Interview questions with answer breakdowns — Page 49 of 52

Browse page 49 of Offer.cc interview question and answer breakdowns with reasoning, implementation details, follow-ups, and public sources.

BackendHard

How Do You Design a Stable Cursor Pagination API?

Start with pagination semantics under concurrent writes, compare offset and cursor pagination, then derive a compound cursor, matching index, bidirectional queries, snapshot boundary, and adversarial tests.

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DataHard

Data Engineering Interview: Design an SCD Type 2 Dimension

Design a point-in-time-correct SCD Type 2 dimension with surrogate keys, half-open validity intervals, idempotent loads, delete semantics, late corrections, and invariant checks.

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CodingHard

Coding Interview: Implement a Bounded SPSC Lock-Free Ring Buffer

Implement a fixed-capacity single-producer single-consumer ring buffer and explain indexes, full/empty checks, memory ordering, and edge tests.

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System designHard

System Design Interview: Design a Real-Time Collaborative Editor

Build a collaborative editor around CRDT convergence, document rooms, a durable update log, and state-vector sync while handling instant local edits, offline reconnects, ephemeral cursors, permission revocation, and testable recovery.

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BackendHard

How Do You Diagnose and Optimize a Slow PostgreSQL Query?

Derive a multicolumn or partial index from workload evidence, execution plans, and cardinality estimates, then validate read gains against write amplification and rollout risk.

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DataHard

Data Engineering Interview: Design a Change Data Capture Pipeline

Derive a gap-free CDC pipeline from PostgreSQL WAL, initial snapshots, and source positions, then handle at-least-once delivery, deletes, schema evolution, replication-slot risk, replay, and reconciliation.

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CodingHard

How Do You Implement a Fenwick Tree for Dynamic Prefix Sums and Weighted Selection?

Implement point updates, prefix and range sums, and rank-by-weight selection with a compact one-array structure while handling indexing, complexity, and negative-weight limits.

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System designHard

System Design Interview: Design a Centralized Logging System

Design a centralized logging platform around local buffering, a durable stream, selective indexing, and object-storage archives so it survives log storms, downstream outages, high-cardinality fields, and noisy tenants.

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BackendHard

How Do You Prevent a Cache Stampede When a Hot Key Expires?

Design a cache read path with soft expiration, request coalescing, and distributed leases that survives hot-key expiry, refresher crashes, cache failure, and delayed writes.

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DataHard

Data Engineering Interview: How Do You Diagnose and Fix a Hot Kafka Partition?

Diagnose a Kafka hot partition from per-partition lag and key skew, calculate backlog growth, then choose throttling, safe intra-partition concurrency, a better key, and an ordering-safe migration.

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CodingHard

How do you implement a skip list and explain its expected O(log N) behavior?

Build a searchable, mutable ordered structure with random levels, then reason about expected complexity, edge cases, and memory trade-offs.

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System designHard

System Design Interview: Design a Cloud File Storage and Sync Service

Design a cloud drive with resumable chunk uploads, immutable file versions, cursor-based device sync, conflict copies, version recovery, and safe garbage collection.

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BackendHard

Prevent Write Skew with Transaction Isolation

Use an on-call roster invariant to derive the actual behavior of Read Committed, Repeatable Read, and Serializable, then compare serializable retries, explicit locking, and a single-row conditional update.

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DataMedium

How Do You Diagnose Sample Ratio Mismatch in an A/B Test?

Use a chi-square test to confirm Sample Ratio Mismatch, then trace assignment, execution, log processing, and analysis filters before trusting an apparently significant experiment lift.

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CodingHard

How do you implement Vose's alias method for O(1) weighted sampling?

Precompute probability and alias tables for constant-time sampling from a static discrete distribution, including updates, precision, and validation.

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System designHard

System Design Interview: Design a Distributed Lock Service

Design a distributed lock service around strongly consistent state, lease renewal, and fencing tokens so it remains safe through process pauses, partitions, lost responses, and leader failure.

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BackendHard

Design an Idempotent Create-Order API

Start with idempotency-key scope, request fingerprints, and a database uniqueness constraint to make order creation safe to retry, including concurrent requests, lost responses, key expiry, and downstream side effects.

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DataMedium

SQL Interview: Find the Top Three Products per Category

Aggregate order items to product grain, use DENSE_RANK to preserve revenue ties, and explain window-function order, edge cases, correctness, and validation.

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CodingHard

How do you implement a Count-Min Sketch for streaming frequency estimates?

Use a two-dimensional counter array and independent hashes to estimate key frequencies in fixed memory, including error bounds, merges, and reset limits.

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System designHard

System Design Interview: Design a Large-Scale Web Crawler

Derive a host-sharded URL frontier from the crawl budget, then handle robots.txt, politeness, deduplication, recrawling, and lease recovery with testable capacity and failure invariants.

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DataMedium

How Do You Monitor an ML Model in Production?

Use a ride-hailing ETA regressor to build layered monitoring for serving health, data quality, distribution shifts, and delayed-label quality, then map each signal to an actionable alert, rollback, or retraining decision.

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CodingMedium

How can HyperLogLog estimate distinct values in a massive stream?

Estimate streaming cardinality with fixed memory by deriving HyperLogLog register updates, error corrections, and distributed merge boundaries.

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System designHard

How Would You Design a Feature Flag System?

Design a low-latency feature flag platform by separating its management control plane from in-process evaluation, then make rollout assignment, stale-data behavior, governance, and failure recovery explicit.

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DataHard

How Do You Evaluate a Recommendation System Offline and Online?

Build a launch decision from point-in-time logged data, ranking and beyond-accuracy metrics, exposure bias, and a randomized online experiment, then diagnose why an offline win can lose online.

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