Data Engineer • Behavioral • medium

Tell me about a time you had to make a technical tradeoff between data quality and pipeline speed. How did you decide, and what was the outcome?

#Trade-offs #Decision Making #Data Quality

Practice

Data Engineer • Behavioral • medium

OpenAI moves very fast. Describe a situation where you had to build a data pipeline with constantly changing requirements and incomplete upstream data schemas. How did you ensure reliability?

#Ambiguity #Adaptability #Reliability

Practice

Data Engineer • Behavioral • medium

Tell me about a time you identified a major bottleneck or inefficiency in a data system that no one else noticed. How did you go about fixing it and getting buy-in from the team?

#Ownership #Proactivity #Impact

Practice

Data Engineer • Behavioral • medium

Describe a time you had to debug a silent data corruption issue. How did you detect it and fix it?

#Debugging #Data Integrity #Problem Solving

Practice

Data Engineer • Behavioral • medium

OpenAI moves incredibly fast. Tell me about a time you had to make a technical trade-off between shipping quickly and building a perfectly scalable system.

#Trade-offs #Agile #Decision Making

Practice

Data Engineer • Behavioral • medium

Tell me about a time you disagreed with a researcher or data scientist about how data should be processed or modeled. How did you resolve it?

#Collaboration #Conflict Resolution #Communication

Practice

Data Engineer • Behavioral • hard

At OpenAI, safety and alignment are critical. How would you handle a situation where you discovered a flaw in a data pipeline that might have introduced biased or unsafe data into a training run?

#Ethics #Safety #Integrity #Incident Response

Practice

Data Engineer • Behavioral • easy

Describe a project where you had to learn a completely new technology or framework on the fly to solve a critical business problem.

#Adaptability #Continuous Learning #Problem Solving

Practice

Data Engineer • Behavioral • medium

Tell me about the most complex data pipeline you've ever built. What made it complex, and what would you do differently today?

#Architecture #Retrospective #Experience

Practice

Data Engineer • Behavioral • medium

Tell me about a time you had to optimize a data pipeline that was failing or severely bottlenecked under scale. What was the root cause and how did you fix it?

#Performance Tuning #Problem Solving #Impact

Practice

Data Engineer • Behavioral • medium

Describe a situation where you had to make a difficult trade-off between data quality and processing speed/delivery time. How did you make your decision?

#Trade-offs #Data Quality #Prioritization

Practice

Data Engineer • Behavioral • medium

OpenAI moves very fast and requirements can change rapidly. Tell me about a time you had to deliver a critical project with ambiguous requirements and a tight deadline.

#Ambiguity #Agility #Execution

Practice

Data Engineer • Behavioral • medium

Tell me about a time you disagreed with a senior engineer or stakeholder about a technical design or architecture. How did you approach the disagreement and what was the outcome?

#Conflict Resolution #Communication #Technical Leadership

Practice

Data Engineer • Behavioral • medium

Describe a time you discovered a critical bug or data corruption issue in your pipeline after it was already in production. How did you handle the incident?

#Incident Management #Accountability #Post-mortems

Practice

Data Engineer • Behavioral • hard

What is the most complex distributed systems problem you have ever debugged? Walk me through your troubleshooting process from alert to resolution.

#Debugging #Distributed Systems #Deep Dive

Practice

Data Engineer • Behavioral • medium

Tell me about a time you proactively identified a bottleneck or technical debt in your team's infrastructure and took the initiative to fix it without being asked.

#Initiative #Technical Debt #Ownership

Practice

Data Engineer • Behavioral • easy

Why do you want to join OpenAI specifically, and how do you see the role of a Data Engineer evolving as AI models become more capable of writing code and analyzing data?

#Motivation #Industry Trends #AGI

Practice

Data Engineer • Coding • medium

Write a Python function to parse a massive JSONL file containing web crawl data, filter out documents with a high proportion of non-alphanumeric characters (spam/code), and yield batches of clean text. Assume the file is significantly larger than available RAM.

#Python #Generators #Memory Management #Text Processing

Practice

Data Engineer • Coding • medium

Given a table of API requests (request_id, user_id, model_name, tokens_used, timestamp), write a SQL query to find the top 3 users by token usage for each model over the last 30 days, but only include users who have used at least two different models.

#Window Functions #CTEs #Aggregations

Practice

Data Engineer • Coding • hard

Implement a rate limiter for our API. Given a stream of requests, allow a maximum of N requests per minute per user. If a user exceeds this, drop the requests. Optimize for high concurrency and minimal latency.

#Rate Limiting #Concurrency #Data Structures #Redis

Practice

Data Engineer • Coding • medium

Given a list of conversational turns (user prompt, assistant response) with timestamps and session IDs, write a function to reconstruct the conversation threads. Note that some turns might arrive out of order or have missing timestamps.

#Data Structures #Sorting #Edge Cases

Practice

Data Engineer • Coding • hard

Design the database schema and write the SQL to track RLHF (Reinforcement Learning from Human Feedback) tasks. We have prompts, multiple model completions, and human rankings. How do you query for the inter-annotator agreement rate?

#Schema Design #Complex Queries #RLHF

Practice

Data Engineer • Coding • easy

Write a function to merge overlapping time intervals. We use this to calculate the total active compute time for GPU clusters given a log of job start and end times.

#Intervals #Sorting #Python

Practice

Data Engineer • Coding • medium

Write a Python generator to efficiently parse a 500GB JSONL file containing conversation logs without loading the whole file into memory.

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

Practice

Data Engineer • Coding • medium

Given a stream of API requests, implement a sliding window rate limiter.

#Data Structures #Concurrency #Queues

Practice

Data Engineer • Coding • medium

Implement a function to merge overlapping text intervals (e.g., highlighting spans in a document).

#Sorting #Arrays #Intervals

Practice

Data Engineer • Coding • hard

Write a distributed map-reduce job from scratch in Python using multiprocessing to count token frequencies across multiple files.

#Python #Multiprocessing #MapReduce #Concurrency

Practice

Data Engineer • Coding • medium

Given a list of data pipeline tasks with dependencies, write a function to return a valid execution order.

#Graphs #Topological Sort #DAGs

Practice

Data Engineer • Coding • medium

Implement an LRU cache with a TTL (Time To Live) for caching database queries.

#Data Structures #Hash Maps #Linked Lists #Caching

Practice

Data Engineer • Coding • medium

Write a script to sample exactly K random lines from a massive text file in a single pass.

#Probability #Reservoir Sampling #Big Data

Practice

Data Engineer • Coding • hard

Implement a MinHash and Locality-Sensitive Hashing (LSH) algorithm to find near-duplicate documents in a massive corpus of web text.

#Hashing #Probability #Text Processing #Big Data

Practice

Data Engineer • Coding • medium

Given a list of text spans representing PII (Personally Identifiable Information) redactions with start and end indices, write a function to merge overlapping intervals efficiently.

#Arrays #Sorting #Intervals

Practice

Data Engineer • Coding • medium

Implement a sliding window rate limiter for the OpenAI API that can handle high concurrency.

#Data Structures #Concurrency #Queues

Practice

Data Engineer • Coding • medium

Write a Python generator function to parse a multi-terabyte JSONL file of Common Crawl data, extract the 'text' field, and yield chunks of exactly 10,000 tokens using a provided tokenizer function.

#Generators #Memory Management #File I/O

Practice

Data Engineer • Coding • medium

Implement a custom MapReduce-like framework in Python using multiprocessing to count token frequencies across multiple large text files.

#Multiprocessing #Concurrency #MapReduce

Practice

Data Engineer • Coding • hard

Find the top K most frequent tokens in a continuous, infinite stream of text data.

#Streaming Algorithms #Heaps #Count-Min Sketch

Practice

Data Engineer • Coding • medium

Implement a Trie data structure to efficiently scan and redact a dynamic list of blocked phrases from training data strings.

#Trees #String Matching #Trie

Practice

Data Engineer • Coding • medium

Write an asynchronous Python script using asyncio and aiohttp to download millions of images from a list of URLs, ensuring a maximum of 100 concurrent requests and implementing exponential backoff for 429 errors.

#Asyncio #Concurrency #Error Handling

Practice

Data Engineer • Coding • medium

Write a SQL query to calculate the 7-day rolling average of API requests per user, ensuring days with zero requests are factored into the average.

#Window Functions #CTEs #Date Generation

Practice

Data Engineer • Coding • hard

Given a table of user prompts, write a SQL query to find users who have submitted prompts in at least 3 different languages within any rolling 24-hour window.

#Self Joins #Window Functions #Time-Series

Practice

Data Engineer • Coding • hard

Write a SQL query to identify ChatGPT session boundaries. A new session starts if there is more than 30 minutes of inactivity between prompts from the same user.

#Gaps and Islands #Window Functions #LAG/LEAD

Practice

Data Engineer • Coding • medium

Given a table of model training runs (run_id, model_size, gpu_count, tokens_processed, duration_seconds), write a query to find the run with the highest throughput (tokens per second per GPU) for each model size.

#Ranking #Window Functions #Math

Practice

Data Engineer • Coding • medium

Write a SQL query to find the median token count per prompt for each day in the last month.

#Percentiles #Aggregation #Date Functions

Practice

Data Engineer • System Design • hard

Design a data pipeline to ingest, deduplicate, and tokenize 10 petabytes of web text data for LLM pre-training. How do you handle exact and fuzzy deduplication at this massive scale?

#Distributed Systems #Data Pipelines #MinHash/LSH #Spark/Ray

Practice

Data Engineer • System Design • hard

Design a real-time monitoring system for ChatGPT API latency and error rates. The system needs to aggregate metrics per minute, per user tier, and per model, handling millions of requests per second.

#Stream Processing #Kafka #Time-Series Databases #High Throughput

Practice

Data Engineer • System Design • hard

Design an ETL pipeline that takes newly published research papers, generates embeddings using our API, and updates a vector database for RAG (Retrieval-Augmented Generation) without causing downtime.

#ETL #Vector Databases #Embeddings #Idempotency

Practice

Data Engineer • System Design • hard

Design a data ingestion pipeline to process petabytes of web crawl data (e.g., CommonCrawl) for LLM pre-training.

#Distributed Systems #Data Ingestion #Scalability #Storage

Practice

Data Engineer • System Design • hard

Design a near real-time telemetry system to track API token usage and latency across millions of ChatGPT users.

#Streaming #Kafka #Real-time Analytics #Metrics

Practice

Data Engineer • System Design • hard

Design a distributed deduplication system to remove exact and near-duplicate documents from a 10TB text dataset.

#Algorithms #Big Data #MinHash #LSH

Practice

Data Engineer • System Design • medium

Design a pipeline to continuously update a vector database with new embeddings generated from daily news articles.

#Vector Databases #Embeddings #ETL #Orchestration

Practice

Data Engineer • System Design • hard

How would you design a system to detect and scrub PII (Personally Identifiable Information) from training datasets at scale?

#Data Privacy #NLP #Distributed Processing #Security

Practice

Data Engineer • System Design • medium

Explain how you would model the data warehouse schema for tracking prompt and completion tokens across different API endpoints.

#Data Modeling #Star Schema #Fact/Dimension Tables

Practice

Data Engineer • System Design • hard

Design a data pipeline to ingest, filter for PII, deduplicate, and tokenize 10PB of Common Crawl data for training a next-generation LLM.

#Big Data #Distributed Systems #Data Pipelines #Spark/Ray

Practice

Data Engineer • System Design • medium

Design a real-time analytics and monitoring system for the OpenAI API to track latency, error rates, and token usage globally.

#Stream Processing #Kafka #Time-Series DB #Monitoring

Practice

Data Engineer • System Design • hard

How would you design a highly available, low-latency system to track and enforce token rate limits for OpenAI API users across multiple global regions?

#Distributed Caching #Redis #Consistency #Rate Limiting

Practice

Data Engineer • System Design • hard

Design a pipeline to continuously ingest newly published news articles, generate embeddings using an OpenAI model, and update a vector database for a real-time RAG application.

#Vector Databases #Embeddings #Event-Driven Architecture #RAG

Practice

Data Engineer • System Design • medium

Architect a system to collect, anonymize, and store telemetry and conversation data from ChatGPT clients for model fine-tuning, ensuring strict privacy compliance.

#Data Privacy #Batch Processing #Data Warehousing #Security

Practice

Data Engineer • System Design • hard

Design an automated evaluation pipeline that runs nightly benchmarks (e.g., MMLU, HumanEval) on the latest model checkpoints and alerts researchers to regressions.

#Orchestration #CI/CD for ML #Airflow #Compute Allocation

Practice

Data Engineer • System Design • hard

How would you design a distributed web scraper to crawl millions of specific domains daily, ensuring data freshness while respecting robots.txt and avoiding IP bans?

#Web Scraping #Distributed Queues #Proxies #Politeness

Practice

Data Engineer • Technical • medium

Explain how you would optimize a PySpark job that is experiencing severe data skew during a join operation between a massive table of web documents and a smaller table of domain reputation scores.

#Spark #Performance Tuning #Distributed Computing

Practice

Data Engineer • Technical • hard

How would you design a system to automatically detect and filter out PII (Personally Identifiable Information) from a continuous stream of training data before it hits our secure storage?

#Data Privacy #PII #Stream Processing #Machine Learning

Practice

Data Engineer • Technical • medium

Compare and contrast using Parquet vs. Avro vs. JSONL for storing our intermediate model checkpoints and training datasets. Which would you choose for a read-heavy analytical workload vs. a write-heavy logging workload?

#File Formats #Parquet #Avro #Optimization

Practice

Data Engineer • Technical • medium

Write a SQL query to find the top 1% of users by token consumption over the last 30 days, partitioned by pricing tier.

#Window Functions #Percentiles #Aggregations

Practice

Data Engineer • Technical • hard

Given a table of user interactions, write a query to calculate the session length for each user, where a session ends after 30 minutes of inactivity.

#Sessionization #Window Functions #CTEs

Practice

Data Engineer • Technical • hard

How would you optimize a slow-running SQL query that joins a massive `api_logs` table with a `users` table, where the `api_logs` table is highly skewed?

#Query Optimization #Data Skew #Joins

Practice

Data Engineer • Technical • medium

Write a query to find the daily retention rate of users who used a specific model (e.g., GPT-4) in their first week.

#Cohorts #Retention #Self Joins

Practice

Data Engineer • Technical • hard

Write a SQL query to identify 'bursty' API users—those who consume more than 10x their daily average tokens within a single hour.

#Advanced Aggregations #Window Functions #Time Series

Practice

Data Engineer • Technical • hard

Explain how you would handle an OutOfMemory (OOM) error in a Spark job processing a highly skewed dataset.

#Apache Spark #OOM #Data Skew #Performance Tuning

Practice

Data Engineer • Technical • medium

Compare and contrast Apache Spark and Ray. When would you choose Ray over Spark for data processing at OpenAI?

#Apache Spark #Ray #Architecture #Machine Learning

Practice

Data Engineer • Technical • hard

How do you ensure exactly-once processing semantics in a Kafka to Spark Streaming pipeline?

#Kafka #Spark Streaming #Exactly-Once #Checkpoints

Practice

Data Engineer • Technical • medium

Describe your strategy for partitioning a massive Delta Lake table containing daily chat logs to optimize for both point-in-time and user-specific queries.

#Delta Lake #Partitioning #Z-Ordering #Storage Optimization

Practice

Data Engineer • Technical • medium

What are the trade-offs between Parquet and JSONL formats for storing LLM training data?

#File Formats #Parquet #JSONL #Compression

Practice

Data Engineer • Technical • medium

How would you implement a backfill strategy for a data pipeline that calculates daily active users, if the logic changed and needs to be applied to the last 2 years of data?

#Backfilling #Airflow #Idempotency #ETL

Practice

Data Engineer • Technical • medium

Explain how Broadcast Joins work in Spark and when they should be avoided.

#Apache Spark #Joins #Optimization

Practice

Data Engineer • Technical • medium

How do you monitor and alert on data drift in a pipeline feeding a machine learning model?

#Data Drift #Monitoring #MLOps #Statistics

Practice

Data Engineer • Technical • medium

What metrics would you track to ensure the quality of a web-scraped dataset intended for model training?

#Data Quality #Metrics #NLP

Practice

Data Engineer • Technical • hard

How do you handle schema evolution in a streaming data pipeline without breaking downstream consumers?

#Schema Evolution #Streaming #Avro #Protobuf

Practice

Data Engineer • Technical • medium

Design an idempotency mechanism for a data pipeline that occasionally fails and retries midway through processing.

#Idempotency #ETL #Fault Tolerance

Practice

Data Engineer • Technical • hard

Explain how you would handle severe data skew in a Spark join operation involving a massive table of user prompts and a smaller table of flagged safety keywords.

#Apache Spark #Data Skew #Performance Tuning

Practice

Data Engineer • Technical • medium

Your Spark job processing tokenized text is experiencing frequent OutOfMemory (OOM) errors during a shuffle phase. Walk me through your debugging and optimization steps.

#Apache Spark #Memory Management #Debugging

Practice

Data Engineer • Technical • hard

Describe the algorithmic and infrastructural differences between implementing exact deduplication versus fuzzy deduplication on a petabyte-scale text dataset.

#Deduplication #Hashing #LSH #Scale

Practice

Data Engineer • Technical • hard

What heuristics, statistical methods, and ML-based approaches would you use to detect and filter out low-quality, toxic, or repetitive text from a pre-training dataset?

#NLP #Data Cleaning #Heuristics #Machine Learning

Practice

Data Engineer • Technical • medium

Explain the differences between Parquet and Avro formats. In what specific scenarios would you choose one over the other for storing tokenized LLM training data?

#File Formats #Parquet #Avro #Columnar vs Row

Practice

Data Engineer • Technical • hard

OpenAI uses Ray heavily for distributed computing. Explain how Ray's architecture differs from Apache Spark, and in what scenarios Ray is a better choice for data processing.

#Ray #Apache Spark #Architecture #ML Workloads

Practice

Data Engineer • Technical • medium

Describe how you would ensure idempotency in a data pipeline that processes billing events for OpenAI API usage, ensuring no user is double-charged in case of pipeline retries.

#Idempotency #Data Pipelines #Transactional Systems

Practice

OpenAI

The Interview Loop

Recruiter Screen (30 min)

Technical Loop (3-4 Rounds)

Interview Question Bank

Tell me about a time you had to make a technical tradeoff between data quality and pipeline speed. How did you decide, and what was the outcome?

OpenAI moves very fast. Describe a situation where you had to build a data pipeline with constantly changing requirements and incomplete upstream data schemas. How did you ensure reliability?

Tell me about a time you identified a major bottleneck or inefficiency in a data system that no one else noticed. How did you go about fixing it and getting buy-in from the team?

Describe a time you had to debug a silent data corruption issue. How did you detect it and fix it?

OpenAI moves incredibly fast. Tell me about a time you had to make a technical trade-off between shipping quickly and building a perfectly scalable system.

Tell me about a time you disagreed with a researcher or data scientist about how data should be processed or modeled. How did you resolve it?

At OpenAI, safety and alignment are critical. How would you handle a situation where you discovered a flaw in a data pipeline that might have introduced biased or unsafe data into a training run?

Describe a project where you had to learn a completely new technology or framework on the fly to solve a critical business problem.

Tell me about the most complex data pipeline you've ever built. What made it complex, and what would you do differently today?

Tell me about a time you had to optimize a data pipeline that was failing or severely bottlenecked under scale. What was the root cause and how did you fix it?

Describe a situation where you had to make a difficult trade-off between data quality and processing speed/delivery time. How did you make your decision?

OpenAI moves very fast and requirements can change rapidly. Tell me about a time you had to deliver a critical project with ambiguous requirements and a tight deadline.

Tell me about a time you disagreed with a senior engineer or stakeholder about a technical design or architecture. How did you approach the disagreement and what was the outcome?

Describe a time you discovered a critical bug or data corruption issue in your pipeline after it was already in production. How did you handle the incident?

What is the most complex distributed systems problem you have ever debugged? Walk me through your troubleshooting process from alert to resolution.

Tell me about a time you proactively identified a bottleneck or technical debt in your team's infrastructure and took the initiative to fix it without being asked.

Why do you want to join OpenAI specifically, and how do you see the role of a Data Engineer evolving as AI models become more capable of writing code and analyzing data?

Write a Python function to parse a massive JSONL file containing web crawl data, filter out documents with a high proportion of non-alphanumeric characters (spam/code), and yield batches of clean text. Assume the file is significantly larger than available RAM.

Given a table of API requests (request_id, user_id, model_name, tokens_used, timestamp), write a SQL query to find the top 3 users by token usage for each model over the last 30 days, but only include users who have used at least two different models.

Implement a rate limiter for our API. Given a stream of requests, allow a maximum of N requests per minute per user. If a user exceeds this, drop the requests. Optimize for high concurrency and minimal latency.

Given a list of conversational turns (user prompt, assistant response) with timestamps and session IDs, write a function to reconstruct the conversation threads. Note that some turns might arrive out of order or have missing timestamps.

Design the database schema and write the SQL to track RLHF (Reinforcement Learning from Human Feedback) tasks. We have prompts, multiple model completions, and human rankings. How do you query for the inter-annotator agreement rate?

Write a function to merge overlapping time intervals. We use this to calculate the total active compute time for GPU clusters given a log of job start and end times.

Write a Python generator to efficiently parse a 500GB JSONL file containing conversation logs without loading the whole file into memory.

Given a stream of API requests, implement a sliding window rate limiter.

Implement a function to merge overlapping text intervals (e.g., highlighting spans in a document).

Write a distributed map-reduce job from scratch in Python using multiprocessing to count token frequencies across multiple files.

Given a list of data pipeline tasks with dependencies, write a function to return a valid execution order.

Implement an LRU cache with a TTL (Time To Live) for caching database queries.

Write a script to sample exactly K random lines from a massive text file in a single pass.

Implement a MinHash and Locality-Sensitive Hashing (LSH) algorithm to find near-duplicate documents in a massive corpus of web text.

Given a list of text spans representing PII (Personally Identifiable Information) redactions with start and end indices, write a function to merge overlapping intervals efficiently.

Implement a sliding window rate limiter for the OpenAI API that can handle high concurrency.

Write a Python generator function to parse a multi-terabyte JSONL file of Common Crawl data, extract the 'text' field, and yield chunks of exactly 10,000 tokens using a provided tokenizer function.

Implement a custom MapReduce-like framework in Python using multiprocessing to count token frequencies across multiple large text files.

Find the top K most frequent tokens in a continuous, infinite stream of text data.

Implement a Trie data structure to efficiently scan and redact a dynamic list of blocked phrases from training data strings.

Write an asynchronous Python script using asyncio and aiohttp to download millions of images from a list of URLs, ensuring a maximum of 100 concurrent requests and implementing exponential backoff for 429 errors.

Write a SQL query to calculate the 7-day rolling average of API requests per user, ensuring days with zero requests are factored into the average.

Given a table of user prompts, write a SQL query to find users who have submitted prompts in at least 3 different languages within any rolling 24-hour window.

Write a SQL query to identify ChatGPT session boundaries. A new session starts if there is more than 30 minutes of inactivity between prompts from the same user.

Given a table of model training runs (run_id, model_size, gpu_count, tokens_processed, duration_seconds), write a query to find the run with the highest throughput (tokens per second per GPU) for each model size.

Write a SQL query to find the median token count per prompt for each day in the last month.

Design a data pipeline to ingest, deduplicate, and tokenize 10 petabytes of web text data for LLM pre-training. How do you handle exact and fuzzy deduplication at this massive scale?

Design a real-time monitoring system for ChatGPT API latency and error rates. The system needs to aggregate metrics per minute, per user tier, and per model, handling millions of requests per second.

Design an ETL pipeline that takes newly published research papers, generates embeddings using our API, and updates a vector database for RAG (Retrieval-Augmented Generation) without causing downtime.

Design a data ingestion pipeline to process petabytes of web crawl data (e.g., CommonCrawl) for LLM pre-training.

Design a near real-time telemetry system to track API token usage and latency across millions of ChatGPT users.

Design a distributed deduplication system to remove exact and near-duplicate documents from a 10TB text dataset.

Design a pipeline to continuously update a vector database with new embeddings generated from daily news articles.

How would you design a system to detect and scrub PII (Personally Identifiable Information) from training datasets at scale?

Explain how you would model the data warehouse schema for tracking prompt and completion tokens across different API endpoints.

Design a data pipeline to ingest, filter for PII, deduplicate, and tokenize 10PB of Common Crawl data for training a next-generation LLM.

Design a real-time analytics and monitoring system for the OpenAI API to track latency, error rates, and token usage globally.

How would you design a highly available, low-latency system to track and enforce token rate limits for OpenAI API users across multiple global regions?

Design a pipeline to continuously ingest newly published news articles, generate embeddings using an OpenAI model, and update a vector database for a real-time RAG application.

Architect a system to collect, anonymize, and store telemetry and conversation data from ChatGPT clients for model fine-tuning, ensuring strict privacy compliance.

Design an automated evaluation pipeline that runs nightly benchmarks (e.g., MMLU, HumanEval) on the latest model checkpoints and alerts researchers to regressions.

How would you design a distributed web scraper to crawl millions of specific domains daily, ensuring data freshness while respecting robots.txt and avoiding IP bans?

Explain how you would optimize a PySpark job that is experiencing severe data skew during a join operation between a massive table of web documents and a smaller table of domain reputation scores.

How would you design a system to automatically detect and filter out PII (Personally Identifiable Information) from a continuous stream of training data before it hits our secure storage?

Compare and contrast using Parquet vs. Avro vs. JSONL for storing our intermediate model checkpoints and training datasets. Which would you choose for a read-heavy analytical workload vs. a write-heavy logging workload?

Write a SQL query to find the top 1% of users by token consumption over the last 30 days, partitioned by pricing tier.

Given a table of user interactions, write a query to calculate the session length for each user, where a session ends after 30 minutes of inactivity.

How would you optimize a slow-running SQL query that joins a massive `api_logs` table with a `users` table, where the `api_logs` table is highly skewed?

Write a query to find the daily retention rate of users who used a specific model (e.g., GPT-4) in their first week.

Write a SQL query to identify 'bursty' API users—those who consume more than 10x their daily average tokens within a single hour.

Explain how you would handle an OutOfMemory (OOM) error in a Spark job processing a highly skewed dataset.

Compare and contrast Apache Spark and Ray. When would you choose Ray over Spark for data processing at OpenAI?

How do you ensure exactly-once processing semantics in a Kafka to Spark Streaming pipeline?

Describe your strategy for partitioning a massive Delta Lake table containing daily chat logs to optimize for both point-in-time and user-specific queries.

What are the trade-offs between Parquet and JSONL formats for storing LLM training data?

How would you implement a backfill strategy for a data pipeline that calculates daily active users, if the logic changed and needs to be applied to the last 2 years of data?

Explain how Broadcast Joins work in Spark and when they should be avoided.

How do you monitor and alert on data drift in a pipeline feeding a machine learning model?