- A
Change distribution to replicated table
Why wrong: 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.
- B
Change distribution to round-robin
Why wrong: Round-robin distributes data evenly but does not co-locate data for any particular column. Queries filtering on WarehouseID would still require shuffling data across distributions, leading to slow performance.
- C
Create a columnstore index
Why wrong: 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.
- D
Change distribution to hash on 'WarehouseID'
Hash-distributing on WarehouseID places all rows for a specific warehouse on the same compute node. Queries that filter by WarehouseID become single-distribution queries, eliminating expensive data shuffles. This directly addresses the observed performance bottleneck.
DP-900 Describe an analytics workload on Azure Practice Question
This DP-900 practice question tests your understanding of describe an analytics workload on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 data evenly but does not co-locate data for any particular column. Queries filtering on WarehouseID would still require shuffling data across distributions, leading to slow performance.
- ✗
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 on WarehouseID places all rows for a specific warehouse on the same compute node. Queries that filter by WarehouseID become single-distribution queries, eliminating expensive data shuffles. This directly addresses the observed performance bottleneck.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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.
Detailed technical explanation
How to think about this question
In Azure Synapse dedicated SQL pool, hash distribution uses a deterministic hash function on the distribution column to assign rows to one of 60 distributions. When queries filter on a non-distribution column, the engine must shuffle data across distributions to satisfy the join or aggregation, which incurs significant network I/O and tempdb usage. By choosing 'WarehouseID' as the hash key, the data is physically partitioned by warehouse, enabling partition elimination and local aggregations that avoid the shuffle bottleneck.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DP-900 question test?
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this DP-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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