The answer is a clustered columnstore index, as it is specifically designed to optimize aggregation queries in Azure Synapse Analytics dedicated SQL pools. This index stores data column-wise rather than row-wise, which allows for high compression and enables the query engine to read only the columns needed for aggregations like summing sales by product and region, drastically reducing I/O. Additionally, columnstore indexes leverage batch-mode processing, which accelerates scan and aggregation operations by processing data in large chunks. On the Microsoft Azure Data Fundamentals DP-900 exam, this concept tests your understanding of workload patterns: fact tables with heavy aggregations favor columnstore indexes, while rowstore indexes suit point lookups or singleton operations. A common trap is choosing a clustered rowstore index for speed, but that would still scan all columns. Remember the memory tip: “Aggregate fast with columnar cast”—columnstore for bulk scans and sums.
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. A key principle to apply: clustered columnstore indexes store data in a columnar format.. 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 store sales data. They frequently run queries that aggregate sales by product and region over the past month. The queries are slow because they scan the entire table. Which index type should they implement on the fact table to improve query performance for these aggregations?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Clustered columnstore index
A clustered columnstore index is ideal for large fact tables in Azure Synapse Analytics dedicated SQL pool because it stores data column-wise, enabling high compression and eliminating the need to scan irrelevant columns. For aggregation queries that sum sales by product and region over the past month, the columnstore index significantly reduces I/O by reading only the necessary columns and applying batch-mode processing, which accelerates scan and aggregation operations.
Key principle: Clustered columnstore indexes store data in a columnar format.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
Clustered columnstore index
Why this is correct
Columnstore indexes are optimized for large scans and aggregations, typical in data warehouse workloads.
Related concept
Clustered columnstore indexes store data in a columnar format.
✗
Clustered index on the primary key
Why it's wrong here
A clustered rowstore index is designed for point lookups, not efficient for full-table scans.
✗
Hash-distributed table on SalesID
Why it's wrong here
Hash distribution affects data placement across distributions, not the index structure itself.
✗
Non-clustered index on (ProductID, Region)
Why it's wrong here
A non-clustered index may help but columnstore is more efficient for heavy aggregations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse indexing strategies for transactional OLTP workloads (where rowstore indexes like clustered or non-clustered are optimal) with analytical OLAP workloads, failing to recognize that columnstore indexes are specifically designed for large-scale aggregations and scans in dedicated SQL pools.
Detailed technical explanation
How to think about this question
Clustered columnstore indexes in Azure Synapse Analytics use column segments and row groups to achieve up to 10x compression compared to rowstore, and they leverage batch-mode execution where operators process data in batches of up to 900 rows, drastically reducing CPU cycles. A real-world scenario is a fact table with billions of rows where monthly aggregation queries on a subset of columns benefit from column elimination and predicate pushdown, making columnstore the default recommendation for data warehousing workloads.
KKey Concepts to Remember
Clustered columnstore indexes store data in a columnar format.
They are highly optimized for analytical queries and aggregations.
Columnstore indexes use advanced compression and segment elimination.
They are the recommended index type for fact tables in data warehouses.
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
Clustered columnstore indexes store data in a columnar format.
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. Clustered columnstore indexes store data in a columnar format. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
Review clustered columnstore indexes store data in a columnar format., then practise related DP-900 questions on the same topic to reinforce the concept.
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Clustered columnstore indexes store data in a columnar format..
What is the correct answer to this question?
The correct answer is: Clustered columnstore index — A clustered columnstore index is ideal for large fact tables in Azure Synapse Analytics dedicated SQL pool because it stores data column-wise, enabling high compression and eliminating the need to scan irrelevant columns. For aggregation queries that sum sales by product and region over the past month, the columnstore index significantly reduces I/O by reading only the necessary columns and applying batch-mode processing, which accelerates scan and aggregation operations.
What should I do if I get this DP-900 question wrong?
Review clustered columnstore indexes store data in a columnar format., then practise related DP-900 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Clustered columnstore indexes store data in a columnar format.
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