DP-203 Practice Question: Secure, monitor, and optimize data storage and data processing
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?
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
✓
Configure workload management with workload groups and classifiers
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.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Scale the dedicated SQL pool to a higher DWU level
Why it's wrong here
Scaling to a higher DWU adds compute capacity but does not resolve resource class conflicts, because concurrency slots and per-class memory allocations scale with it only proportionally. Scaling suits sustained workload growth. The stem's queuing from class conflicts calls for workload management and resource class configuration.
- ✓
Configure workload management with workload groups and classifiers
Why this is correct
Workload groups with classifiers assign queries to resource buckets based on rules, giving each workload dedicated memory and concurrency. This reduces resource class conflicts and queuing by preventing competing queries from contending for the same resources.
- ✗
Create materialized views for the most common aggregations
Why it's wrong here
Materialised views precompute aggregations to speed repeated queries; they do not alter resource class allocation or concurrency slots, so queued queries remain queued. They suit recurring heavy aggregations over large fact tables. The stem's resource class conflicts require workload management configuration instead.
- ✗
Enable result-set caching for frequently run queries
Why it's wrong here
Result-set caching reuses identical query output, so it cannot resolve resource class conflicts, which arise from insufficient memory grants per query. It suits repeated identical read queries on static data, where it avoids re-execution entirely.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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