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DP-300 Practice Question: Monitor, configure, and optimize database resources

Which TWO metrics are available in Azure Monitor for an Azure SQL Database that can be used to set autoscale rules? (Select two.)

⚠ Common exam trap

DP-300 often tests which metrics are available for autoscale — candidates pick diagnostic metrics like deadlock count or Query Store size, which are not autoscale triggers.

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

✓

CPU percentage

Option A (CPU percentage) is correct because Azure SQL Database exposes the 'cpu_percent' metric through Azure Monitor, which reports the average CPU utilization of the database and is a standard metric used to drive autoscale rules (for example, scaling up when CPU exceeds a threshold). Option D (DTU percentage) is correct because the 'dtu_consumption_percent' metric measures the percentage of the Database Transaction Unit limit consumed and is one of the most commonly used metrics for autoscale decisions on DTU-based databases. The unmarked options do not belong: Log write throughput is not a directly exposed Azure Monitor metric for autoscale on Azure SQL Database, Deadlock count is a diagnostic/query-level statistic rather than an autoscale metric, and Query Store size is an internal Query Store property, not an Azure Monitor metric available for autoscale rules.

Answer analysis

Option-by-option breakdown

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

  • ✓

    CPU percentage

    Why this is correct

    CPU percentage is a native Azure Monitor metric for Azure SQL Database, reflecting average compute utilisation. It satisfies the autoscale requirement because Azure Monitor autoscale rules can trigger scale actions directly from this metric, unlike log-based or non-emitted measures.

  • ✗

    Log write throughput

    Why it's wrong here

    Log write throughput measures transaction log I/O, not a resource that autoscale rules act on; Azure SQL autoscale targets CPU, DTU/vCore or storage, not log throughput. It is tempting because log throughput is a genuine Azure Monitor metric, useful for diagnosing write-heavy workloads, but it cannot drive scaling decisions.

  • ✗

    Deadlock count

    Why it's wrong here

    Deadlock count is a diagnostic counter surfaced for troubleshooting concurrency, not a capacity metric Azure Monitor autoscale rules evaluate; those act on CPU, DTU/vCore or storage. It is tempting because deadlocks indicate contention, but they reflect workload behaviour rather than resource pressure requiring scale-out.

  • ✓

    DTU percentage

    Why this is correct

    DTU percentage is a native Azure Monitor metric for Azure SQL Database, expressing consumption of the blended Database Transaction Unit. It satisfies the autoscale requirement because Azure Monitor autoscale rules can scale directly on this metric, which is emitted for DTU-based databases.

  • ✗

    Query Store size

    Why it's wrong here

    Query Store size reports internal plan-cache storage consumption, which Azure Monitor does not expose as a scaling signal; autoscale rules respond to CPU, DTU/vCore or storage usage. It is tempting because Query Store tuning is a real performance task, yet its size is diagnostic, not an autoscale trigger.

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Written and reviewed by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

Last reviewed September 2026 · checked against the official Microsoft exam blueprint

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