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Develop data processinghardMultiple SelectObjective-mapped

DP-203 Develop data processing Practice Question

You are designing a data processing solution in Azure Synapse Analytics. You need to load data from multiple sources into a dedicated SQL pool. Which THREE of the following are best practices for loading data?

⚠ Common exam trap

Many candidates assume more files always increase parallelism, but Azure Synapse Analytics optimizes for fewer, larger files to minimize metadata operations and maximize throughput.

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 round-robin distribution for staging tables

Round-robin distribution is recommended for staging tables because it distributes data evenly across all distributions without requiring a hash key, minimizing data movement during the load process. Since staging tables are temporary and used for intermediate storage, the even distribution ensures that the subsequent INSERT...SELECT operation into the final table benefits from maximum parallelism and reduced skew.

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 round-robin distribution for staging tables

    Why this is correct

    Round-robin distributes data evenly for fast load.

  • Use a staging table to load data and then insert into the final table

    Why this is correct

    Staging reduces locking and allows data validation.

  • Split data into many small files to increase parallelism

    Why it's wrong here

    Many small files cause overhead; use fewer, larger files.

  • Use PolyBase or COPY statement for parallel loading

    Why this is correct

    Parallel loading maximizes throughput.

  • Enable clustered columnstore index during load to improve query performance

    Why it's wrong here

    Disable indexes during load for faster inserts.

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Last reviewed: Jun 24, 2026

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