Data Engineer • Behavioral • medium

Anthropic places a heavy emphasis on AI safety and Constitutional AI. Tell me about a time you had to push back on a project or feature because of data privacy, security, or ethical concerns. How did you handle the stakeholder conversation?

#AI Safety #Stakeholder Management #Ethics

Practice

Data Engineer • Behavioral • medium

Data Engineers at Anthropic work closely with ML Researchers whose requirements change rapidly based on experimental results. Tell me about a time you built a data pipeline or tool where the requirements were highly ambiguous or changed midway through development.

#Ambiguity #Agile #Cross-functional Teamwork

Practice

Data Engineer • Behavioral • hard

Walk me through the most complex data pipeline you've ever built from scratch. What were the bottleneck constraints (CPU, memory, network, or I/O), and how did you measure and overcome them?

#Architecture #Performance Profiling #Problem Solving

Practice

Data Engineer • Behavioral • medium

Anthropic focuses heavily on AI safety. Tell me about a time you identified a potential privacy, security, or safety risk in a dataset or pipeline. How did you raise the issue and what was the outcome?

#Safety #Communication #Ethics

Practice

Data Engineer • Behavioral • medium

Tell me about a time you had to debug a complex, distributed data pipeline failure under severe time pressure. What was your methodology?

#Debugging #Incident Response #Pressure

Practice

Data Engineer • Behavioral • medium

Anthropic highly values intellectual honesty. Tell me about a time you made a significant technical mistake that impacted a project. How did you handle it and what did you learn?

#Intellectual Honesty #Growth Mindset #Accountability

Practice

Data Engineer • Behavioral • medium

How do you prioritize tasks when supporting multiple fast-moving AI research teams with competing data needs and tight deadlines?

#Prioritization #Stakeholder Management #Agile

Practice

Data Engineer • Behavioral • easy

Tell me about a time you optimized a system or pipeline that resulted in significant cost or time savings. Walk me through the technical details of the bottleneck and your solution.

#Optimization #Impact #Problem Solving

Practice

Data Engineer • Behavioral • medium

Tell me about a time you had to push back on a product or research request because you had concerns about data safety, privacy, or quality.

#Communication #Safety #Integrity

Practice

Data Engineer • Behavioral • medium

Anthropic places a heavy emphasis on 'Constitutional AI' and safety. How do you ensure your day-to-day engineering work aligns with broad ethical guidelines and safety standards?

#Alignment #Ethics #Company Values

Practice

Data Engineer • Behavioral • medium

Describe a situation where you had to debug a complex, distributed data issue in production where there were no clear error logs or obvious failures.

#Debugging #Problem Solving #Resilience

Practice

Data Engineer • Behavioral • easy

Tell me about a time you had to learn a completely new technology stack or domain (like transitioning from traditional ETL to ML data engineering) under a tight deadline.

#Adaptability #Learning #Agility

Practice

Data Engineer • Behavioral • medium

How do you balance the need for rapid iteration and experimentation in AI research with the need for robust, reliable, and scalable data engineering practices?

#Trade-offs #Research vs Engineering #Prioritization

Practice

Data Engineer • Coding • medium

Given a table of API requests containing `user_id`, `timestamp`, `prompt_tokens`, and `completion_tokens`, write a SQL query to find the top 3 users by total token usage for each day over the last 30 days, including a rolling 7-day average of their token usage.

#Window Functions #Aggregations #Time-series Data

Practice

Data Engineer • Coding • hard

Write a Python function to efficiently find near-duplicate text documents in a large corpus. You do not need to implement the full distributed system, but implement the core hashing logic (e.g., MinHash) and explain how you would scale it across a cluster.

#Hashing #Text Processing #Optimization

Practice

Data Engineer • Coding • medium

Write a Python program that takes a massive JSONL file of Wikipedia articles and chunks the text into overlapping segments of exactly 512 tokens (assume a simple whitespace tokenizer for this exercise), while preserving the document metadata in each chunk. The file is larger than available RAM.

#Generators #Memory Management #Text Processing

Practice

Data Engineer • Coding • medium

Given a table of raw chat interactions (`interaction_id`, `user_id`, `timestamp`, `message`), write a SQL query to group these interactions into 'sessions'. A new session starts if there is a gap of more than 30 minutes between messages from the same user.

#Gaps and Islands #Window Functions #Data Modeling

Practice

Data Engineer • Coding • medium

Given a table of user prompts, write a SQL query to find the top 3 most frequent prompt categories for each user. Include ties if they exist.

#Window Functions #Ranking #CTEs

Practice

Data Engineer • Coding • medium

Implement a rate limiter in Python for our API. The rate limiter should allow a user to make up to N requests per minute, but also enforce a maximum of M tokens generated per day. How would you make this distributed across multiple API servers?

#Data Structures #Concurrency #API Design

Practice

Data Engineer • Coding • medium

Given a massive table of web crawl documents with `doc_id`, `url`, `content_hash`, and `crawled_at`, write a highly optimized SQL query to keep only the most recent version of each document per URL, but flag URLs that have multiple distinct content hashes over time.

#Window Functions #Deduplication #Data Cleaning

Practice

Data Engineer • Coding • medium

Write a Python function to process a 500GB JSONL file of raw text data. You need to filter out documents containing specific blocklisted keywords, compute a basic word count across the valid documents, and output the clean data to a new file. You have 8GB of RAM.

#Python #Generators #Memory Management #I/O

Practice

Data Engineer • Coding • hard

Implement a distributed rate limiter in Python. Assume this will be used to throttle API requests for our Claude models based on a user's tier (e.g., tokens per minute).

#Concurrency #Redis #Token Bucket #Distributed Systems

Practice

Data Engineer • Coding • medium

Given a list of overlapping time intervals representing periods when a GPU cluster was fully utilized, write a function to merge all overlapping intervals and return the total duration of full utilization.

#Sorting #Intervals #Python

Practice

Data Engineer • Coding • hard

Write a SQL query to calculate the 7-day rolling average of token usage per user, but only for users who have exceeded 10,000 tokens in at least three distinct days within the last month.

#Advanced SQL #Rolling Averages #Subqueries

Practice

Data Engineer • Coding • medium

Implement a Trie (Prefix Tree) data structure in Python. Then, write a method to find all words in the Trie that share a given prefix. Explain how this relates to LLM tokenization.

#Data Structures #Trees #String Manipulation

Practice

Data Engineer • Coding • hard

You have a stream of incoming chat logs. Write a Python algorithm to maintain the top K most frequent words over a sliding window of 1 hour.

#Streaming Algorithms #Heaps #Sliding Window

Practice

Data Engineer • Coding • hard

Write a SQL query to find the 'sessionization' of user interactions. Group consecutive user prompts into a single session if they occur within 30 minutes of each other. Output the user_id, session_start, session_end, and prompt_count.

#Sessionization #Window Functions #Time Series

Practice

Data Engineer • Coding • medium

Write a Python script that implements a custom MapReduce framework using the `multiprocessing` library to count the frequency of n-grams in a large corpus of text files.

#Concurrency #MapReduce #Python

Practice

Data Engineer • Coding • hard

Given a directed acyclic graph (DAG) representing data pipeline dependencies, write a Python function to execute the tasks in parallel where possible, respecting the dependency order. Assume each task is a sleep function.

#Graphs #Topological Sort #Concurrency

Practice

Data Engineer • Coding • medium

Write a SQL query to find the top 3 most frequently used prompt templates per user, but exclude templates that consist entirely of stop words (assume a `stop_words` table exists).

#Joins #Filtering #Window Functions

Practice

Data Engineer • Coding • hard

Given a massive string of text, write an algorithm to find the longest repeating substring. This is a simplified version of finding duplicated boilerplate text in web scrapes.

#String Algorithms #Suffix Arrays #Dynamic Programming

Practice

Data Engineer • Coding • medium

Write a SQL query to calculate the 30-day rolling average of tokens processed per model version, given a table of daily token usage logs.

#Window Functions #Aggregations #Time Series

Practice

Data Engineer • Coding • medium

Write a Python generator function to efficiently parse a 500GB JSONL file containing web crawl data, filtering out documents that do not contain a specific set of keywords, without loading the entire file into memory.

#Python #Generators #Memory Management #File I/O

Practice

Data Engineer • Coding • hard

Given a massive dataset of text documents, implement a MinHash and Locality-Sensitive Hashing (LSH) algorithm in Python to identify near-duplicate documents. How would you scale this across a distributed cluster?

#Hashing #Deduplication #Big Data #Distributed Systems

Practice

Data Engineer • Coding • medium

Write a function that takes a stream of text and a target keyword, and returns a sliding window of N tokens before and after every occurrence of the keyword. Handle edge cases like overlapping windows.

#Sliding Window #Text Processing #Queues

Practice

Data Engineer • Coding • medium

We need to create a pre-training dataset with a specific language distribution (e.g., 60% English, 20% Spanish, 20% French). Write a script to sample proportionally from a massive, unsorted stream of multilingual documents.

#Sampling #Probability #Streaming Algorithms

Practice

Data Engineer • Coding • easy

Given a list of text spans representing PII (Personally Identifiable Information) redactions in a document, where each span is a tuple of (start_index, end_index), write a function to merge all overlapping spans.

#Intervals #Arrays #Sorting

Practice

Data Engineer • Coding • hard

Implement a thread-safe Token Bucket rate limiter in Python. This will be used to throttle incoming requests to our data ingestion API to prevent overwhelming the downstream Kafka cluster.

#Concurrency #Rate Limiting #System Design

Practice

Data Engineer • Coding • medium

Write a program to compute the top K most frequent tokens in a continuous, infinite stream of text. Optimize for both time and space complexity.

#Heaps #Hash Maps #Streaming

Practice

Data Engineer • Coding • hard

Given two large documents, write an algorithm to find the longest common contiguous substring. This is used in our pipeline to detect data contamination between training and evaluation sets.

#Dynamic Programming #Suffix Trees #Strings

Practice

Data Engineer • Coding • hard

We have a log table of safety filter triggers. Write a SQL query to identify all user sessions where a user triggered a safety filter more than 3 times within any 5-minute window.

#Self Joins #Time Series #Complex Window Functions

Practice

Data Engineer • Coding • hard

Write a SQL query to find the median model response latency per day from a massive logs table, assuming your SQL dialect does not have a built-in MEDIAN() function.

#Percentiles #Math #Advanced SQL

Practice

Data Engineer • Coding • medium

In our distributed logging system, log IDs are supposed to be sequential. Write a SQL query to find all gaps (missing sequential IDs) in the log table.

#Gaps and Islands #Sequences #Self Joins

Practice

Data Engineer • Coding • medium

Write a SQL query to calculate the Day-1, Day-7, and Day-30 retention rate of users interacting with the Claude API, grouped by the month they signed up.

#Cohorts #Retention #Date Math

Practice

Data Engineer • Coding • medium

You have a table of model evaluation scores in a long format: (model_id, eval_metric, score). Write a SQL query to pivot this table so that 'Helpfulness', 'Honesty', and 'Harmlessness' are columns.

#Pivot #Data Transformation #Aggregations

Practice

Data Engineer • System Design • hard

Design a scalable data pipeline to ingest, deduplicate, and filter 50TB of raw web scrape data per day to be used for pre-training a large language model. How do you handle PII scrubbing and ensure high data quality at this scale?

#Distributed Systems #Data Pipelines #Data Quality #MapReduce/Spark

Practice

Data Engineer • System Design • hard

Design a real-time monitoring and alerting system for Claude's inference endpoints. The system needs to track latency, error rates, and token generation speed (Time to First Token, Tokens per Second), processing millions of events per minute with sub-second alerting latency.

#Stream Processing #Kafka #Observability #Real-time Analytics

Practice

Data Engineer • System Design • hard

Design a data architecture to support automated model evaluations. Every time a new model checkpoint is saved, it needs to be run against 10,000 benchmark datasets. How do you manage the orchestration, store the results, and provide a dashboard for researchers to compare model versions?

#Orchestration #Airflow/Dagster #Data Modeling #CI/CD for ML

Practice

Data Engineer • System Design • hard

Design a data ingestion and processing pipeline to handle 10PB of raw web scrape data. The pipeline must perform exact and fuzzy deduplication, remove PII, and format the output into tokenized chunks for LLM pre-training.

#Distributed Systems #Data Pipelines #MinHash/LSH #MapReduce

Practice

Data Engineer • System Design • hard

Design a real-time monitoring and alerting system for LLM inference. It needs to track latency, token generation speed, and run a lightweight toxicity classifier on the output stream. How do you handle spikes of 100,000 requests per second?

#Stream Processing #Kafka #Real-time Analytics #Monitoring

Practice

Data Engineer • System Design • hard

Design a system to track data provenance and lineage for Constitutional AI training sets. If a specific document is found to be corrupted, we need to know exactly which model checkpoints were trained on it.

#Data Lineage #Metadata Management #Graph Databases

Practice

Data Engineer • System Design • hard

Design an evaluation pipeline that runs 50,000 complex prompts against multiple versions of an LLM daily. The pipeline must aggregate scores, compute regressions, and block model deployment if safety thresholds are breached.

#Batch Processing #CI/CD for ML #Airflow/Dagster

Practice

Data Engineer • System Design • medium

Design a scalable backend system for collecting RLHF (Reinforcement Learning from Human Feedback) data. Human annotators will be comparing two model outputs. The system must ensure no data loss, handle annotator concurrency, and output training-ready datasets.

#Transactional Databases #Concurrency #API Design

Practice

Data Engineer • System Design • hard

Design a distributed vector embedding storage and retrieval system. Researchers need to perform KNN searches on billions of embeddings generated from our models.

#Vector Databases #KNN/ANN #Distributed Systems

Practice

Data Engineer • System Design • hard

Design a multi-region active-active data replication system for model checkpoints. Each checkpoint is 100GB, and they are generated every hour. Researchers globally need fast access to the latest checkpoints.

#Data Replication #Cloud Storage #Network Optimization

Practice

Data Engineer • System Design • medium

Design an experiment management system to track hyperparameter tuning, dataset versions, and evaluation metrics for thousands of concurrent LLM training runs.

#MLOps #Database Design #API Design

Practice

Data Engineer • System Design • hard

Design a distributed task queue specifically optimized for scheduling offline batch inference jobs on GPUs. Some jobs take seconds, others take days. GPUs are heterogeneous (e.g., A100s vs H100s).

#Task Queues #Resource Scheduling #Distributed Systems

Practice

Data Engineer • System Design • hard

Design a data pipeline to ingest, clean, and deduplicate 100TB of raw web crawl data for LLM pre-training. Walk me through the architecture, tools, and how you handle failures.

#Batch Processing #Data Pipelines #LLM Training #Spark

Practice

Data Engineer • System Design • hard

Design a real-time monitoring system to track model inference latency and safety filter trigger rates across millions of requests per minute. How do you ensure low latency for the dashboard?

#Streaming #Monitoring #Metrics #Kafka #Druid/Pinot

Practice

Data Engineer • System Design • hard

How would you design a system to handle continuous, high-throughput updates to a vector database used for Retrieval-Augmented Generation (RAG) without impacting read performance?

#Vector Databases #RAG #Data Sync #Concurrency

Practice

Data Engineer • System Design • medium

Design an automated evaluation pipeline that runs nightly benchmarks on the latest model checkpoints. The pipeline needs to run thousands of prompts, score them using another LLM, and aggregate the results.

#Orchestration #CI/CD for ML #Airflow #Batch Inference

Practice

Data Engineer • System Design • hard

Design a distributed data processing framework to tokenize petabytes of text data efficiently. How do you handle vocabulary updates and ensure reproducibility?

#Distributed Systems #MapReduce #Tokenization #Reproducibility

Practice

Data Engineer • System Design • medium

How would you architect a data lake at Anthropic to support both ML researchers needing raw text blobs and business analysts needing structured API usage metrics?

#Data Lake #Architecture #Storage Formats #Governance

Practice

Data Engineer • System Design • hard

Design a system to track data lineage for datasets used in training Claude. If a researcher finds a toxic output, how do we trace it back to the specific training document?

#Data Lineage #Governance #Metadata Management

Practice

Data Engineer • System Design • medium

Design a highly scalable web scraper to build a high-quality dataset of academic papers. How do you handle rate limiting, IP bans, and parsing diverse PDF layouts?

#Web Scraping #Distributed Systems #Queues #Unstructured Data

Practice

Data Engineer • System Design • medium

How do you handle schema evolution in a massive data pipeline where upstream data formats (like web crawl schemas or partner data) change frequently without notice?

#Schema Evolution #Data Quality #Data Contracts

Practice

Data Engineer • System Design • hard

Design a system to securely handle, detect, and anonymize PII (Personally Identifiable Information) in petabytes of training datasets before they reach the ML models.

#Security #PII #Compliance #NLP

Practice

Data Engineer • Technical • medium

We store petabytes of text data for model training. Compare and contrast storing this data in Parquet, JSONL, and TFRecord/WebDataset formats. Which would you choose for a distributed PyTorch training job and why?

#File Formats #Storage Optimization #Machine Learning Infrastructure

Practice

Data Engineer • Technical • hard

During a distributed Spark job to compute vocabulary frequencies across our training corpus, you encounter severe data skew because some words (like 'the') appear orders of magnitude more often than others, causing out-of-memory errors on specific worker nodes. How do you resolve this?

#Apache Spark #Data Skew #Distributed Computing #Performance Tuning

Practice

Data Engineer • Technical • hard

Explain how you would build a pipeline to keep a vector database updated in near real-time as underlying source documents change (inserts, updates, deletes). How do you handle embedding versioning when the embedding model itself is updated?

#Vector Databases #RAG #Change Data Capture (CDC) #Embeddings

Practice

Data Engineer • Technical • medium

For Constitutional AI, we rely on high-quality human preference data (RLHF). If you have a pipeline receiving human-annotated rankings of model outputs, what automated data quality checks would you implement to detect spammy, biased, or low-effort annotators?

#Anomaly Detection #Data Validation #Heuristics

Practice

Data Engineer • Technical • hard

In Apache Spark, how would you handle a situation where a `join` operation causes severe data skew, specifically when processing text data where certain domains (e.g., Wikipedia) are vastly overrepresented?

#Apache Spark #Data Skew #Performance Optimization

Practice

Data Engineer • Technical • medium

Explain the trade-offs between Parquet, Avro, and JSONL formats. Which would you choose for storing intermediate RLHF (Reinforcement Learning from Human Feedback) data, and why?

#File Formats #Storage Optimization #Schema Evolution

Practice

Data Engineer • Technical • medium

How do you manage schema evolution in a rapidly changing data environment where AI researchers are constantly adding new metadata fields to evaluation logs?

#Schema Evolution #Data Governance #Protobuf/Thrift

Practice

Data Engineer • Technical • hard

What strategies do you use to minimize cloud storage and compute costs for petabyte-scale datasets while maintaining high read throughput for ML training clusters?

#Cloud Architecture #Cost Optimization #Caching

Practice

Data Engineer • Technical • hard

How would you handle backfilling a massive historical dataset (2PB) after a subtle bug is found in the tokenization logic that has been running for 6 months?

#Backfilling #Data Pipelines #Idempotency

Practice

Data Engineer • Technical • medium

Explain the differences between at-least-once, at-most-once, and exactly-once delivery semantics in distributed streaming platforms like Kafka. How do you achieve exactly-once processing?

#Kafka #Streaming #Distributed Systems

Practice

Data Engineer • Technical • medium

Describe your approach to implementing strict data quality checks for safety-critical datasets. How do you prevent 'bad' data from silently corrupting a model training run?

#Data Quality #Testing #Anomaly Detection

Practice

Data Engineer • Technical • hard

What are the challenges of managing state in streaming applications (e.g., Apache Flink) compared to batch processing, particularly when dealing with late-arriving data?

#Stream Processing #State Management #Watermarks

Practice

Data Engineer • Technical • medium

How do you ensure reproducibility in data pipelines used for machine learning? If a researcher asks for the exact dataset used to train a model 6 months ago, how do you provide it?

#Reproducibility #Data Versioning #MLOps

Practice

Data Engineer • Technical • medium

Explain how you would diagnose and optimize a PySpark job that is failing due to OutOfMemory (OOM) errors caused by severe data skew.

#Spark #Performance Tuning #Data Skew

Practice

Data Engineer • Technical • hard

How does Apache Kafka ensure exactly-once semantics? In what scenarios would you choose at-least-once over exactly-once for Anthropic's data pipelines?

#Kafka #Distributed Messaging #Semantics

Practice

Data Engineer • Technical • medium

Describe the trade-offs between columnar storage formats like Parquet and row-based storage formats like Avro. Which would you choose for storing tokenized LLM training data and why?

#Storage Formats #Big Data #I/O Optimization

Practice

Data Engineer • Technical • medium

How do you ensure data quality and detect statistical drift in a continuous ingestion pipeline feeding an active learning system?

#Data Quality #Anomaly Detection #Observability

Practice

Data Engineer • Technical • hard

Explain how you would implement backpressure in a streaming data pipeline. What happens if the downstream consumer (e.g., an ML inference endpoint) goes down?

#Streaming #Architecture #Resilience

Practice

Anthropic

The Interview Loop

Recruiter Screen (30 min)

Technical Loop (3-4 Rounds)

Interview Question Bank

Anthropic places a heavy emphasis on AI safety and Constitutional AI. Tell me about a time you had to push back on a project or feature because of data privacy, security, or ethical concerns. How did you handle the stakeholder conversation?

Data Engineers at Anthropic work closely with ML Researchers whose requirements change rapidly based on experimental results. Tell me about a time you built a data pipeline or tool where the requirements were highly ambiguous or changed midway through development.

Walk me through the most complex data pipeline you've ever built from scratch. What were the bottleneck constraints (CPU, memory, network, or I/O), and how did you measure and overcome them?

Anthropic focuses heavily on AI safety. Tell me about a time you identified a potential privacy, security, or safety risk in a dataset or pipeline. How did you raise the issue and what was the outcome?

Tell me about a time you had to debug a complex, distributed data pipeline failure under severe time pressure. What was your methodology?

Anthropic highly values intellectual honesty. Tell me about a time you made a significant technical mistake that impacted a project. How did you handle it and what did you learn?

How do you prioritize tasks when supporting multiple fast-moving AI research teams with competing data needs and tight deadlines?

Tell me about a time you optimized a system or pipeline that resulted in significant cost or time savings. Walk me through the technical details of the bottleneck and your solution.

Tell me about a time you had to push back on a product or research request because you had concerns about data safety, privacy, or quality.

Anthropic places a heavy emphasis on 'Constitutional AI' and safety. How do you ensure your day-to-day engineering work aligns with broad ethical guidelines and safety standards?

Describe a situation where you had to debug a complex, distributed data issue in production where there were no clear error logs or obvious failures.

Tell me about a time you had to learn a completely new technology stack or domain (like transitioning from traditional ETL to ML data engineering) under a tight deadline.

How do you balance the need for rapid iteration and experimentation in AI research with the need for robust, reliable, and scalable data engineering practices?

Given a table of API requests containing `user_id`, `timestamp`, `prompt_tokens`, and `completion_tokens`, write a SQL query to find the top 3 users by total token usage for each day over the last 30 days, including a rolling 7-day average of their token usage.

Write a Python function to efficiently find near-duplicate text documents in a large corpus. You do not need to implement the full distributed system, but implement the core hashing logic (e.g., MinHash) and explain how you would scale it across a cluster.

Write a Python program that takes a massive JSONL file of Wikipedia articles and chunks the text into overlapping segments of exactly 512 tokens (assume a simple whitespace tokenizer for this exercise), while preserving the document metadata in each chunk. The file is larger than available RAM.

Given a table of raw chat interactions (`interaction_id`, `user_id`, `timestamp`, `message`), write a SQL query to group these interactions into 'sessions'. A new session starts if there is a gap of more than 30 minutes between messages from the same user.

Given a table of user prompts, write a SQL query to find the top 3 most frequent prompt categories for each user. Include ties if they exist.

Implement a rate limiter in Python for our API. The rate limiter should allow a user to make up to N requests per minute, but also enforce a maximum of M tokens generated per day. How would you make this distributed across multiple API servers?

Given a massive table of web crawl documents with `doc_id`, `url`, `content_hash`, and `crawled_at`, write a highly optimized SQL query to keep only the most recent version of each document per URL, but flag URLs that have multiple distinct content hashes over time.

Write a Python function to process a 500GB JSONL file of raw text data. You need to filter out documents containing specific blocklisted keywords, compute a basic word count across the valid documents, and output the clean data to a new file. You have 8GB of RAM.

Implement a distributed rate limiter in Python. Assume this will be used to throttle API requests for our Claude models based on a user's tier (e.g., tokens per minute).

Given a list of overlapping time intervals representing periods when a GPU cluster was fully utilized, write a function to merge all overlapping intervals and return the total duration of full utilization.

Write a SQL query to calculate the 7-day rolling average of token usage per user, but only for users who have exceeded 10,000 tokens in at least three distinct days within the last month.

Implement a Trie (Prefix Tree) data structure in Python. Then, write a method to find all words in the Trie that share a given prefix. Explain how this relates to LLM tokenization.

You have a stream of incoming chat logs. Write a Python algorithm to maintain the top K most frequent words over a sliding window of 1 hour.

Write a SQL query to find the 'sessionization' of user interactions. Group consecutive user prompts into a single session if they occur within 30 minutes of each other. Output the user_id, session_start, session_end, and prompt_count.

Write a Python script that implements a custom MapReduce framework using the `multiprocessing` library to count the frequency of n-grams in a large corpus of text files.

Given a directed acyclic graph (DAG) representing data pipeline dependencies, write a Python function to execute the tasks in parallel where possible, respecting the dependency order. Assume each task is a sleep function.

Write a SQL query to find the top 3 most frequently used prompt templates per user, but exclude templates that consist entirely of stop words (assume a `stop_words` table exists).

Given a massive string of text, write an algorithm to find the longest repeating substring. This is a simplified version of finding duplicated boilerplate text in web scrapes.

Write a SQL query to calculate the 30-day rolling average of tokens processed per model version, given a table of daily token usage logs.

Write a Python generator function to efficiently parse a 500GB JSONL file containing web crawl data, filtering out documents that do not contain a specific set of keywords, without loading the entire file into memory.

Given a massive dataset of text documents, implement a MinHash and Locality-Sensitive Hashing (LSH) algorithm in Python to identify near-duplicate documents. How would you scale this across a distributed cluster?

Write a function that takes a stream of text and a target keyword, and returns a sliding window of N tokens before and after every occurrence of the keyword. Handle edge cases like overlapping windows.

We need to create a pre-training dataset with a specific language distribution (e.g., 60% English, 20% Spanish, 20% French). Write a script to sample proportionally from a massive, unsorted stream of multilingual documents.

Given a list of text spans representing PII (Personally Identifiable Information) redactions in a document, where each span is a tuple of (start_index, end_index), write a function to merge all overlapping spans.

Implement a thread-safe Token Bucket rate limiter in Python. This will be used to throttle incoming requests to our data ingestion API to prevent overwhelming the downstream Kafka cluster.

Write a program to compute the top K most frequent tokens in a continuous, infinite stream of text. Optimize for both time and space complexity.

Given two large documents, write an algorithm to find the longest common contiguous substring. This is used in our pipeline to detect data contamination between training and evaluation sets.

We have a log table of safety filter triggers. Write a SQL query to identify all user sessions where a user triggered a safety filter more than 3 times within any 5-minute window.

Write a SQL query to find the median model response latency per day from a massive logs table, assuming your SQL dialect does not have a built-in MEDIAN() function.

In our distributed logging system, log IDs are supposed to be sequential. Write a SQL query to find all gaps (missing sequential IDs) in the log table.

Write a SQL query to calculate the Day-1, Day-7, and Day-30 retention rate of users interacting with the Claude API, grouped by the month they signed up.

You have a table of model evaluation scores in a long format: (model_id, eval_metric, score). Write a SQL query to pivot this table so that 'Helpfulness', 'Honesty', and 'Harmlessness' are columns.

Design a scalable data pipeline to ingest, deduplicate, and filter 50TB of raw web scrape data per day to be used for pre-training a large language model. How do you handle PII scrubbing and ensure high data quality at this scale?

Design a real-time monitoring and alerting system for Claude's inference endpoints. The system needs to track latency, error rates, and token generation speed (Time to First Token, Tokens per Second), processing millions of events per minute with sub-second alerting latency.

Design a data architecture to support automated model evaluations. Every time a new model checkpoint is saved, it needs to be run against 10,000 benchmark datasets. How do you manage the orchestration, store the results, and provide a dashboard for researchers to compare model versions?

Design a data ingestion and processing pipeline to handle 10PB of raw web scrape data. The pipeline must perform exact and fuzzy deduplication, remove PII, and format the output into tokenized chunks for LLM pre-training.

Design a real-time monitoring and alerting system for LLM inference. It needs to track latency, token generation speed, and run a lightweight toxicity classifier on the output stream. How do you handle spikes of 100,000 requests per second?

Design a system to track data provenance and lineage for Constitutional AI training sets. If a specific document is found to be corrupted, we need to know exactly which model checkpoints were trained on it.

Design an evaluation pipeline that runs 50,000 complex prompts against multiple versions of an LLM daily. The pipeline must aggregate scores, compute regressions, and block model deployment if safety thresholds are breached.

Design a scalable backend system for collecting RLHF (Reinforcement Learning from Human Feedback) data. Human annotators will be comparing two model outputs. The system must ensure no data loss, handle annotator concurrency, and output training-ready datasets.

Design a distributed vector embedding storage and retrieval system. Researchers need to perform KNN searches on billions of embeddings generated from our models.

Design a multi-region active-active data replication system for model checkpoints. Each checkpoint is 100GB, and they are generated every hour. Researchers globally need fast access to the latest checkpoints.

Design an experiment management system to track hyperparameter tuning, dataset versions, and evaluation metrics for thousands of concurrent LLM training runs.

Design a distributed task queue specifically optimized for scheduling offline batch inference jobs on GPUs. Some jobs take seconds, others take days. GPUs are heterogeneous (e.g., A100s vs H100s).

Design a data pipeline to ingest, clean, and deduplicate 100TB of raw web crawl data for LLM pre-training. Walk me through the architecture, tools, and how you handle failures.

Design a real-time monitoring system to track model inference latency and safety filter trigger rates across millions of requests per minute. How do you ensure low latency for the dashboard?

How would you design a system to handle continuous, high-throughput updates to a vector database used for Retrieval-Augmented Generation (RAG) without impacting read performance?

Design an automated evaluation pipeline that runs nightly benchmarks on the latest model checkpoints. The pipeline needs to run thousands of prompts, score them using another LLM, and aggregate the results.

Design a distributed data processing framework to tokenize petabytes of text data efficiently. How do you handle vocabulary updates and ensure reproducibility?

How would you architect a data lake at Anthropic to support both ML researchers needing raw text blobs and business analysts needing structured API usage metrics?

Design a system to track data lineage for datasets used in training Claude. If a researcher finds a toxic output, how do we trace it back to the specific training document?

Design a highly scalable web scraper to build a high-quality dataset of academic papers. How do you handle rate limiting, IP bans, and parsing diverse PDF layouts?

How do you handle schema evolution in a massive data pipeline where upstream data formats (like web crawl schemas or partner data) change frequently without notice?

Design a system to securely handle, detect, and anonymize PII (Personally Identifiable Information) in petabytes of training datasets before they reach the ML models.

We store petabytes of text data for model training. Compare and contrast storing this data in Parquet, JSONL, and TFRecord/WebDataset formats. Which would you choose for a distributed PyTorch training job and why?

During a distributed Spark job to compute vocabulary frequencies across our training corpus, you encounter severe data skew because some words (like 'the') appear orders of magnitude more often than others, causing out-of-memory errors on specific worker nodes. How do you resolve this?

Explain how you would build a pipeline to keep a vector database updated in near real-time as underlying source documents change (inserts, updates, deletes). How do you handle embedding versioning when the embedding model itself is updated?

For Constitutional AI, we rely on high-quality human preference data (RLHF). If you have a pipeline receiving human-annotated rankings of model outputs, what automated data quality checks would you implement to detect spammy, biased, or low-effort annotators?

In Apache Spark, how would you handle a situation where a `join` operation causes severe data skew, specifically when processing text data where certain domains (e.g., Wikipedia) are vastly overrepresented?

Explain the trade-offs between Parquet, Avro, and JSONL formats. Which would you choose for storing intermediate RLHF (Reinforcement Learning from Human Feedback) data, and why?

How do you manage schema evolution in a rapidly changing data environment where AI researchers are constantly adding new metadata fields to evaluation logs?

What strategies do you use to minimize cloud storage and compute costs for petabyte-scale datasets while maintaining high read throughput for ML training clusters?