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 company uses Azure Synapse Analytics dedicated SQL pool to run large-scale analytics. The data engineering team notices that queries are slow due to excessive data movement between distributions. Which index type should be recommended to minimize data movement for fact tables that are frequently joined on a specific column?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Why wrong: Ordered clustered columnstore index improves performance for queries with range filters, but it does not affect data movement during joins.
B
Clustered columnstore index
Why wrong: Clustered columnstore index is a storage format that improves compression and scan performance, but it does not address data movement between distributions.
C
Round-robin distributed table
Why wrong: Round-robin distributes rows evenly but without any alignment, causing data movement for joins.
D
Hash-distributed table
Hash-distributed table distributes rows based on a hash of the distribution column, ensuring that rows with the same join key are on the same distribution, minimizing data movement.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Hash-distributed table
Hash-distributed tables distribute rows across distributions based on a hash of a chosen column. When fact tables are frequently joined on that specific column, using the same distribution column ensures that matching rows from both tables reside on the same distribution, eliminating the need to shuffle data between distributions during the join. This minimizes data movement and significantly improves query performance in Azure Synapse dedicated SQL pools.
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.
✗
Ordered clustered columnstore index
Why it's wrong here
Ordered clustered columnstore index improves performance for queries with range filters, but it does not affect data movement during joins.
✗
Clustered columnstore index
Why it's wrong here
Clustered columnstore index is a storage format that improves compression and scan performance, but it does not address data movement between distributions.
✗
Round-robin distributed table
Why it's wrong here
Round-robin distributes rows evenly but without any alignment, causing data movement for joins.
✓
Hash-distributed table
Why this is correct
Hash-distributed table distributes rows based on a hash of the distribution column, ensuring that rows with the same join key are on the same distribution, minimizing data movement.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
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 confuse index types (like columnstore) with table distribution strategies, assuming that a better index alone can solve data movement issues, when in fact distribution design is the primary mechanism to minimize cross-distribution data shuffling in Azure Synapse dedicated SQL pools.
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 each row to one of 60 distributions. When two tables are hash-distributed on the same column used in a join, the query engine can perform a collocated join, where each distribution processes only its local data without shuffling. This is critical for fact tables in star schemas, where the fact table is often joined to multiple dimension tables; choosing the most frequently joined column as the distribution key can reduce or eliminate expensive data movement operations like ShuffleMove or BroadcastMove.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
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: Hash-distributed table — Hash-distributed tables distribute rows across distributions based on a hash of a chosen column. When fact tables are frequently joined on that specific column, using the same distribution column ensures that matching rows from both tables reside on the same distribution, eliminating the need to shuffle data between distributions during the join. This minimizes data movement and significantly improves query performance in Azure Synapse dedicated SQL pools.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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