Quick Answer
The answer is auto-pause and auto-resume, which is the correct feature to configure for automatic scaling in Azure Synapse Analytics. This works because the dedicated SQL pool can automatically scale compute resources by pausing them when idle and resuming on demand, effectively adjusting capacity without manual intervention. For the DP-900 exam, this tests your understanding of cost optimization and compute management in Synapse, often appearing as a scenario where a company needs to handle variable workloads efficiently. A common trap is confusing auto-scale with manual scaling or thinking it applies to serverless SQL pools—auto-pause and auto-resume is specific to dedicated SQL pools and focuses on pausing compute, not dynamically adjusting DWU tiers. Remember the memory tip: "Pause saves cash, resume handles the dash"—if the workload is unpredictable, auto-pause and auto-resume is the cost-effective scaling solution.
This DP-900 practice question tests your understanding of describe an analytics workload on azure. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company uses Azure Synapse Analytics to run large-scale analytics on sales data. They need to ensure that the workload can automatically scale based on demand without manual intervention. What feature should they configure?
Auto-pause and auto-resume
Enables automatic scaling and cost optimization by pausing when idle and resuming on demand.
Read Scale-out replicas
Why wrong: Provides read-only replicas for query offloading, not scaling compute capacity.
Elastic Database Pools
Why wrong: Used in Azure SQL Database for pooling resources, not for Synapse Analytics.
Manual scale-up during peak hours
Why wrong: Requires manual intervention, not automatic scaling.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Auto-pause and auto-resume
Auto-pause and auto-resume is the correct feature because Azure Synapse Analytics (dedicated SQL pool) supports automatic scaling through the ability to pause the compute resources when idle and resume them on demand, which effectively scales the workload based on demand without manual intervention. This feature reduces costs by stopping compute billing during inactivity and automatically resumes when a query or activity is detected, meeting the requirement for automatic scaling.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Auto-pause and auto-resume
Why this is correct
Enables automatic scaling and cost optimization by pausing when idle and resuming on demand.
Related concept
Read the scenario before looking for a memorised answer.
Read Scale-out replicas
Why it's wrong here
Provides read-only replicas for query offloading, not scaling compute capacity.
Elastic Database Pools
Why it's wrong here
Used in Azure SQL Database for pooling resources, not for Synapse Analytics.
Manual scale-up during peak hours
Why it's wrong here
Requires manual intervention, not automatic scaling.
Common exam traps
The trap here is that candidates confuse auto-pause/auto-resume with manual scaling or other database scaling features (like read replicas or elastic pools) that are specific to Azure SQL Database, not Azure Synapse Analytics.
Detailed technical explanation
Under the hood, auto-pause and auto-resume in Azure Synapse Analytics works by deallocating the compute nodes (the SQL pool's compute resources) after a configurable period of inactivity (default 6 hours, minimum 1 hour), stopping billing for compute while storage remains. When a new query or activity arrives, the service automatically re-provisions the compute nodes, which can take a few minutes, making it ideal for intermittent or unpredictable workloads where cost savings are prioritized over immediate query response. A subtle behavior is that auto-resume is triggered by any operation that requires compute, including login attempts or metadata queries, not just actual data queries.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
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FAQ
Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..
The correct answer is: Auto-pause and auto-resume — Auto-pause and auto-resume is the correct feature because Azure Synapse Analytics (dedicated SQL pool) supports automatic scaling through the ability to pause the compute resources when idle and resume them on demand, which effectively scales the workload based on demand without manual intervention. This feature reduces costs by stopping compute billing during inactivity and automatically resumes when a query or activity is detected, meeting the requirement for automatic scaling.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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