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Home/Questions/SQL/Indexing - True/False question on indexes and query optimization

Indexing - True/False question on indexes and query optimization

SQLhard0.6 min read

Reviewed by Aditya Kumar · Last reviewed 2026-03-24

Indexing true/false: TRUE—Indexes speed up SELECT with WHERE/JOIN on indexed columns; indexes can slow down INSERT/UPDATE/DELETE (writes must update index); unique indexes enforce uniqueness; composite indexes support left-prefix queries. FALSE—Indexes always improve all queries...

🤖 Analyze Your Answer
Frequency
Low
Asked at 1 company
Category
487
questions in SQL
Difficulty Split
130E|271M|86H
in this category
Total Bank
1,863
across 7 categories
Key Concepts Tested
joinoptimization

Why This Question Matters

This hard-level SQL question appears frequently in data engineering interviews at companies like Myntra. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (join, optimization) will help you answer variations of this question confidently.

How to Approach This

This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.

Expert Answer
111 words

Indexing true/false: TRUE—Indexes speed up SELECT with WHERE/JOIN on indexed columns; indexes can slow down INSERT/UPDATE/DELETE (writes must update index); unique indexes enforce uniqueness; composite indexes support left-prefix queries. FALSE—Indexes always improve all queries (no: only relevant columns); more indexes always better (no: diminishing returns, write cost); indexes work on all data types equally (some types less effective). Best practice: index columns used in filters and joins; avoid indexing low-cardinality columns (e.g., boolean); monitor index usage and drop unused indexes. Why it matters: Design choices compound at scale—wrong approach can cause 100× overhead. Scalability trade-offs: Profile before optimizing; validate on sample then full. Cost implications: Suboptimal choices multiply at billion-row scale.

⚡
Pro Tip

Red Flag: Optimizing without EXPLAIN or baseline. Pro-Move: 'EXPLAIN ANALYZE + composite index cut P99 80%; we measured write impact before rollout.'

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According to DataEngPrep.tech, this is one of the most frequently asked SQL interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.

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