Databricks Data Engineer Professional exam dumps

Databricks Data Engineer Professional practice question 38 of 313

Databricks Certified Data Engineer Professional. Professional level, Databricks. Free question with the correct answer and a full explanation.

Databricks Data Engineer Professional Question 38

Single answer

You are working on a large-scale e-commerce dataset stored in a Delta Lake table. The table contains transaction data including columns for transaction_id, user_id, product_id, transaction_date, and region. The table will be queried frequently to analyze daily transaction trends per region. Additionally, the dataset will be appended daily with new transactions. Which column(s) would be the most appropriate choice for partitioning the table to optimize query performance?

  1. A

    transaction_id

  2. B

    user_id

  3. C

    region

  4. D

    transaction_date

  5. E

    product_id

Show answer and explanation

Correct answer: D

Explanation

Partitioning a table requires selecting a column (or columns) that align with the most common query patterns and the data ingestion strategy to optimize performance. Since the queries focus on daily trends and new data is appended daily, partitioning by transaction_date ensures efficient query execution by enabling partition pruning for specific dates. Partitioning by other columns, such as transaction_id, user_id, or product_id, would either lead to inefficiencies or fail to optimize for the query patterns.

  • A. Incorrect.

    Partitioning by transaction_id would not be effective because it is a unique identifier for each transaction and would result in too many small partitions, leading to inefficiencies.

  • B. Incorrect.

    Partitioning by user_id is not ideal because it does not align with the query requirements, which focus on daily trends and regions rather than individual users.

  • C. Incorrect.

    Partitioning by region alone would not sufficiently optimize the queries focusing on daily trends, as it does not account for the transaction_date column, which is crucial for daily analysis.

  • D. Correct.

    Partitioning by transaction_date is the most appropriate choice because the queries involve analyzing daily trends, and this column allows for pruning unnecessary partitions for queries targeting specific dates. Additionally, appending daily data aligns with this partitioning strategy.

  • E. Incorrect.

    Partitioning by product_id is not ideal because the queries and appends are not focused on products but rather on daily and regional trends.

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