DP-300 Practice Question: Monitor, configure, and optimize database resources
You are optimizing an Azure SQL Database that runs a reporting workload. The database is in the General Purpose tier. You notice that many queries are performing table scans on large tables. Which TWO actions would most likely improve query performance without increasing costs?
⚠ Common exam trap
DP-300 often tests the trade-off between performance and cost, tempting candidates to choose tier upgrades or MAXDOP changes when cost-neutral options like indexing and statistics are correct.
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
✓
Update statistics on the tables.
Option A is correct because stale or missing statistics prevent the query optimizer from accurately estimating cardinality, often forcing scans instead of seeks; running UPDATE STATISTICS (or relying on auto-update statistics) gives the optimizer better row-count estimates and can produce more efficient plans at no extra cost. Option E is correct because creating nonclustered indexes on the columns referenced in WHERE clauses gives the optimizer a covering or seekable access path, converting full table scans on large tables into index seeks or scans of a much smaller structure, which directly improves reporting query performance without changing the service tier. Option B is not appropriate because upgrading to Business Critical increases cost, violating the 'without increasing costs' constraint. Option C is not appropriate because raising MAXDOP to 8 changes parallelism for the whole workload and does not address the root cause of scans, and it can even hurt performance or increase resource usage. Option D is not appropriate because enabling automatic tuning may create or drop indexes and force plans, but it is not a guaranteed, immediate fix for the observed scans and does not by itself ensure the specific WHERE-clause columns are indexed.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Update statistics on the tables.
Why this is correct
Updating statistics gives the query optimiser accurate cardinality estimates, so it can choose index seeks or better join orders instead of scanning large tables. This directly addresses the stem's table-scan symptom while remaining within the existing General Purpose tier, satisfying the no-added-cost constraint.
- ✗
Upgrade to Business Critical tier.
Why it's wrong here
Business Critical tier adds local SSD and in-memory OLTP replicas, but it raises cost, violating the no-cost-increase constraint. It is tempting because it genuinely accelerates I/O-bound reporting workloads, yet the scenario's requirement is improvement within the existing General Purpose tier's spend.
- ✗
Increase MAXDOP to 8.
Why it's wrong here
MAXDOP governs intra-query parallelism, so raising it to 8 can speed individual scans but does not eliminate them or add supporting indexes. It is tempting because parallelism genuinely helps large scans, yet the stem's scan problem stems from missing indexes, which MAXDOP cannot address.
- ✗
Enable automatic tuning.
Why it's wrong here
Automatic tuning applies plan forcing and index creation based on regression detection, but it does not rewrite scan-heavy reporting queries or add missing indexes on demand. It is tempting because it genuinely improves performance without cost, yet it targets plan regressions rather than the missing-index cause here.
- ✓
Create nonclustered indexes on columns used in WHERE clauses.
Why this is correct
Nonclustered indexes store a separate, ordered copy of the key columns, letting the engine seek directly to matching rows instead of scanning the whole table. This directly removes the table scans identified in the stem, and indexes consume no additional Azure SQL Database cost in the General Purpose tier.
Go deeper
Related to this question
Learn chapter
Monitoring Database Performance with Azure Tools
Key term
Azure SQL Performance Tuning
Azure SQL Performance Tuning is the process of optimizing the speed and efficiency of queries and database operations in Microsoft Azure SQL Database or SQL Managed Instance to reduce latency and improve throughput.
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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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