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Explain a conversion pixel, click-to-signup flow, and failure points
Tests cross-session attribution state. Strong answers trace the GCLID from URL parameter to cookie or local storage, then to the conversion tag, plus list failure modes like ITP, ad blockers, and race conditions.
A/B test copy in a native app without app store releases
Tests native release friction versus web. Strong answers cite app store review, binary caching, and stale config; then remote fetch, local defaults, and user targeting. A red flag is assuming updates are instant like the web.
How would you architect personalized email and coupon delivery at scale?
This tests decoupled rendering and atomic coupon reservation. A strong answer uses template rendering, atomic coupon reservation, an idempotent queue, and batched ESP delivery. Red flag: generating coupons during SMTP without reservation risks overspend.
How would you instrument a CTA button and describe its event payload?
This tests basic event instrumentation and payload design. Mention a click listener, a gtag call with an event name, and parameters like button_text and page_path. A red flag is a bare event name with no parameters or mixing pageviews and clicks.
Describe how you'd implement an A/B test for a landing page headline
Mention deterministic bucketing, anti-flicker rendering, tracking with variant IDs, and sample-size planning.
How would you engineer pSEO templating and data integration to prevent duplication?
Architecting data-templating pipelines that enforce genuine uniqueness at scale. A strong answer hits modular variable-rich blocks, canonical and noindex guardrails, and similarity monitoring.
Design an algorithmic E-E-A-T scoring system
Tests turning subjective quality into signals. Split E-E-A-T into distinct feature families, combine structured metadata with unstructured NLP and graph signals, and calibrate against human rater labels. Red flag: one opaque score or CTR as trust proxy.
Design a system to detect keyword cannibalization
Query-page-position data, intent clustering, and rank volatility.
Design a content gap tool: data sources and core logic
Tests system design for SEO pipelines. Strong answers cite APIs (Ahrefs, Semrush, GSC), explain normalization, and frame logic as a left-anti-join on keyword plus geo and device filtered by rank. Red flag: dismissing API cost, rate limits, and freshness.
How would you use NLP to analyze top articles for semantic themes?
This tests practical NLP pipeline design for SEO. A strong answer covers preprocessing, NER with spaCy or BERT, topic modeling with LDA or BERTopic, and semantic similarity via embeddings.
Design a system to suggest internal links for new articles
This tests large-scale retrieval design. A strong answer uses an offline embedding index for candidate retrieval and a real-time ranker scoring topical overlap and anchor fit. Red flag: scanning the full corpus per request or omitting index architecture.
What critical on-page SEO elements would you extract from HTML?
Tests if you know which HTML elements search engines use to index pages. Strong answer: title tag, H1, and meta description shape search results and topical relevance. Red flag: claiming meta descriptions directly affect rankings or conflating H1 with title.
Differentiate informational, navigational, and transactional intent and classify keywords programmatically
Define three intents by user goal; regex-match modifiers like what/how vs buy/near; check SERP features.
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.
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 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.
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 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.
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 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.