DP-203 Develop data processing Practice Question
You are designing a data processing solution in Azure Synapse Analytics. The solution must use a dedicated SQL pool to store fact and dimension tables. The fact table is expected to have billions of rows. Which distribution strategy should you recommend for the fact table to optimize query performance and minimize data movement?
⚠ Common exam trap
Many candidates confuse partitioning with distribution, thinking that partitioning alone can optimize data movement across nodes, but partitioning operates within a distribution and does not affect how data is distributed across compute resources.
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
✓
Hash distribution on a column that is frequently used in joins and aggregations.
Hash distribution on a column frequently used in joins and aggregations is the best choice for a fact table with billions of rows in a dedicated SQL pool. It distributes rows across distributions based on a hash of the distribution column, ensuring that rows with the same key value are co-located on the same distribution. This minimizes data movement during joins and aggregations, as the data required for these operations is already local to each distribution, significantly improving query performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Round-robin distribution.
Why it's wrong here
Round-robin spreads rows evenly without co-locating matching keys, so joins to dimension tables force shuffles across all distributions, harming performance on a billion-row fact table. It suits staging or temporary tables lacking a clear join column, not large fact tables queried with dimensions.
- ✗
Partitioned table with a partition key.
Why it's wrong here
Partitioning splits rows by date or range within a distribution but does not determine how rows are assigned across compute nodes, so joins still trigger data movement. Partitioning complements hash distribution on large fact tables; it is not itself a distribution strategy.
- ✓
Hash distribution on a column that is frequently used in joins and aggregations.
Why this is correct
Hash distribution spreads billions of fact rows evenly across distributions, and choosing a column frequently used in joins and aggregations enables collocated joins that eliminate shuffling. Round-robin would force data movement during joins, while replicated distribution cannot hold a fact table of this size.
- ✗
Replicated distribution.
Why it's wrong here
Replicated distribution copies the full table to every compute node, which is impractical for a fact table of billions of rows because of storage and rebuild costs. Replication suits small dimension tables joined frequently, where eliminating shuffles outweighs the duplication overhead.
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