DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
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?
⚠ Common exam trap
DP-203 often tests the misconception that performance features like caching, compression, or materialized views solve resource contention, when only workload groups with importance actually govern prioritization.
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
✓
Create workload groups for high-priority and low-priority queries, assigning appropriate importance and resource percentages
Workload groups in a dedicated SQL pool let you classify requests into groups and assign each group an importance level (e.g., HIGH for real-time queries, LOW for batch) plus a resource percentage (min/max memory and concurrency). This ensures high-priority queries preempt lower-priority ones for resources while guaranteeing batch jobs a floor so they don't starve. It is the native workload management mechanism for dedicated SQL pools.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable result-set caching for the high-priority queries
Why it's wrong here
Result-set caching returns cached results for repeated identical queries, bypassing resource allocation entirely and offering no priority mechanism for first executions. It would be correct when high-priority queries repeat identically and you want to cut their execution cost.
- ✗
Enable data compression on the tables used by batch jobs
Why it's wrong here
Compression reduces storage footprint and I/O for batch scans; it allocates no workload groups or resource classes, so it cannot guarantee priority or prevent starvation. It would be the right choice when the goal is purely reducing storage and query I/O costs.
- ✓
Create workload groups for high-priority and low-priority queries, assigning appropriate importance and resource percentages
Why this is correct
Workload groups let you assign importance and a resource percentage per group, so high-priority queries pre-empt batch work while the batch group retains a guaranteed minimum, preventing starvation under the dedicated SQL pool's concurrency limits.
- ✗
Create materialized views for the batch job queries
Why it's wrong here
Materialised views precompute batch query results, cutting execution time but granting no concurrency slots or resource governance. It would be correct when batch queries repeatedly aggregate the same large tables and latency, not resource contention, is the problem.
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JA
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
This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.