DP-203 Dedicated SQL pool scaling (DWU) Practice Question
Your company uses Azure Synapse Analytics to run a large-scale batch processing job every night. The job currently runs on a dedicated SQL pool and takes 4 hours. Management wants to reduce the runtime to under 2 hours without increasing cost. The job involves heavy compute operations with no data movement limitations. What should you do?
⚠ Common exam trap
Candidates often assume that increasing DWU always increases cost, but total cost can remain constant when runtime decreases proportionally. They may also mistakenly believe materialized views or result-set caching can significantly speed up a large, non-repetitive batch job.
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
✓
Increase the service level objective (DWU) of the dedicated SQL pool.
Increasing the service level objective (DWU) of the dedicated SQL pool provides more compute resources, directly reducing the runtime of compute-heavy batch jobs. Since the pool runs for fewer hours, the total cost (DWU × hours) may remain the same or even decrease, meeting the requirement to reduce runtime without increasing cost. Materialized views (A) and result-set caching (D) benefit repeated queries, not a unique nightly job. Workload management (C) only prioritizes resources, without increasing overall compute power.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create materialized views on frequently queried tables.
Why it's wrong here
Materialised views precompute and store query results, speeding repeated reads, but they do not accelerate the heavy compute operations of a batch job. They are tempting because they reduce query latency, yet the requirement is faster compute, which is addressed by scaling the dedicated SQL pool's DWUs.
- ✓
Increase the service level objective (DWU) of the dedicated SQL pool.
Why this is correct
Dedicated SQL pool compute scales linearly with data warehouse units, so doubling DWU from the current level roughly halves the four-hour runtime to about two hours. Because DWU is billed per hour, the higher rate applies for half the duration, keeping total cost broadly unchanged.
- ✗
Implement workload management to prioritize the job.
Why it's wrong here
Workload management allocates resources among concurrent queries; it cannot accelerate a single job's compute throughput. The nightly batch already runs alone, so prioritising it redistributes nothing. This feature suits environments where mixed workloads contend for fixed resources and important queries starve — not a scenario needing raw parallel compute scaling.
- ✗
Enable result-set caching on the dedicated SQL pool.
Why it's wrong here
Result-set caching only returns previously computed query results, so it cannot accelerate a first-run nightly batch whose data changes each execution. It is tempting because caching genuinely reduces latency for repeated identical queries against static data, which is its intended purpose. Here, the requirement is faster compute, which caching does not provide.
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