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.

HardTechnical

51 practiced

Describe advanced remedies when optimizer chooses a nested-loop plan due to histogram skew on a highly-skewed column: options include increasing histogram resolution, creating filtered statistics, using extended statistics (multi-column), rewriting predicates, or enforcing join hints. Discuss trade-offs and long-term maintenance implications.

HardSystem Design

79 practiced

Design an automated monitoring and alerting strategy for index health in a BI environment. Which metrics (e.g., fragmentation, last-used timestamp, index bloat, stale stats, lock waits) would you collect, what thresholds would trigger alerts, and what automated remediation (or human-driven) steps would you recommend?

EasyTechnical

36 practiced

Explain the difference between an index scan (index seek) and a sequential scan. Given an example query that filters on a non-indexed column and takes 30s, how would you explain to a non-technical stakeholder why adding an index might reduce report latency?

MediumTechnical

44 practiced

When would a bitmap index be beneficial in a data warehouse environment? Contrast its behavior with a B-tree index for a low-cardinality column such as 'gender' or 'region' and discuss update concurrency and storage trade-offs.

MediumTechnical

42 practiced

Explain functional (expression) indexes and provide a PostgreSQL example creating an index on lower(email) to support case-insensitive joins and filters. When would you prefer this over normalizing data to a lowercased column?

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