DP-203 Design and implement data storage Practice Question
You are tuning a dedicated SQL pool in Azure Synapse Analytics. A query that joins two large tables (fact_sales and dim_product) is slow. The fact_sales table is hash-distributed on product_id, and dim_product is replicated. You notice that the query plan shows a shuffle move. What is the most likely cause?
⚠ Common exam trap
Watch out — candidates often confuse a shuffle move with a broadcast join or blame indexing, but the root cause is the mismatch between the join key and the distribution key, which forces data movement regardless of other optimizations.
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
✓
The join condition does not include the distribution key for fact_sales.
When the join condition does not include the distribution key (product_id) of the hash-distributed fact_sales table, the SQL engine cannot perform a collocated join. Instead, it must shuffle data across distributions to satisfy the join, which introduces expensive data movement. The shuffle move in the query plan directly indicates this redistribution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The fact_sales table uses clustered columnstore index.
Why it's wrong here
Columnstore index does not cause shuffle.
- ✗
The dim_product table is replicated, causing a broadcast join.
Why it's wrong here
Replicated tables are broadcast to all distributions, avoiding shuffle.
- ✗
Statistics are out of date on both tables.
Why it's wrong here
Outdated statistics affect cardinality estimates but not directly shuffle.
- ✓
The join condition does not include the distribution key for fact_sales.
Why this is correct
Joins on non-distribution keys require data movement.
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Senior Network & Security Engineer · founder of Courseiva
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