DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
You are optimizing an Azure Synapse Analytics dedicated SQL pool. You need to reduce the amount of data read from storage during queries that filter on a date column. The fact table is partitioned by month on the date column. What should you do to improve query performance?
⚠ Common exam trap
The trap here is thinking that adding indexes or changing distribution will reduce data reads, when partition elimination is the key for partitioned tables.
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
✓
Ensure that queries include a predicate on the partitioning column to enable partition elimination.
Partition elimination is a technique where the query optimizer skips partitions that do not match the query's filter criteria. By ensuring queries filter on the partitioning column, you minimize the data scanned, reducing I/O and improving performance. This is especially effective for large fact tables partitioned by date.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the resource class of the queries to allocate more memory.
Why it's wrong here
Increasing the resource class allocates more memory to queries, which can help with complex operations, but it does not reduce the amount of data read from storage. The bottleneck is I/O, not memory. Partition elimination directly addresses the amount of data read.
- ✗
Create a nonclustered index on the date column.
Why it's wrong here
Nonclustered indexes are not supported on clustered columnstore tables in dedicated SQL pools. Even if they were, they would not reduce the amount of data read from storage as effectively as partition elimination. The primary benefit would be for point lookups, not range scans.
- ✓
Ensure that queries include a predicate on the partitioning column to enable partition elimination.
Why this is correct
Partition elimination allows the query engine to skip reading partitions that do not contain relevant data. By including a filter on the partitioning column, queries only scan the necessary partitions, reducing I/O and improving performance. This is a fundamental optimization for partitioned tables.
- ✗
Change the distribution to round-robin to evenly spread data across distributions.
Why it's wrong here
Round-robin distribution can improve load performance and evenly distribute data, but it does not help with partition elimination. In fact, it may increase data movement during joins. The goal is to reduce data read during queries, which is achieved by partition elimination, not by changing distribution.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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