DP-900 Practice Question: Identify considerations for relational data on Azure

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  DTU-based purchasing model

  Why it's wrong here

  The DTU-based purchasing model bundles compute, memory, and I/O into fixed performance units (DTUs) sold in Basic, Standard, and Premium service tiers, with no option to pause or automatically scale compute. This model was designed for predictable workloads with a steady baseline of usage, and because it bills at a flat, reserved rate regardless of actual consumption, an application that sees significant idle periods will pay for capacity it never uses. Although elastic database pools can share DTUs across multiple databases, they still do not provide the per-query or per-second auto-scaling and auto-pause behavior found in the serverless vCore tier. Therefore, it does not meet the cost-minimization requirement for a workload that has sharp demand peaks and long quiet stretches.

  When this WOULD be correct

  For a workload with predictable, steady-state performance requirements and no need for auto-scaling or pause, such as a legacy application with consistent usage patterns, the DTU model offers simplicity and lower cost.

• ✗

  vCore-based purchasing model with provisioned compute tier

  Why it's wrong here

  The vCore-based provisioned compute tier allocates a fixed set of resources (vCores and memory) that remain running continuously, regardless of actual workload. This fails the scenario because it cannot automatically scale down or pause during idle nights and weekends, nor scale up dynamically during peak hours—it requires manual resizing or scheduled scaling. This option is tempting because provisioned compute is designed for predictable, steady-state workloads where consistent performance is critical, making it correct for applications with stable, high-volume traffic that never idles.

  When this WOULD be correct

  A company needs predictable performance for a workload with steady, moderate transaction volume and wants to reserve compute capacity for a fixed cost, with the ability to scale manually or via Azure Automation for planned changes.

• ✓

  vCore-based purchasing model with serverless compute tier

  Why this is correct

  The vCore-based serverless compute tier is the right fit because it provisions compute capacity for Azure SQL Database that autonomously scales between a configurable minimum and maximum number of vCores based on demand. During periods of low or no activity—such as nights and weekends—the service can scale down to the minimum vCores or fully pause the database, after which billing for compute stops entirely while storage and backups continue to be charged at their own rates. When traffic returns, it resumes automatically, enabling peak-load handling without paying for idle capacity. This directly minimizes costs over a variable workload while preserving compatibility with the vCore architecture.

• ✗

  vCore-based purchasing model with Hyperscale service tier

  Why it's wrong here

  Hyperscale is designed for very large databases (over 1 TB) with high storage and read scalability. It does not offer auto-pause or compute auto-scaling based on idle periods, and it is more expensive for small to medium databases.

  When this WOULD be correct

  A company has a database exceeding 4 TB that requires rapid scaling for unpredictable, high-throughput workloads and needs fast recovery from backups. They choose the vCore-based Hyperscale tier to handle massive storage and high concurrency without storage size limitations.

Option-by-option analysis

Why each answer is right or wrong

Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The DP-900 exam frequently reuses these exact scenarios with slightly different constraints.

Analysis generated from the official DP-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”