DP-900 Describe an analytics workload on Azure Practice Question
A logistics company uses Azure Synapse Analytics dedicated SQL pool to analyze billions of shipment records. The table 'Shipments' is 10 TB and hash-distributed on 'ShipmentID'. Analysts frequently run queries that filter on 'WarehouseID' and aggregate by 'Region'. These queries are slow because they cause data movement (shuffle) across distributions. Which table design change will most improve query performance for these analytical workloads?
⚠ Common exam trap
Candidates often confuse indexing (columnstore) with distribution design, assuming that a better index alone can fix shuffle-related performance issues, when in fact the distribution key is the primary factor determining data movement in a massively parallel processing (MPP) architecture.
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
✓
Change distribution to hash on 'WarehouseID'
D is correct because hash-distributing the 'Shipments' table on 'WarehouseID' ensures that all rows for a given warehouse are co-located on the same distribution node. This eliminates the need for data movement (shuffle) when queries filter on 'WarehouseID' and aggregate by 'Region', as the aggregation can be performed locally on each distribution without redistributing data across nodes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change distribution to replicated table
Why it's wrong here
Replicated tables are suitable for smaller dimension tables that do not change frequently. The Shipments table is a large fact table (10 TB), so replicating it to all distributions would be impractical and would consume excessive storage and cause performance issues during load.
- ✗
Change distribution to round-robin
Why it's wrong here
Round-robin distributes rows evenly across all distributions without regard to any column value, so there is no co-location of data for WarehouseID. A filter on WarehouseID forces the query engine to scan every distribution and then shuffle matching rows across nodes to assemble the result, rather than pruning to a single distribution. This causes the same expensive data movement the query is trying to avoid, making round-robin a poor choice for a fact table that is frequently filtered by WarehouseID.
- ✗
Create a columnstore index
Why it's wrong here
Dedicated SQL pool already uses columnstore indexes by default. While columnstore indexes improve compression and scan performance, they do not reduce data movement caused by distribution. The root cause is the distribution key, not the index type.
- ✓
Change distribution to hash on 'WarehouseID'
Why this is correct
Hash-distributing the Shipments table on WarehouseID uses a deterministic hash function to assign every row for a given warehouse to the same distribution, physically co-locating all related data on a single compute node. When a query filters on WarehouseID, the engine can directly target that one distribution, eliminating the need to shuffle data across all nodes. This converts a full-distribution scan into a single-distribution seek, drastically reducing I/O and data movement, which is exactly the fix for the observed performance bottleneck.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
Key term
Dedicated SQL pool
A Dedicated SQL pool is a cloud-based analytics service in Azure Synapse Analytics that provides a managed, scalable environment for running large-scale data warehousing queries using Transact-SQL.
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