Databricks Machine Learning Professional Question 157
Single answerYou are working with a large dataset containing transaction data for an e-commerce platform. The dataset is stored as a Delta table and includes the columns transaction_id, user_id, product_id, category, transaction_date, and amount. The majority of queries on this table filter data based on the transaction_date column. To improve query performance, what would be the most effective approach?
- A
Partition the Delta table by the
transaction_datecolumn. - B
Partition the Delta table by the
user_idcolumn. - C
Partition the Delta table by the
product_idcolumn. - D
Partition the Delta table by the
categorycolumn.
Show answer and explanation
Correct answer: A
Explanation
Partitioning is a technique used to improve query performance by organizing data based on a specific column that is frequently used in filtering conditions. In this case, since most queries filter on the transaction_date column, partitioning by this column ensures that only the necessary partitions are scanned during query execution, significantly reducing query time and improving efficiency.
- A. Correct.
Partitioning the Delta table by the
transaction_datecolumn is the most effective option because most of the queries filter on this column. Partitioning ensures that only the relevant partitions are scanned during queries, leading to faster performance. - B. Incorrect.
Partitioning by the
user_idcolumn is not ideal in this case because the queries are not primarily filtering onuser_id. This would not significantly improve query performance. - C. Incorrect.
Partitioning by the
product_idcolumn is not effective here because the queries are focused ontransaction_date, notproduct_id. Partitioning on an unused column for filtering would result in unnecessary overhead. - D. Incorrect.
Partitioning by the
categorycolumn is not suitable because the queries do not filter primarily oncategory. This would not align with the query patterns and would not yield performance improvements.