MLS-C01 Exploratory Data Analysis Practice Question
A data scientist runs a SQL query on an Amazon Athena table and notices that the query scans a large amount of data. Which approach would reduce the amount of data scanned without changing the SQL logic?
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 on a column that is frequently used in WHERE clauses.
Partitioning the table on a column that is frequently used in WHERE clauses allows Athena to prune partitions and only scan the relevant data, reducing the amount of data scanned. Option B (JSON) does not reduce scan because it is not columnar. Option C (Parquet without partitioning) is columnar and can reduce scan through column pruning, but without partitioning it still scans entire columns. Option D (GZIP) compresses data but Athena decompresses and scans the full file size, so no reduction in scanned data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Partition the table on a column that is frequently used in WHERE clauses.
Why this is correct
Partitioning prunes data and reduces scanned bytes.
- ✗
Convert the data from CSV to JSON format.
Why it's wrong here
JSON typically results in larger data volume than CSV.
- ✗
Store the data in Parquet format without partitioning.
Why it's wrong here
Parquet reduces scan size due to columnar storage, but without partitioning, full column scans still occur.
- ✗
Use GZIP compression on the data files.
Why it's wrong here
Compression alone does not reduce scan size; Athena decompresses data before scanning.
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