DP-900 Describe an analytics workload on Azure Practice Question
A company uses Azure Synapse Analytics to run large-scale data transformations. They need to optimize costs for predictable workloads that run every night. Which Azure feature should they configure?
⚠ Common exam trap
Test-takers frequently confuse auto-scale (which scales compute up/down while running) with pause/resume (which stops compute entirely), failing to recognize that predictable idle periods benefit from complete compute suspension rather than dynamic scaling.
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 dedicated SQL pool pause and resume
Azure Synapse Analytics dedicated SQL pool supports pause and resume, which stops compute billing while preserving data in storage. For predictable nightly workloads, pausing the pool during idle hours eliminates compute costs, then resuming it before the job runs. This directly optimizes cost for scheduled, non-continuous workloads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement dedicated SQL pool pause and resume
Why this is correct
Pause/resume is the most direct cost optimization because it completely stops the dedicated SQL pool's compute engine, so billing for compute hours drops to zero while storage and data remain intact. For workloads that run on a predictable schedule, such as nightly ETL or business-hours reporting, you can automate pauses and resumes with Azure Automation or pipelines. This approach eliminates the need to pay for idle compute, unlike scaling down which still bills for running compute.
- ✗
Enable always-on availability
Why it's wrong here
Always On availability is a high-availability and disaster-recovery feature that maintains synchronous secondary replicas, often across availability zones. It requires additional compute and storage resources for those replicas and incurs ongoing replication work, which increases cost instead of lowering it. This is a fault-tolerance and continuity capability, not a cost-control mechanism, so it is incorrect for the scenario.
- ✗
Enable data compression on tables
Why it's wrong here
Enabling data compression on tables reduces physical storage footprint and can improve I/O performance by packing more rows per page, but it does not lower the compute cost of running a dedicated SQL pool. In Synapse Analytics, compute and storage are billed separately; compression affects storage and can actually add CPU overhead to query operations. Therefore, while it may cut storage expenses, it does not address the goal of reducing compute cost for large-scale workloads.
- ✗
Configure auto-scale
Why it's wrong here
Auto-scaling dynamically adjusts the service level (DWU) in response to workload demand, but the pool remains running and continues to bill for whatever compute capacity is allocated. For a predictable workload, scheduled pause/resume achieves zero compute cost during idle periods, which auto-scale cannot match because it never pauses. Additionally, auto-scale only helps when demand varies unpredictably; it is not the optimal answer here.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
Key term
Dedicated SQL pool
A Dedicated SQL pool is a cloud-based analytics service in Azure Synapse Analytics that provides a managed, scalable environment for running large-scale data warehousing queries using Transact-SQL.
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