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How to merge analytics and CRM data to auto-segment users by persona
This tests multi-source data unification and identity resolution. A strong answer covers ID stitching, a unified profile, deterministic or ML segmentation, and data quality.
Design backend logic for personalized hero by industry
Tests multi-signal segmentation and graceful degradation. Strong answers list explicit profile data, IP or domain enrichment, behavioral inference, and a default fallback. Red flag: proposing heavy ML for a binary rule or skipping fallback entirely.
Build a competitor sitemap scraper for H1 and word count
Tests architecture of a polite, resilient scraper with clear legal guardrails. Strong answers cover throttled async fetches, robots.txt checks, HTML parsing, and copyright review. Red flag: ignoring rate limits, retry logic, or legal risk entirely.
Outline an NLP pipeline to categorize reviews and identify pain points
Tests text mining pipeline design. Answers cover deduplication and normalization, then clustering or topic modeling for categories, plus sentiment analysis to rank pain points. Red flag: skipping validation or jumping to LLMs without cleaning.
Design a next-best-article recommender
Content-based on article embeddings, collaborative on behavior, a hybrid blend, popularity or onboarding fallbacks for new users.
Propose a multi-armed bandit system to optimize headlines faster
This tests online learning and the explore-exploit tradeoff. Answers contrast fixed A/B with adaptive allocation, sketch a Bayesian bandit service with a min exploration rate, and note delay.
How would you design a centralized copy service for consistent UI text?
This tests separation of UI and content. A strong answer uses a key-value map with semantic IDs, a lookup hook, and context injection so components call copy.button.save. Red flag: raw strings in JSX or a database for static English-only copy.
How would you architect a white-label content system for multiple brand voices?
Tests content-presentation separation for multi-tenant apps. Strong answers use a headless CMS with tenant keys, a domain resolver for brand voice, and shared components with injected strings. Red flag: duplicate codebases or hard-coded copy per client.
How would you automate forbidden-word checks in CI/CD?
This tests embedding brand governance into engineering workflows. A strong answer covers AST-aware string extraction, CI gating with severity levels, and allowlisting to cut false positives.
Design a headless CMS model for brand voice metadata and API usage
Structured content beyond page blobs. Strong answers validate tone, context, and character limits in the CMS; expose them via API filters; and let clients render context-aware components.
Design a secure templating engine for user notifications
Tests balancing creator flexibility with defense-in-depth security and i18n. Strong answers cover context-aware auto-escaping, a restricted AST grammar, ICU MessageFormat for pluralization, and sandboxed execution.
Propose an NLP approach to audit brand voice and build a dashboard
This tests turning brand principles into measurable NLP features. A strong answer uses readability for simplicity and agency verbs for empowerment, scores copy via LLM classifiers, and visualizes trends by product area.
Design a database schema for a blog with posts, authors, and tags
Tests normalization and many-to-many design. A strong answer covers a users table with role enum, a posts table referencing user_id, and a post_tags join table for categories. Red flag: storing tags as a comma-separated string in the posts table.
Data model for an editorial calendar
A content entity with publish_date and a status enum, a foreign key to authors, indexes on date and status.
Design a content versioning system with history and revert
Versions table with article_id, version_num, content_snapshot, timestamp; APIs for createVersion, getHistory, restoreVersion.
Design architecture for multi-channel article distribution from a single source of truth
Tests separation of content and presentation via headless CMS. Strong answers cite a central structured CMS, content API, channel-specific rendering layers, and webhook push. Red flag: manual duplication or direct database sharing without API abstraction.
How would you technically approach building a related articles feature?
Tests decomposition of a content recommender into data, similarity, and serving layers. Strong answers use TF-IDF, embeddings, or tag overlap with caching and cold-start fallbacks.
Design a highly scalable headless CMS architecture
Tests write-heavy authoring versus read-heavy delivery separation at scale. Strong answers outline dual-cluster isolation, event-driven invalidation, and CDN edge caching. Red flag: a monolithic service with only database query caching.
Architect a large-scale real-time recommendation system with data pipelines
Tests multi-stage ML serving under 200ms latency. Strong answers use a funnel: two-tower embeddings with ANN retrieval, ranking, and guardrails, plus separate batch and real-time pipelines. Red flag: scoring the full catalog per request without approximation.
Design a system to automate the content lifecycle
This tests time-based state modeling and scheduler choice at scale. A strong answer names a workflow engine or cron-plus-queue, separates analytics ingestion, and requires idempotency. Red flag: a single cron script with no backpressure or failure handling.