Question 211 of 760
DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
You are optimizing an Azure Synapse Analytics dedicated SQL pool that is experiencing high concurrency and frequent resource class contention. You need to improve query performance and reduce contention without changing the workload. Which two actions should you take? (Choose two.)
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
✓
Implement workload isolation to separate critical queries into dedicated resource groups.
Options B and D are correct. Workload isolation and workload classification help manage resource allocation and reduce contention. Option A is wrong because increasing DWU may help but does not address contention directly. Option C is wrong because materialized views improve performance but do not reduce contention. Option E is wrong because result set caching helps read workloads but not contention.
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 DWU (Data Warehouse Units) to allocate more resources.
Why it's wrong here
Increasing DWU allocates more resources but does not address resource class contention directly; it may improve overall throughput but does not isolate workloads or prioritize queries.
- ✓
Implement workload isolation to separate critical queries into dedicated resource groups.
Why this is correct
Workload isolation creates dedicated resource groups for critical queries, ensuring they have reserved resources and reducing contention with other workloads.
- ✗
Create materialized views to pre-aggregate data.
Why it's wrong here
Materialized views pre-aggregate data to improve query performance for read-heavy workloads, but they do not manage resource allocation or reduce contention between concurrent queries.
- ✓
Use workload classification to assign importance and resource allocation to different queries.
Why this is correct
Workload classification allows assigning importance and resource allocation to different queries, enabling prioritization of critical queries and better resource management to reduce contention.
- ✗
Enable result set caching to reduce repeated query execution.
Why it's wrong here
Result set caching stores query results to avoid recomputation for repeated queries, but it does not help with resource class contention that arises from concurrent queries competing for resources.
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Same concept, more angles
2 more ways this is tested on DP-203
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Your Azure Synapse Analytics dedicated SQL pool is experiencing performance degradation. You notice that some queries are being queued due to resource class conflicts. What should you implement to optimize performance and reduce queuing?
medium- A.Scale the dedicated SQL pool to a higher DWU level
- ✓ B.Configure workload management with workload groups and classifiers
- C.Create materialized views for the most common aggregations
- D.Enable result-set caching for frequently run queries
Why B: Workload management with workload groups and classifiers allows you to assign queries to different resource classes and prioritize them, directly addressing resource class conflicts and reducing queuing. Option A is incorrect: scaling the pool to a higher DWU increases overall resources but does not specifically manage resource class contention; it may also incur additional cost without solving the root issue. Option C is incorrect: materialized views improve query performance by pre-aggregating data but do not affect concurrency or queuing. Option D is incorrect: result-set caching reduces repeated computation for identical queries but does not resolve queuing caused by resource class conflicts.
Variation 2. You have an Azure Synapse Analytics dedicated SQL pool that handles both high-priority real-time queries and low-priority batch jobs. You need to ensure that high-priority queries always get the resources they need, while batch jobs do not starve. What should you configure?
medium- A.Enable result-set caching for the high-priority queries
- B.Enable data compression on the tables used by batch jobs
- ✓ C.Create workload groups for high-priority and low-priority queries, assigning appropriate importance and resource percentages
- D.Create materialized views for the batch job queries
Why C: Workload management with workload groups allows you to set importance and resource allocation. Option A is wrong because result-set caching does not prioritize queries. Option B is wrong because data compression reduces storage but does not affect prioritization. Option D is wrong because materialized views improve performance but do not prioritize.
Last reviewed: Jun 21, 2026
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