The correct answer is to create a clustered columnstore index on the table and order by OrderDate. This works because physically ordering the data by OrderDate within each columnstore segment enables segment elimination, where the engine skips entire segments that do not match the filter, drastically reducing I/O and scan time. On the DP-203 exam, this scenario tests your understanding of how to optimize large fact tables in Azure Synapse dedicated SQL pools without altering distribution keys—a common trap is to suggest re-distributing the table or adding a non-clustered index, which would not leverage columnstore’s compression and segment skipping. Remember that hash-distribution on CustomerID handles parallelism, while ORDER BY on the filter column handles data skipping. Memory tip: “Hash for balance, Order for filter—segment elimination is the killer.”
DP-203 Design and implement data storage Practice Question
This DP-203 practice question tests your understanding of design and implement data storage. 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.
Your company uses Azure Synapse Analytics dedicated SQL pool for a data warehouse. You notice that queries on a large fact table are slow. The table is hash-distributed on CustomerID and has 60 distributions. After reviewing the query plan, you see that many queries filter on OrderDate. You want to improve query performance without redesigning the table. What should you do?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create a clustered columnstore index on the table and order by OrderDate
Option D is correct because creating a clustered columnstore index with ORDER BY OrderDate on the fact table improves query performance by physically ordering the data by OrderDate within each columnstore segment. This allows segment elimination during queries that filter on OrderDate, reducing the amount of data scanned. Since the table is already hash-distributed on CustomerID, this approach does not require redesigning the table and directly addresses the slow queries without changing the distribution strategy.
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 the distribution key to OrderDate
Why it's wrong here
Changing distribution key requires table redesign and may impact join performance.
✗
Recreate the table with round-robin distribution
Why it's wrong here
Round-robin distribution would hurt join performance on CustomerID.
✗
Update statistics only on the OrderDate column
Why it's wrong here
Statistics alone may not provide enough performance improvement without an appropriate index.
✓
Create a clustered columnstore index on the table and order by OrderDate
Why this is correct
Clustered columnstore index with ordering by OrderDate accelerates range queries.
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 assume changing the distribution key (Option A) is the only way to optimize filter-heavy queries, but they overlook that a clustered columnstore index with ORDER BY can provide similar I/O reduction without the risks of data skew or redesign.
Detailed technical explanation
How to think about this question
In Azure Synapse dedicated SQL pool, a clustered columnstore index with ORDER BY enables segment elimination: each columnstore segment stores metadata (min/max values) for the ordered column, so queries with range filters on OrderDate can skip entire segments that do not contain relevant data. This is similar to partition elimination but operates at the segment level within a distribution, and it works without altering the hash distribution key. In practice, this technique is especially effective for fact tables with time-series data where queries frequently filter on a date range.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
Design and implement data storage — This question tests Design and implement data storage — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a clustered columnstore index on the table and order by OrderDate — Option D is correct because creating a clustered columnstore index with ORDER BY OrderDate on the fact table improves query performance by physically ordering the data by OrderDate within each columnstore segment. This allows segment elimination during queries that filter on OrderDate, reducing the amount of data scanned. Since the table is already hash-distributed on CustomerID, this approach does not require redesigning the table and directly addresses the slow queries without changing the distribution strategy.
What should I do if I get this DP-203 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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Question Discussion
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