time string allows Athena to use partition pruning, which significantly reduces the data scanned when querying daily trends, making it the most cost-effective and efficient approach.
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 |
Go deeper
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