Question 1

Design a recommendation system that generates a personalized homepage for each of Netflix's 200 million subscribers. The page must load in under 200 milliseconds. Walk me through the offline training pipeline, the online serving layer, and how you handle cold-start for a new subscriber.

Accepted Answer

A strong answer separates the offline batch pipeline (collaborative filtering or two-tower neural models trained on viewing history) from the online serving layer (pre-computed embeddings cached in Redis, real-time context like time of day and device blended at request time). The candidate should cover feature stores, the ranking layer that merges multiple candidate generators, A/B experimentation on ranking, and a cold-start strategy using popularity signals and onboarding questions.

Question 2

Design the end-to-end system behind a feature like Discover Weekly: a personalized playlist of 30 songs generated once a week for roughly 600 million users. Walk me through the data pipeline, the model serving layer, and how you would keep recommendations fresh and relevant without regenerating everything from scratch each week.

Accepted Answer

A strong answer separates offline training from online serving and explains the batch pipeline that computes user embeddings from listening history. It covers the candidate generation stage (collaborative filtering, content embeddings), the ranking stage, and how diversity constraints prevent filter-bubble playlists. It discusses the cold-start problem for new users and new tracks, and addresses how to store and serve 600 million playlists efficiently using object storage plus a CDN-like precompute strategy.

Question 3

Design a system that identifies the top 10 trending topics on X in near real-time, refreshing every 30 seconds, at a scale of 500,000 tweets per minute. Walk me through data ingestion, aggregation, and how you would handle the thundering-herd problem when a major event causes a sudden spike.

Accepted Answer

A strong answer sketches a Kafka ingestion layer, stream processors (Flink or Spark Streaming) using a Count-Min Sketch or a sliding-window heavy-hitter algorithm to approximate top-K, and a periodic snapshot to Redis Sorted Sets. The candidate should address locality (trending should be geo-scoped), deduplication of retweets, and how a load-shedding strategy prevents the aggregation tier from collapsing under a viral event.

Question 4

We need to score transactions for fraud within 50ms. Features include user 30-day spend history, merchant risk score, and device fingerprint. Design a feature store that serves these features with low latency.

Accepted Answer

The candidate should separate the offline store (batch-computed features in a data warehouse or object store) from the online store (low-latency key-value retrieval via Redis or DynamoDB). A feature pipeline computes aggregates in batch (30-day spend) and writes to both stores; a point-in-time correct training dataset is assembled from the offline store using the event timestamp. They should discuss cache invalidation, TTLs for spend aggregations, and how to handle cold starts for new users.

Question 5

Your company runs 100 concurrent experiments across web and mobile. Design the system that handles experiment assignment, event logging, and statistical analysis at scale.

Accepted Answer

Key components: an assignment service that deterministically hashes user_id plus experiment_id to a bucket (consistent hashing ensures the same user always gets the same variant), an event collection pipeline (Kafka to a data warehouse like BigQuery or Snowflake), and an analysis layer that computes metrics on demand. The candidate should discuss experiment isolation (one user can be in multiple experiments only if orthogonal), holdout groups, and how to support sequential testing to allow peeking safely.

Question 6

Design an end-to-end recommendation system for a video platform with 50 million users and 5 million videos. It must serve recommendations within 100ms.

Accepted Answer

Two-stage architecture: (1) candidate retrieval using approximate nearest neighbor search (Faiss or ScaNN) on user and item embeddings generated by a two-tower model trained offline; (2) ranking with a point-wise or pairwise model that takes the top 500 candidates and scores them with richer features. Candidate embeddings are pre-computed and indexed. The candidate should discuss how to incorporate real-time signals (last 3 watched videos) via user embedding updates and cold-start handling for new videos.

Question 7

We need to ingest 10TB of raw transaction logs daily from S3, transform them into analytics tables, and make them available to analysts by 6am. Design this pipeline with failure recovery in mind.

Accepted Answer

The candidate should propose a DAG-based orchestrator (Airflow, Prefect, or dbt with a scheduler), partitioned S3 ingestion using Spark or dbt with incremental models, and idempotent transformation steps so reruns are safe. They should discuss data quality checks (Great Expectations or dbt tests) as a gate before writing to production tables, SLA monitoring with alerting, and a backfill strategy for catching up after failures.

Question 8

Your team trains 20 models per week across 5 product areas. Design a system to track experiments, manage model versions, and deploy models to production with canary releases.

Accepted Answer

The candidate should describe a model registry (MLflow or custom) that stores model artifacts, metrics, hyperparameters, and lineage to training data. Deployment pipeline: CI triggers on model promotion, builds a Docker container with the model, and deploys to a serving layer (BentoML, Seldon, or a custom FastAPI service) behind a feature flag system that controls traffic split for canary testing. Rollback is triggered by automated metric monitoring comparing canary to baseline.

Question 9

Design a system that predicts user churn weekly, scores all 5 million users, and triggers personalized retention campaigns through the marketing platform.

Accepted Answer

Weekly batch job assembles features from the data warehouse (aggregated behavioral signals), runs inference via a batch scoring script or ML platform job, and writes scores to a scoring table. A downstream integration reads from the scoring table and pushes high-risk users into the CRM or marketing automation tool with a personalized campaign tag. The candidate should discuss score freshness, score threshold calibration against campaign capacity, and holdout groups to measure campaign lift.

Question 10

Your company's analytics depend on 50 upstream tables updated daily. Design a system to detect and alert on data quality issues before they reach analysts.

Accepted Answer

The candidate should describe automated checks at the schema level (column presence, dtype), statistical level (row count, null rate, distribution of key metrics within expected bands), and business rule level (revenue must be non-negative). Tools: dbt tests, Great Expectations, or Monte Carlo for anomaly detection on historical distributions. Alerts should trigger in Slack or PagerDuty before the downstream tables are published, using a quality gate pattern in the DAG.

Question 11

Adobe Analytics processes billions of web and app events per day. Design the pipeline that ingests raw click-stream events from client SDKs, deduplicates them, enriches them with user and session context, and makes them queryable within 30 seconds of ingestion.

Accepted Answer

The pipeline has four stages: ingestion (load balancers accepting UDP or HTTPS beacons, writing to Kafka partitioned by visitor ID), stream processing (Flink or Kafka Streams for deduplication by event ID within a time window, session stitching, geolocation enrichment), hot storage (Druid or ClickHouse for sub-second queries on recent data), and cold storage (Parquet on S3 for long-term reporting). The candidate should address out-of-order events (watermarks), exactly-once semantics in the Kafka consumer, and how to replay a failed enrichment step without re-ingesting from clients.

Question 12

Design a search system for Amazon's product catalog. It needs to support full-text search, filtering by category and price, ranking by relevance and sales rank, and return results in under 200ms for hundreds of millions of queries per day.

Accepted Answer

A strong answer separates the indexing pipeline from the query path. The indexing side ingests product updates through a message queue, builds an inverted index (Elasticsearch or a custom Lucene-based system), and refreshes incrementally. The query path hits a distributed search cluster, applies ranking (a mix of BM25 textual relevance and real-time sales signals), and returns results through a CDN-cached response where possible. The candidate should discuss how to handle the long tail of rare queries versus hot queries, and how to update the index without re-indexing the entire catalog on every product change.

Question 13

When a customer places an order, DoorDash needs to assign a Dasher. The naive approach is first-available, but that ignores Dasher proximity, multi-order batching, restaurant prep time, and Dasher acceptance rates. How would you design the dispatch algorithm, and what data inputs does it need?

Accepted Answer

Strong answers frame this as an optimization problem with multiple objectives (minimize delivery time, maximize Dasher utilization, reduce unassigned orders) and explain why a greedy heuristic often beats ML models in latency-constrained dispatch. Candidates should mention the signal inputs (Dasher GPS, restaurant historical prep time, traffic ETA from a maps API, Dasher historical acceptance rate), batching logic for nearby orders, and a fallback when no Dasher is within a threshold radius.

Question 14

Netflix needs to track every play, pause, seek, and quality switch event from 200 million devices in real time. Design a pipeline that ingests these events, makes them available for real-time dashboards within 30 seconds, and stores them for batch analysis. Walk me through the entire path from device to dashboard.

Accepted Answer

A strong answer covers client-side event batching and compression before sending (to reduce request overhead), a high-throughput Kafka ingest layer with partitioning by user or session, stream processors (Flink or Spark Streaming) for real-time aggregations written to a low-latency store (Druid or ClickHouse), and a cold path to S3 via Parquet for batch queries in Spark or Presto. The candidate should discuss event deduplication, out-of-order events, and schema evolution via Avro or Protobuf.

Question 15

Netflix runs hundreds of simultaneous A/B tests across its product, from recommendation algorithms to UI layout to encoding parameters. Design the infrastructure that assigns users to test cells, logs exposure events, computes statistical significance, and prevents users from seeing conflicting experiments.

Accepted Answer

A strong answer covers a hashing-based assignment service that deterministically maps user IDs to experiment cells without network round-trips, a namespace collision system that prevents the same user from being in two mutually exclusive tests, server-side and client-side exposure logging at the moment of fork rather than at page load, and a statistics layer that computes confidence intervals and guards against peeking by requiring fixed-horizon or sequential analysis. The candidate should discuss how engineering teams integrate the SDK without slowing request paths.

Question 16

Suppose you are building a document retrieval system that uses OpenAI embeddings and approximate nearest-neighbor search. Walk me through the full design: how you generate and store embeddings, how you keep them fresh as documents update, how you choose an ANN index, and how you handle queries that the embedding model does not handle well.

Accepted Answer

A strong answer covers the embedding pipeline (batch vs on-write), the choice between Pinecone, pgvector, or Faiss and why (latency, update frequency, infrastructure simplicity), handling document updates without full re-indexing, hybrid search combining BM25 keyword retrieval with vector retrieval for robustness, and reranking as a latency-quality tradeoff. Edge cases: very short documents, multilingual queries, embedding model version changes.

Question 17

You are building the ontology layer for a Gotham-style threat intelligence platform. Analysts need to link entities like persons, locations, vehicles, and events across dozens of ingested data sources, many with conflicting or incomplete records. How would you design the underlying data model, and what are the biggest trade-offs you would make?

Accepted Answer

A strong answer covers entity resolution and probabilistic merging, a graph-based or property-graph model for linking heterogeneous entities, versioning of entity state over time, and how schema-on-read differs from schema-on-write for messy ingested data. The candidate should explicitly call out trade-offs between query performance and flexibility, and how you handle conflicting attribute values from different sources.

Question 18

Palantir serves multiple government agencies from the same platform. An analyst at Agency A must never see data that belongs to Agency B, but a small set of data must be shared between them. Data is stored in a shared data lake on S3 backed by a query engine. How do you design the access control layer?

Accepted Answer

A strong answer covers row-level and column-level security, attribute-based access control policies tied to the data ontology, enforcement at the query layer versus the storage layer, and audit logging. The candidate should discuss the risk of policy misconfiguration, how you test that no data leaks across tenants, and the performance overhead of applying fine-grained policy on every query. Bonus for mentioning the tension between centralised policy and delegated administration.

Question 19

A military client has 500,000 IoT sensors each emitting two events per second. You must ingest all events, store them durably, and make them queryable by analysts within 30 seconds of arrival. Walk me through your end-to-end architecture.

Accepted Answer

A strong answer covers a distributed log (Kafka or Pulsar) as the ingest backbone, partitioned by sensor ID for ordering guarantees, a stream processor (Flink or Spark Structured Streaming) for enrichment and transformation, and a time-series-friendly storage layer (ClickHouse, InfluxDB, or a columnar format in S3). The candidate must address back-pressure, schema evolution, exactly-once semantics, and hot spots when one sensor produces vastly more data than others.

Question 20

You need to build a near-real-time change data capture pipeline that detects inserts, updates, and deletes on a large Snowflake source table and incrementally loads a downstream analytics table. How would you use Snowflake Streams and Tasks to do this, and what are the failure modes you would watch for?

Accepted Answer

A stream tracks a hidden offset into the table's change log and exposes changed rows via the METADATA$ACTION column (INSERT or DELETE) and the METADATA$ISUPDATE boolean column. Updates appear as a DELETE row for the old value and an INSERT row for the new value, both with METADATA$ISUPDATE = TRUE. A scheduled Task consumes the stream in a MERGE statement into the target table. Key failure modes include the stream going stale if not consumed within the table's data retention period, tasks silently failing without alerting, and the MERGE statement taking longer than the task schedule and overlapping. A strong answer also mentions Serverless Tasks vs warehouse-driven tasks for cost.

Question 21

You are building a multi-tenant SaaS product where each customer's data must be fully isolated. How would you architect the data layer on Snowflake: one database per tenant, one schema per tenant, or row-level isolation with a tenant_id column? Walk through the tradeoffs.

Accepted Answer

A strong answer compares the three patterns: one database per tenant gives the strongest isolation and simplest permissioning but creates management overhead at thousands of tenants; one schema per tenant is a middle ground with easier cross-tenant analytics via views but still requires automating schema provisioning; a shared schema with tenant_id is operationally simplest but requires row-level security policies and careful query rewriting to prevent data leakage. The candidate should mention Snowflake's Row Access Policies for the shared model and discuss how warehouse sizing changes across patterns.

Question 22

You need to ingest 5 TB of JSON event data per day into Snowflake from thousands of IoT devices. Latency can be up to 15 minutes. Design the full ingestion architecture, naming the specific Snowflake features and complementary cloud services you would use, and explain how you would handle schema drift.

Accepted Answer

A strong answer describes devices publishing to a message bus (Kafka or AWS Kinesis), a processing layer (Kafka Connect Snowflake connector or a Flink job) writing Parquet files to S3/GCS, then Snowpipe automatically ingesting files via SQS event notifications. For schema drift, VARIANT columns absorb any JSON structure changes, and schema evolution can be handled by defining the final schema in Snowflake and using PARSE_JSON plus lateral flatten transforms at query time or in a transformation layer like dbt. The candidate should address error handling (dead-letter staging tables), monitoring (Snowpipe ingestion history views), and cost at scale.

Question 23

Every song play, skip, pause, and seek generates an event at Spotify. At peak, this is hundreds of thousands of events per second. Walk me through how you would build the ingestion layer using Kafka, what guarantees you need around exactly-once delivery versus at-least-once, how you partition topics, and how downstream consumers like the recommendation engine and billing system would subscribe.

Accepted Answer

A strong answer explains partition strategy by user ID or track ID depending on downstream ordering needs, discusses consumer group isolation so billing and recommendations do not affect each other's offsets, and addresses the tradeoff between exactly-once semantics (expensive) and idempotent at-least-once (simpler with deduplication keys). It covers schema evolution with Avro or Protobuf in a schema registry, and lag monitoring so a slow consumer does not silently fall behind.

Question 24

Tesla's Autopilot team collects billions of short video clips from the fleet every day. Design the end-to-end pipeline that ingests raw camera clips from vehicles, labels interesting edge cases automatically, routes ambiguous clips to human labelers, and delivers a clean training dataset to the model training cluster. Focus on throughput, cost, and labeling quality.

Accepted Answer

A strong answer covers: compressed upload from vehicle to cloud (cellular bandwidth constraints), cheap pre-filtering with a lightweight classifier to discard boring clips, automatic labeling via a teacher model with confidence scoring, active learning to route low-confidence examples to human labelers, deduplication to avoid overrepresenting common scenarios, versioned dataset snapshots, and feedback loops from model evaluation back into the labeling pipeline. The candidate should discuss throughput targets, storage cost (petabytes of video), and how to detect and correct systematic labeling errors.

Question 25

X's 'Who to Follow' feature suggests accounts to users. Describe how you would build a recommendation system that suggests users who share second-degree connections, common interests (inferred from tweets), or topic overlap. How would you scale this to run for all 300 million daily active users?

Accepted Answer

A strong answer covers graph traversal (BFS up to 2 hops in a sharded graph store like FlockDB or a custom adjacency list in Cassandra), collaborative filtering on follow patterns, topic modeling on tweet text (LDA or embedding similarity), and a candidate generation plus scoring pipeline that runs nightly for most users and near-real-time for new accounts. The candidate should quantify the candidate set size before and after ranking and explain why pre-computation is necessary at this scale.

Question 26

Design a system that flags anomalous user sessions in real time, for example a user suddenly downloading 100x their normal number of files. It needs to alert within 2 minutes of the anomalous event.

Accepted Answer

Stream processing layer (Kafka plus Flink or Spark Streaming) consumes events and maintains rolling feature aggregates per user (count in last 5 minutes, 1 hour, 24 hours). Anomaly scoring runs in-stream using a pre-trained model loaded into Flink, or a simple statistical baseline (z-score versus a user's historical distribution). Alerts are published to a downstream notification service. The candidate should discuss state management for streaming aggregates and the tradeoff between false positive rate and latency.

Question 27

Design a machine learning system to rank 10 million products for search queries at an e-commerce company. The system must update rankings daily based on user interactions.

Accepted Answer

Two-stage: retrieval (BM25 or dense retrieval with a bi-encoder) narrows candidates to 1,000, then a learning-to-rank model (LambdaMART or cross-encoder) scores them using query-product features (text similarity, sales velocity, click rate, price) plus user personalization signals. Training data is collected from logged click-through events with position bias correction. Daily retraining pipeline and online A/B test of ranking changes.

Question 28

You are building an LLM-powered writing assistant. Design an evaluation pipeline to test new model versions before shipping, covering quality, safety, and latency.

Accepted Answer

The pipeline needs three tracks: (1) automated metrics on a fixed benchmark set (ROUGE, BERTScore, task-specific metrics); (2) LLM-as-judge scoring using a stronger model to evaluate quality dimensions; (3) human evaluation queue for a sample of outputs on safety, factuality, and tone. Results are compared to a baseline and a regression threshold blocks shipping. Latency is tested under load. The candidate should discuss benchmark contamination risk and the limits of LLM-as-judge for safety.

Question 29

Airbnb needs to detect fraudulent hosts who create fake listings to collect deposits or scam guests. Design a system that flags suspicious listings and accounts in near real time. What signals would you use, and how would you act on them?

Accepted Answer

Strong answers identify signals at multiple levels: account (new account, unverified identity, no prior bookings), listing (copied photos from other sites, prices far below market, vague location), and behavioral (rapid listing creation, rapid message replies asking to pay off-platform). They propose a streaming pipeline (Kafka) feeding rule-based filters and a ML model, with a human review queue for borderline cases, and discuss the precision/recall tradeoff and the cost of false positives (blocking legitimate hosts).

Question 30

DoorDash regularly runs promotions like '50% off your first three orders.' Some users create multiple accounts to claim new-user discounts repeatedly. Design a system that detects and blocks this abuse in real time at the order checkout step, with low false-positive rates for legitimate new users.

Accepted Answer

Strong answers identify the abuse signals (same device fingerprint, same payment method, same delivery address, same phone number, overlapping IP address, account creation timing patterns) and design a risk-scoring service called at checkout that aggregates these signals from a feature store. The score triggers a block, a CAPTCHA challenge, or a hold for manual review depending on confidence. Candidates should discuss the asymmetry between false positives (blocking real new users is worse than letting one abuser through) and propose a feedback loop from fraud team decisions to retrain the model.

Questions

Design Netflix's recommendation engineSystem designhardVery common

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