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DP-203 Develop data processing Practice Question

Your organization uses Azure Synapse Analytics dedicated SQL pool to store sales data. You need to design a data loading process for a nightly batch that inserts new rows and updates existing rows based on the business key. The table has a clustered columnstore index. Which approach minimizes table fragmentation?

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

Create a staging table, load data, then use CTAS and partition switching to replace the target partition.

Using a staging table with CREATE TABLE AS SELECT (CTAS) and then switching partitions replaces the entire partition without individual row modifications, which minimizes fragmentation in a clustered columnstore index. Option A (UPDATE and INSERT) is wrong because individual updates cause columnstore fragmentation. Option B (DELETE and INSERT in a single transaction) also involves row-level changes that fragment columnstore. Option C (MERGE) is wrong because MERGE operations on columnstore indexes cause significant fragmentation due to row-by-row modifications.

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 UPDATE for existing rows and INSERT for new rows.

    Why it's wrong here

    UPDATE on columnstore causes fragmentation.

  • Use DELETE and INSERT statements in a single transaction.

    Why it's wrong here

    Individual row operations fragment columnstore.

  • Use a MERGE statement to perform upserts.

    Why it's wrong here

    MERGE causes row-by-row operations and fragmentation.

  • Create a staging table, load data, then use CTAS and partition switching to replace the target partition.

    Why this is correct

    CTAS rebuilds the partition, minimizing fragmentation.

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