Synapse Dedicated SQL Pool Table Design
Which THREE of the following are best practices for designing tables in a dedicated SQL pool in Azure Synapse Analytics?
⚠ Common exam trap
Watch out — candidates often assume clustered columnstore indexes are unsuitable for large tables due to memory constraints, but they are actually the default and recommended index type for fact tables in Synapse dedicated SQL pools.
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
✓
Avoid data skew by choosing a good distribution key.
Option B is correct because choosing a good distribution key prevents data skew, which otherwise causes some distributions to hold disproportionate rows and forces the query engine to move data across nodes, degrading performance in a dedicated SQL pool. Option D is correct because replicated tables copy the full table to every compute node, eliminating data movement for joins; this is recommended for small dimension tables under roughly 1 GB (or up to 2 GB compressed). Option E is correct because hash distribution on a high-cardinality column spreads rows evenly across the 60 distributions, which is ideal for large fact tables and minimizes skew. Option A is wrong because clustered columnstore indexes are the default and preferred storage for large tables in dedicated SQL pools, delivering high compression and fast analytical scans. Option C is wrong because round-robin distribution is a fallback for staging or tables with no clear join key, not a blanket best practice for all large fact tables, since it can cause costly data movement during joins.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Avoid using clustered columnstore indexes on large tables.
Why it's wrong here
Clustered columnstore indexes are the recommended default for large dedicated SQL pool tables, delivering compression and columnar scan performance; avoiding them forfeits that. The option tempts because columnstore indexes complicate single-row updates, yet that concern is addressed by partitioning and staging patterns, not avoidance.
- ✓
Avoid data skew by choosing a good distribution key.
Why this is correct
A well-chosen hash distribution key spreads rows evenly across the sixty distributions, preventing one distribution from bearing disproportionate rows. Skew forces serialised processing, so even distribution directly supports the parallel-query design of dedicated SQL pools.
- ✗
Use round-robin distribution for all large fact tables.
Why it's wrong here
Round-robin distribution suits small staging or temporary tables; large fact tables benefit from hash distribution on a frequently joined column to co-locate data and cut shuffle. Round-robin is tempting because it removes data-skew concerns, but it forces costly data movement during joins across large fact tables.
- ✓
Use replicated tables for small dimension tables (less than 1 GB).
Why this is correct
Replicated tables copy the full table to every compute node, eliminating data movement during joins. For dimension tables under 1 GB, this removes shuffle operations and is cheaper than hash distribution, satisfying the small-dimension join pattern.
- ✓
Use hash distribution on a column with high cardinality for large fact tables.
Why this is correct
Hash distribution on a high-cardinality column spreads large fact table rows evenly across the 60 distributions, preventing data skew and maximising parallel query throughput. This satisfies the stem's dedicated SQL pool constraint, where even distribution is essential for large fact tables and hash distribution outperforms round-robin for frequent joins.
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