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

A company has an Azure SQL Database that is experiencing performance degradation during peak hours. The database is configured with the Standard tier (S2). Which action should you recommend to improve performance without changing the application code?

⚠ Common exam trap

DP-300 often tests the difference between diagnosing a problem (Query Store, Performance Dashboard) and fixing it (scaling, indexing), so candidates who pick the diagnostic tool over the actual remediation lose the point.

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

✓

Scale up the database to a higher service objective (e.g., S3).

Scaling up the Azure SQL Database from S2 to a higher service objective (e.g., S3) increases the allocated DTUs/vCores, memory, and IOPS, directly addressing resource saturation during peak hours without requiring any application changes. This is the fastest, least invasive remediation when the workload is CPU/IO bound and the tier is the bottleneck. It preserves connection strings, schema, and code, making it the correct first recommendation.

Answer analysis

Option-by-option breakdown

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

  • ✓

    Scale up the database to a higher service objective (e.g., S3).

    Why this is correct

    Scaling up to S3 increases the allocated DTUs and associated compute, memory, and IO resources for the same database, relieving peak-hour contention without application changes. Vertical scaling within the Standard tier is the direct remedy when the current service objective is the bottleneck.

  • ✗

    Enable Query Store and run the Performance Dashboard.

    Why it's wrong here

    Query Store and the Performance Dashboard only capture and display query-level telemetry; they diagnose the cause but change no resource allocation, so peak-hour degradation persists. It is tempting because it is the standard first step in tuning, and would be correct when you must identify the offending queries before acting.

  • ✗

    Enable read scale-out to offload read queries.

    Why it's wrong here

    Read scale-out is unavailable on the Standard tier and only redirects read-only connections to a secondary replica, leaving the write workload on the primary. It is tempting because it genuinely relieves read pressure, and would be correct on Premium or Business Critical tiers with read-heavy workloads.

  • ✗

    Create nonclustered indexes on all tables.

    Why it's wrong here

    Indexes cannot be created on every table blindly; write-heavy tables gain insert, update and delete overhead, and the S2 tier's fixed DTUs remain the binding constraint. It is tempting because indexing does accelerate reads, and would be correct after identifying specific slow queries with missing-index recommendations.

Quick reference

AWS S3 Storage Class Comparison

Storage ClassMin DurationRetrievalUse Case
S3 StandardNoneImmediateFrequently accessed data
S3 Standard-IA30 daysImmediateInfrequent access, rapid retrieval
S3 One Zone-IA30 daysImmediateNon-critical infrequent data
S3 Intelligent-TieringNoneImmediate–hoursUnknown or changing access patterns
S3 Glacier Instant90 daysMillisecondsArchive with instant retrieval
S3 Glacier Flexible90 daysMinutes–hoursArchive, flexible retrieval
S3 Glacier Deep Archive180 daysHoursLong-term compliance archive

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JA

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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