Indexing Strategy and Selection Questions

Covers index design principles and practical selection of indexes to accelerate queries while managing storage and write cost. Topics include index types such as B tree hash and bitmap indexes and full text and functional indexes; single column composite and covering indexes; clustered versus nonclustered index architectures and partial or filtered indexes. Candidates should reason about index selectivity and cardinality and how statistics and histograms influence optimizer choices. Also assess index maintenance overhead fragmentation and rebuild strategies and the trade off between faster reads and slower inserts updates and deletes. Practical skills include reading execution plans to identify missing or inefficient indexes proposing index consolidation or covering index designs testing and benchmarking index changes and understanding interactions between indexing partitioning and denormalization.

EasyTechnical

0 practiced

Explain the difference between clustered and nonclustered indexes and describe three situations a Business Intelligence Analyst should prefer a clustered index versus a nonclustered index when modeling reporting schemas and OLAP queries. Include implications for storage, range scans, and sort operations.

MediumTechnical

0 practiced

Show how you would create a covering index in SQL Server for this dashboard query:

SELECT customer_id, order_date, total_amount FROM orders WHERE order_date >= '2025-01-01' AND status = 'shipped' ORDER BY total_amount DESC LIMIT 100;

Explain which columns should be key columns and which should be included, and why.

EasyTechnical

0 practiced

List the common index types a BI analyst should know (B-tree, hash, bitmap, full-text, functional/expression) and provide a one-sentence description plus a typical BI use-case for each (e.g., date range filters, equality joins, low-cardinality filters, product-descriptions search, case-insensitive joins).

MediumTechnical

0 practiced

Explain how database histograms and statistics influence the optimizer's decision to use an index. Provide a concrete example where stale or default statistics cause the optimizer to choose a table scan and describe steps to correct the problem.

HardTechnical

0 practiced

For columnar analytical databases (e.g., Redshift, BigQuery, Snowflake), indexing concepts differ from row stores. Explain how sortkeys, clustering, and partitioning map to traditional index concepts and describe a migration plan from a row-store with many indexes to a columnar store while preserving dashboard performance.

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