AZ-204 Practice Question: Monitor, troubleshoot, and optimize Azure solutions
You are optimizing an Azure SQL Database that runs a heavy reporting workload. Queries are slow due to high logical reads. Which index strategy should you recommend?
⚠ Common exam trap
The trap here is that candidates might focus on specific query optimizations like covering indexes (Option B) to eliminate key lookups. However, for a heavy reporting workload with general high logical reads, columnstore indexes (Option A) offer a more comprehensive and impactful solution by optimizing data storage and processing for analytical queries, reducing the overall volume of data read.
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
✓
Use columnstore indexes on fact tables
High logical reads in a heavy reporting workload are often best addressed by columnstore indexes. Columnstore indexes provide significant data compression, reducing the number of pages that need to be read from disk. They also enable batch mode processing and segment elimination, which dramatically improve performance for analytical queries involving large scans and aggregations, thereby directly reducing logical reads across the workload.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use columnstore indexes on fact tables
Why this is correct
Columnstore indexes are primarily designed for data warehousing and analytical workloads, excelling at compressing and scanning large datasets for aggregations. For point queries, which retrieve specific rows, the overhead of decompressing and processing individual row groups can actually increase logical reads compared to traditional row-store indexes. They are not optimized for the rapid, single-row lookups typical of heavy read OLTP scenarios, making them unsuitable for directly reducing logical reads for such queries.
- ✗
Create nonclustered indexes with included columns
Why it's wrong here
Creating nonclustered indexes with included columns is highly effective for reducing logical reads by creating covering indexes. A covering index contains all columns required by a query, either in its key or as included columns, allowing the database engine to satisfy the query entirely from the index structure. This eliminates the need to access the underlying clustered index or heap, thereby minimizing disk I/O and improving query performance significantly for heavy read operations.
- ✗
Disable auto-update statistics
Why it's wrong here
Disabling auto-update statistics is detrimental to performance optimization as it prevents the SQL Server query optimizer from having current information about data distribution. Stale statistics lead to inaccurate cardinality estimates, causing the optimizer to generate inefficient execution plans that may perform unnecessary table or index scans instead of optimal seeks. This directly results in a higher number of logical reads and degraded query performance, especially in databases with frequent data modifications.
- ✗
Rebuild clustered indexes with higher fill factor
Why it's wrong here
Rebuilding clustered indexes with a higher fill factor primarily impacts storage efficiency and write performance by reducing page splits during data modifications. While a higher fill factor can reduce the total number of pages for an index, its direct impact on reducing logical reads for specific queries on *existing data* is minimal compared to index design. For read-heavy workloads, the structure and covering capabilities of an index are far more critical than fill factor for minimizing logical I/O.
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Written by Johnson Ajibi, MSc IT Security
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