The answer is to implement table partitioning on a date column and create statistics on columns used in WHERE clauses. Partitioning on a date column enables partition elimination, allowing the query engine to scan only relevant subsets of large fact tables rather than the entire dataset, drastically reducing I/O and improving performance. Creating statistics on frequently filtered columns gives the Azure Synapse Analytics dedicated SQL pool query optimizer accurate cardinality estimates, which is critical for choosing efficient join strategies and avoiding suboptimal plans on skewed data. On the DP-203 exam, this scenario tests your understanding of dedicated SQL pool design patterns, often appearing as a multi-select question where a common trap is to suggest indexing every column or using round-robin distribution for fact tables. Remember the memory tip: “Partition to prune, stats to tune” — partition eliminates data scans, while statistics fine-tune the optimizer’s path.
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.
Which TWO actions should you take to optimize query performance in Azure Synapse Analytics dedicated SQL pool when working with large fact tables?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create statistics on columns used in WHERE clauses.
Option C is correct because creating statistics on columns used in WHERE clauses enables the Azure Synapse Analytics dedicated SQL pool query optimizer to generate more accurate cardinality estimates, leading to better join strategies and index selections. Without up-to-date statistics, the optimizer may choose suboptimal plans, especially for large fact tables where data distribution skew is common.
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.
✗
Use replicated distribution for the fact table.
Why it's wrong here
Replicated distribution is only for small dimension tables.
✗
Use round-robin distribution to evenly distribute data.
Why it's wrong here
Round-robin causes data movement for joins and aggregations.
✓
Create statistics on columns used in WHERE clauses.
Why this is correct
Statistics help the optimizer choose efficient query plans.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use clustered index instead of columnstore index.
Why it's wrong here
Columnstore indexes are optimized for large fact tables; clustered indexes are not.
✓
Implement table partitioning on a date column.
Why this is correct
Partition elimination reduces the amount of data scanned.
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 distribution methods (replicated, round-robin, hash) with performance tuning for large fact tables, overlooking that statistics maintenance is a critical and separate optimization step that directly impacts query plan quality.
Detailed technical explanation
How to think about this question
In Azure Synapse dedicated SQL pools, statistics are stored as histogram objects that capture data distribution across columns; the query optimizer uses these histograms to estimate row counts for filters, joins, and aggregations. Without statistics on a date column used in a WHERE clause, the optimizer may assume uniform distribution, leading to a poor choice between hash-distributed joins or broadcast moves, which can cause excessive data movement. Real-world scenarios with date-range filters on fact tables benefit significantly from statistics because they allow the optimizer to choose partition elimination and efficient hash joins.
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 statistics on columns used in WHERE clauses. — Option C is correct because creating statistics on columns used in WHERE clauses enables the Azure Synapse Analytics dedicated SQL pool query optimizer to generate more accurate cardinality estimates, leading to better join strategies and index selections. Without up-to-date statistics, the optimizer may choose suboptimal plans, especially for large fact tables where data distribution skew is common.
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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