MLS-C01 Exploratory Data Analysis Practice Question
A data engineer is exploring a large dataset in Amazon Athena. The dataset is partitioned by date and stored in Parquet format. The engineer wants to check the number of distinct values in a column for a specific date range. Which THREE practices reduce query cost and improve performance?
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
✓
Use the COUNT(DISTINCT column) function.
Options A, B, and E are correct. Using COUNT(DISTINCT column) (A) is a precise way to count distinct values, and while it scans the column, it avoids fetching unnecessary data. Filtering with a WHERE clause on the partition column (B) limits the data scanned to only the relevant partitions, significantly reducing cost and improving performance. Using a columnar format like Parquet (E) reduces I/O by reading only the required columns. Option C (ORDER BY) is incorrect because it requires sorting the entire result set, increasing processing time and cost. Option D (SELECT *) is incorrect as it retrieves all columns, negating the benefits of columnar storage and increasing data scanned.
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 the COUNT(DISTINCT column) function.
Why this is correct
Efficiently counts distinct values without fetching all rows.
- ✓
Filter the query with a WHERE clause on the partition column.
Why this is correct
Partition pruning reduces data scanned.
- ✗
Use ORDER BY to sort the results.
Why it's wrong here
Sorting requires full scan and extra compute.
- ✗
Use SELECT * to retrieve all columns.
Why it's wrong here
Scans unnecessary columns, increasing cost.
- ✓
Ensure the table is columnar (Parquet) to reduce I/O.
Why this is correct
Parquet stores column data efficiently, reducing scan.
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