DP-203 Design and implement data storage Practice Question
Which THREE are best practices for optimizing query performance in Azure Synapse Analytics dedicated SQL pool?
⚠ Common exam trap
A common mix-up: candidates confuse resource class with performance optimization, assuming larger resource classes always speed up queries, when in fact they reduce concurrency and can cause resource contention, making them a poor general-purpose best practice.
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 materialized views for complex aggregations
Option A is correct because materialized views in a dedicated SQL pool precompute and persist the results of complex aggregations (such as SUM, COUNT, AVG over large fact tables), letting the optimizer rewrite queries to read the smaller materialized result instead of rescanning and re-aggregating the base tables, which dramatically reduces I/O and CPU. Option C is correct because clustered columnstore indexes are the default and recommended storage format for dedicated SQL pools, providing high compression and batch-mode vectorized execution that speeds up large-scale analytical scans and aggregations on fact tables. Option D is correct because hash distribution on columns frequently used in JOINs co-locates matching rows on the same distribution, enabling collocated joins that avoid costly data movement (shuffle) across the 60 distributions. Option B is not a best practice because assigning the largest resource class to every query consumes excessive memory and concurrency slots, reducing overall workload throughput; resource classes should be sized to each query's needs. Option E is not a best practice because round-robin distribution is suited to staging or temporary tables, whereas large fact tables benefit from hash distribution on a join/filter column to minimize data movement during queries.
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 materialized views for complex aggregations
Why this is correct
Materialized views persist precomputed results of complex aggregations, so repeated queries read stored data instead of rescanning the large fact table. This directly reduces compute and elapsed time for the aggregation-heavy workloads the dedicated SQL pool must optimise.
- ✗
Use the largest resource class for all queries
Why it's wrong here
Larger resource classes allocate more memory per query but reduce concurrency, so applying the maximum to every query starves other sessions. It tempts for individual heavy queries needing extra memory, yet workload-wide use throttles throughput on a dedicated SQL pool.
- ✓
Create clustered columnstore indexes
Why this is correct
Clustered columnstore indexes store data column-wise with high compression and batch-mode processing, giving the dedicated SQL pool its fastest scan performance on large fact tables. This is the default and recommended physical design for analytical queries.
- ✓
Use hash distribution on columns used in JOINs
Why this is correct
Hash distribution spreads rows across distributions by hashing the join key, so matching rows land on the same distribution. This enables collocated joins that avoid costly data movement shuffles across the dedicated SQL pool's compute nodes.
- ✗
Use round-robin distribution for large fact tables
Why it's wrong here
Round-robin distribution scatters fact rows evenly, forcing costly data movement during joins and aggregations; hash distribution on a key keeps matching rows co-located. It tempts for staging or temporary tables, where even distribution and simple loading matter more than join locality.
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