Databricks
Unified analytics platform built on Apache Spark for data engineering and ML.
4 Rounds
~21 Days
Hard
The Interview Loop
Recruiter Screen (30 min)
Standard fit check, behavioral questions, and resume overview.
Technical Loop (3-4 Rounds)
Deep dive into domain knowledge, coding, and system design.
Interview Question Bank
Cloud Engineer
•
Behavioral
•
medium
Databricks values 'Let the Data Decide.' Can you share an example of when you used data or metrics to drive an infrastructure architecture decision or resolve a team disagreement?
#Data-Driven Decisions
#Conflict Resolution
#Metrics
Cloud Engineer
•
Behavioral
•
medium
Describe a situation where you had to push back on a feature release or architectural change because it didn't meet reliability or security standards.
#Reliability
#Security
#Communication
#Pushback
Cloud Engineer
•
Behavioral
•
medium
Tell me about a time you had to troubleshoot a complex, intermittent infrastructure issue that was impacting customer workloads. How did you isolate the root cause?
#Troubleshooting
#Customer Obsession
#Incident Management
Cloud Engineer
•
Coding
•
medium
Write a Python or Go script to interact with a cloud provider's API to find and terminate all compute instances missing a specific mandatory tagging standard, while gracefully handling API rate limits and pagination.
#Python/Go
#API Integration
#Rate Limiting
#Pagination
Cloud Engineer
•
Coding
•
hard
Implement a distributed rate limiter in Go or Python that could be used to throttle incoming API requests to a cloud provisioning service to prevent quota exhaustion.
#Concurrency
#Distributed Systems
#Rate Limiting
#Redis
Cloud Engineer
•
Coding
•
easy
Given a list of JSON objects representing cloud resource logs, write a function to parse the logs, aggregate the total compute cost per team, and return the top 3 most expensive teams.
#JSON Parsing
#Aggregation
#Data Structures
Cloud Engineer
•
System Design
•
medium
Design an automated log ingestion and alerting pipeline for cloud infrastructure events (e.g., CloudTrail, VPC Flow Logs) that scales to petabytes of data.
#Logging
#Alerting
#Big Data
#CloudTrail
Cloud Engineer
•
System Design
•
hard
Design a highly available, cross-region disaster recovery strategy for a Kubernetes-based microservices architecture serving the Databricks control plane.
#Kubernetes
#Disaster Recovery
#High Availability
#Global Routing
Cloud Engineer
•
System Design
•
hard
Design a secure, multi-tenant cloud architecture for Databricks workspaces where the control plane is hosted in our account and the data plane runs in the customer's AWS or Azure account.
#Multi-tenancy
#AWS/Azure
#Control Plane vs Data Plane
#Security
Cloud Engineer
•
Technical
•
medium
Walk me through the lifecycle of a Kubernetes Pod. What happens at the network layer when two pods on different nodes communicate?
#Kubernetes
#CNI
#Networking
#Pod Lifecycle
Cloud Engineer
•
Technical
•
medium
Explain the concept of Cross-Account IAM Roles in AWS. How would you securely configure a Databricks service to access an S3 bucket in a completely separate customer AWS account?
#AWS IAM
#Cross-Account Access
#S3
#Security
Cloud Engineer
•
Technical
•
hard
How do you manage and scale Terraform across hundreds of cloud accounts? Describe your approach to state management, module versioning, and CI/CD integration.
#Terraform
#CI/CD
#State Management
#Scalability
Cloud Engineer
•
Technical
•
hard
Describe how you would implement zero-downtime database migrations for a critical cloud service. What are the risks and how do you mitigate them?
#Zero-Downtime
#Migrations
#State Management
Cloud Engineer
•
Technical
•
medium
Explain how you would establish secure, private connectivity between a Databricks control plane VPC and a customer's data plane VPC without exposing traffic to the public internet.
#AWS PrivateLink
#Azure Private Link
#VPC Peering
#Network Routing
Cloud Engineer
•
Technical
•
hard
A customer's Spark cluster is failing to provision EC2 instances in their AWS environment. Walk me through your troubleshooting steps, considering IAM permissions, VPC limits, and AWS API quotas.
#AWS EC2
#IAM
#Quotas
#Spark Provisioning
Data Engineer
•
Behavioral
•
medium
Databricks highly values 'Customer Obsession'. Tell me about a time you had to pivot a data engineering project completely because the customer's requirements or business needs changed.
#Customer Obsession
#Adaptability
#Communication
#Agile
Data Engineer
•
Behavioral
•
medium
Tell me about a time you identified a major bottleneck in a legacy data pipeline. How did you convince your team to adopt your proposed architectural changes?
#Influence
#Problem Solving
#Initiative
#Mentorship
Data Engineer
•
Coding
•
medium
Write a Python script to flatten a deeply nested JSON object representing e-commerce transactions into a tabular format suitable for a Pandas or Spark DataFrame.
#Python
#Recursion
#Data Parsing
#JSON
Data Engineer
•
Coding
•
medium
Given a list of user session logs with start and end timestamps, write a Python function to find the peak concurrent active users.
#Python
#Intervals
#Sorting
#Time Complexity
Data Engineer
•
Coding
•
hard
Write a SQL query to identify 'sessionization' of user clicks. A new session starts if there is a gap of more than 30 minutes between clicks for a given user.
#Window Functions
#Sessionization
#CTEs
#LAG/LEAD
Data Engineer
•
Coding
•
medium
Given a table of employee salaries and departments, write a SQL query to find the top 3 highest paid employees in each department without using the LIMIT clause.
#Window Functions
#Ranking
#Aggregations
Data Engineer
•
System Design
•
hard
Design a real-time analytics platform for IoT telemetry data using Databricks. Walk through the ingestion, processing, and serving layers using the Medallion architecture.
#Streaming
#Medallion Architecture
#Kafka
#Structured Streaming
#Delta Live Tables
Data Engineer
•
System Design
•
hard
Design a batch ETL pipeline to process 10TB of daily log data. The business needs to query this data interactively with sub-second latency. How do you model the data and optimize the storage?
#Batch Processing
#Data Modeling
#Performance Optimization
#Lakehouse
Data Engineer
•
System Design
•
medium
How would you handle late-arriving data and out-of-order events in a Spark Structured Streaming pipeline? Explain the concept of watermarking.
#Structured Streaming
#Watermarking
#Late Data
#Event Time
Data Engineer
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Technical
•
medium
What is Adaptive Query Execution (AQE) in Spark 3.x? Explain the three main features it introduces and how they improve query performance.
#AQE
#Performance Tuning
#Query Plans
#Shuffle Partitions
Data Engineer
•
Technical
•
medium
Compare and contrast Z-Ordering and standard partitioning in Delta Lake. When would you use one over the other?
#Z-Ordering
#Partitioning
#Data Skipping
#Delta Lake
Data Engineer
•
Technical
•
medium
Explain how Delta Lake implements ACID transactions on top of cloud object storage. How do the transaction log and checkpointing work?
#Delta Lake
#ACID
#Parquet
#Transaction Log
#Concurrency
Data Engineer
•
Technical
•
hard
Walk me through the exact execution lifecycle of a Spark application from the moment you submit it using spark-submit to the final output. Mention the Driver, Executors, Tasks, and Stages.
#Distributed Systems
#DAG
#Task Scheduling
#Cluster Manager
Data Engineer
•
Technical
•
hard
You have a Spark job joining a massive fact table with a dimension table, and it is failing with an OutOfMemory (OOM) error due to data skew. How do you diagnose and fix this?
#Data Skew
#OOM
#Salting
#AQE
#Broadcast Joins
Data Engineer
•
Technical
•
medium
How do you handle schema evolution in a continuous ETL pipeline writing to Delta Lake? What happens if an upstream source drops a column or changes a data type?
#Schema Evolution
#Data Quality
#ETL
#Delta Lake
Data Scientist
•
Behavioral
•
easy
Tell me about a time you discovered a significant flaw in your data or model after it was already shared with stakeholders. How did you handle it?
#Integrity
#Communication
#Problem Solving
#Ownership
Data Scientist
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Behavioral
•
medium
Databricks moves very fast. Tell me about a time you had to deliver an ML model or analysis under a very tight deadline with ambiguous requirements.
#Ambiguity
#Delivery
#Prioritization
#Bias for Action
Data Scientist
•
Coding
•
hard
Given a table of `user_logins` (user_id, login_timestamp), write a SQL query to find the maximum number of consecutive days each user has logged into the Databricks platform.
#SQL
#Window Functions
#Gaps and Islands
Data Scientist
•
Coding
•
medium
Given a table of `job_runs` (job_id, workspace_id, start_time, end_time, status), write a SQL query to find the workspace with the highest rate of failed jobs in the last 7 days, considering only workspaces with at least 100 runs.
#SQL
#Aggregations
#CTEs
#Filtering
Data Scientist
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Coding
•
hard
Write a Python algorithm to implement a stratified sampling method for a dataset that is too large to fit into memory, reading it chunk by chunk.
#Python
#Streaming
#Reservoir Sampling
#Memory Management
Data Scientist
•
Coding
•
medium
Given a list of strings representing Databricks notebook execution logs, write a Python function to extract the most frequent error codes and return them sorted by frequency. Assume logs are unstructured text.
#Python
#String Parsing
#Hash Maps
#Regex
Data Scientist
•
Coding
•
medium
Write a PySpark script to calculate the 7-day rolling average of cluster compute costs per customer, given a massive dataframe of daily billing events.
#PySpark
#Window Functions
#Distributed Computing
Data Scientist
•
System Design
•
hard
Design an LLM-powered coding assistant for Databricks notebooks (similar to Databricks Assistant). Focus on the telemetry data you would collect to evaluate the model's performance offline and online.
#LLMs
#Telemetry
#Online Evaluation
#Product Analytics
Data Scientist
•
System Design
•
hard
Design a machine learning system to predict which Databricks customers are likely to churn in the next 30 days. Discuss feature engineering, model selection, and how you would scale the inference using Delta Lake.
#Churn Prediction
#Delta Lake
#Scalability
#Feature Engineering
Data Scientist
•
Technical
•
medium
Explain the difference between a broadcast hash join and a sort-merge join in Spark. When would you force a broadcast join in a data science pipeline?
#Spark Joins
#Optimization
#Big Data
#Query Planning
Data Scientist
•
Technical
•
medium
We want to test a new auto-scaling algorithm for Databricks SQL warehouses. How would you design the A/B test? What are your primary and secondary metrics?
#A/B Testing
#Experimentation
#Metrics
#Cloud Infrastructure
Data Scientist
•
Technical
•
hard
Explain how Apache Spark handles out-of-memory (OOM) errors during a wide transformation. How would you diagnose and fix an OOM error in a PySpark ML pipeline?
#Apache Spark
#OOM
#Debugging
#Distributed ML
Data Scientist
•
Technical
•
medium
How does Delta Lake handle ACID transactions under the hood? Explain how you would use time travel to recover a dropped ML feature table.
#Delta Lake
#ACID
#Time Travel
#Storage
Data Scientist
•
Technical
•
medium
How would you use MLflow to manage the lifecycle of a model that requires frequent retraining? Describe the architecture of your CI/CD pipeline for this model.
#MLflow
#Model Registry
#CI/CD
#Model Lifecycle
Data Scientist
•
Technical
•
hard
You are training a distributed XGBoost model on a massive dataset using Spark. The training job is taking too long and some executors are idling. How do you identify the bottleneck and optimize the training process?
#Distributed ML
#XGBoost
#Spark
#Performance Tuning
Machine Learning Engineer
•
Behavioral
•
medium
Databricks heavily values 'Truth-seeking'. Tell me about a time when you had to challenge a deeply held assumption in your team's ML architecture or model choice. How did you prove your case?
#Core Values
#Communication
#Data-Driven Decisions
Machine Learning Engineer
•
Behavioral
•
hard
Tell me about a time you had to dive deep into a complex distributed system bug that was silently degrading your machine learning model's performance in production.
#Debugging
#Production ML
#Problem Solving
Machine Learning Engineer
•
Coding
•
medium
Implement a thread-safe Rate Limiter class for an API. It should support a method `is_allowed(client_id)` which returns True if the client has made fewer than N requests in the last M seconds, and False otherwise.
#Concurrency
#System Design
#Queues
Machine Learning Engineer
•
Coding
•
medium
Implement a LazyArray class in Python that takes an array of integers. It should support two operations: map(function) which applies a function to all elements, and indexOf(value) which returns the index of the first occurrence of the value. The map operation must be lazy (deferred execution) and optimized so that indexOf does not compute unnecessary elements.
#Object-Oriented Design
#Lazy Evaluation
#Arrays
Machine Learning Engineer
•
Coding
•
hard
Given a list of tasks with dependencies (represented as a directed graph) and the execution time for each task, write a function to calculate the minimum time required to complete all tasks assuming you have infinite parallel workers.
#Graphs
#Topological Sort
#Dynamic Programming
Machine Learning Engineer
•
Coding
•
medium
Given two sparse matrices A and B represented as lists of non-zero elements (row, col, value), write a function to compute their product. How would you optimize this for a distributed environment?
#Math
#Hash Maps
#Distributed Computing
Machine Learning Engineer
•
Coding
•
medium
Given a stream of user activity logs (timestamp, user_id, action), write a function to find the longest continuous session for each user. A session ends if there is a gap of more than 30 minutes between actions.
#Sliding Window
#Hash Maps
#Sorting
Machine Learning Engineer
•
Coding
•
medium
You are given a list of intervals representing compute jobs on a cluster [start, end] and an associated CPU core requirement for each job. Write a function to determine the maximum number of CPU cores used at any point in time.
#Sweep Line
#Intervals
#Sorting
Machine Learning Engineer
•
System Design
•
medium
Design a model registry and experiment tracking system similar to MLflow. How do you handle model versioning, lineage tracking, and concurrent writes from thousands of distributed training runs?
#MLOps
#Databases
#API Design
Machine Learning Engineer
•
System Design
•
medium
Design an automated hyperparameter tuning service that can schedule and manage thousands of concurrent ML jobs. How do you allocate resources and handle early stopping for poorly performing runs?
#AutoML
#Resource Management
#Scheduling
Machine Learning Engineer
•
System Design
•
hard
Design a scalable LLM serving architecture for a multi-tenant environment. How would you handle thousands of users requesting inference from different fine-tuned versions of a base model like Llama-3?
#LLMs
#Multi-tenancy
#GPU Optimization
#Model Serving
Machine Learning Engineer
•
System Design
•
hard
Design a machine learning system to predict job/cluster failures in a distributed computing environment like Databricks. How do you handle the massive volume of telemetry data and the extreme class imbalance?
#Predictive Maintenance
#Streaming Data
#Imbalanced Data
Machine Learning Engineer
•
Technical
•
hard
Explain the differences between Data Parallelism, Tensor Parallelism, and Pipeline Parallelism. In what scenarios would you choose one over the others when training a 70B parameter model?
#Deep Learning
#Distributed Training
#LLMs
Machine Learning Engineer
•
Technical
•
medium
What are the primary bottlenecks when using Stochastic Gradient Descent (SGD) in a distributed cluster? How do algorithms like Ring-AllReduce mitigate these bottlenecks?
#Optimization Algorithms
#Networking
#Distributed Systems
Machine Learning Engineer
•
Technical
•
medium
How would you implement a distributed K-Means clustering algorithm from scratch using Spark RDDs or a MapReduce paradigm?
#Distributed Computing
#Apache Spark
#Clustering
Product Manager
•
Behavioral
•
medium
Databricks values 'Customer Obsession.' Describe a time you pivoted a product roadmap based on direct customer feedback despite strong pushback from your engineering team.
#Customer Obsession
#Roadmapping
#Prioritization
#Influence
Product Manager
•
Behavioral
•
easy
Walk me through how you balance technical debt, infrastructure scaling, and new feature development in your quarterly planning.
#Agile
#Technical Debt
#Sprint Planning
#Resource Allocation
Product Manager
•
Behavioral
•
medium
Tell me about a time when a feature you launched failed to achieve its goals. How did you measure the failure, and what was your post-mortem process?
#Failure
#Resilience
#Post-mortem
#Metrics
Product Manager
•
Behavioral
•
hard
Tell me about a time you had to align engineering, sales, and marketing on a major product launch timeline that was actively slipping.
#Cross-functional Collaboration
#Conflict Resolution
#Stakeholder Management
Product Manager
•
Coding
•
medium
Write a Python function using PySpark to read a JSON dataset of user events, filter out records with missing user_ids, and aggregate the count of specific event types per user per day.
#PySpark
#Data Processing
#Python
#ETL
Product Manager
•
Coding
•
easy
Write a SQL query to find the top 5 customers by compute usage (measured in DBUs) over the last 30 days, partitioned by workspace region.
#SQL
#Window Functions
#Data Analysis
#Billing
Product Manager
•
System Design
•
hard
Design an enterprise-grade access control system for Unity Catalog that supports both row-level and column-level security across multiple cloud providers.
#Unity Catalog
#Data Governance
#Security
#Cloud Infrastructure
Product Manager
•
System Design
•
hard
How would you design a serverless compute offering for Databricks notebooks to minimize cold start times for data scientists while managing AWS/Azure infrastructure costs?
#Serverless
#Cloud Infrastructure
#Performance
#Cost Optimization
Product Manager
•
System Design
•
medium
Design a real-time model monitoring dashboard for MLflow that alerts users when data drift or concept drift occurs in their production endpoints.
#MLOps
#MLflow
#Model Drift
#Monitoring
Product Manager
•
System Design
•
hard
Databricks recently acquired MosaicML. How would you integrate MosaicML's LLM training capabilities into the existing Databricks Machine Learning workspace to create a seamless user experience?
#Generative AI
#MLflow
#Product Integration
#User Experience
Product Manager
•
System Design
•
medium
Design a new feature for Databricks SQL that provides data analysts with actionable recommendations to optimize their slow-running queries.
#Databricks SQL
#User Experience
#Performance Optimization
#AI Assistants
Product Manager
•
Technical
•
medium
How would you measure the success and adoption of Delta Live Tables (DLT) among our existing Apache Spark user base?
#Metrics
#Delta Live Tables
#Adoption
#Data Engineering
Product Manager
•
Technical
•
medium
We are noticing a sudden 15% increase in churn rate during the 14-day free trial of Databricks. Walk me through how you would investigate the root cause and what product changes you might propose.
#Growth
#Churn Analysis
#Onboarding
#Root Cause Analysis
Product Manager
•
Technical
•
hard
You have engineering capacity to build either a native, deep integration with dbt or a new, proprietary visual data transformation UI. How do you decide which to build?
#Prioritization
#Partner Ecosystem
#Build vs Buy
#Market Dynamics
Product Manager
•
Technical
•
medium
Explain the Lakehouse architecture to a non-technical Chief Data Officer. What are the specific trade-offs and advantages compared to a traditional cloud data warehouse like Snowflake?
#Lakehouse
#Data Warehouse
#Competitive Analysis
#Delta Lake
Software Engineer
•
Behavioral
•
medium
Tell me about a time you identified a significant bottleneck in a production system and took the initiative to fix it. How did you measure the impact?
#Initiative
#Performance Optimization
#Impact
Software Engineer
•
Behavioral
•
medium
Describe a situation where you disagreed with a senior engineer or manager on a system design choice. How did you navigate the disagreement and what was the outcome?
#Communication
#Conflict Resolution
#Truth-seeking
Software Engineer
•
Behavioral
•
medium
Databricks heavily values 'First Principles' thinking. Tell me about a time you solved a complex technical problem by breaking it down to its fundamental truths rather than relying on existing analogies or standard practices.
#Problem Solving
#First Principles
#Innovation
Software Engineer
•
Coding
•
hard
Implement a Key-Value store that supports transactions with `begin()`, `commit()`, and `rollback()` methods. It must handle nested transactions efficiently.
#Hash Map
#Stack
#State Management
Software Engineer
•
Coding
•
medium
Implement a rate limiter using the Token Bucket algorithm. It should support multiple users, be thread-safe, and handle high concurrency efficiently.
#Rate Limiting
#Multithreading
#System Design
Software Engineer
•
Coding
•
medium
Given a list of tasks with dependencies (represented as a directed graph) and execution times for each task, write a function to find the minimum time required to complete all tasks assuming infinite parallel workers.
#Graph Theory
#Topological Sort
#Dynamic Programming
Software Engineer
•
Coding
•
medium
Implement a data structure that supports `insert(key, value)`, `get(key)`, and `setAll(value)` all in O(1) time complexity.
#Hash Map
#Versioning
#Data Structures
Software Engineer
•
Coding
•
hard
Implement a concurrent web crawler. Given a starting URL and a maximum depth, crawl the web pages efficiently using multiple threads without visiting the same page twice.
#Multithreading
#Graph Traversal
#Synchronization
Software Engineer
•
Coding
•
medium
Given a string containing parentheses and lowercase characters, remove the minimum number of invalid parentheses to make the string valid. Return any valid result.
#Strings
#Stack
Software Engineer
•
Coding
•
medium
Implement a Lazy Iterable class that takes a list of iterables and a mapping function, and evaluates the elements lazily. This simulates how Spark RDD transformations operate before an action is called.
#Iterators
#Lazy Evaluation
#Generators
Software Engineer
•
System Design
•
hard
Design a distributed job execution engine similar to Apache Spark. How would you handle task scheduling, worker node failures, and data shuffling between stages?
#Distributed Systems
#Fault Tolerance
#DAG Scheduling
Software Engineer
•
System Design
•
hard
Design the backend for Databricks Collaborative Notebooks. Multiple users can edit the same notebook concurrently, execute code cells, and see the output in real-time.
#Operational Transformation
#WebSockets
#Concurrency
Software Engineer
•
System Design
•
hard
Design an auto-scaling service for Databricks clusters. The service needs to monitor cluster utilization and dynamically add or remove cloud instances (e.g., EC2) based on workload demands while minimizing cost.
#Cloud Infrastructure
#Auto-scaling
#Resource Management
Software Engineer
•
System Design
•
hard
Design a high-throughput, low-latency distributed message queue (similar to Kafka) that guarantees at-least-once delivery.
#Distributed Systems
#Messaging
#Replication
Software Engineer
•
Technical
•
hard
Explain how you would diagnose and resolve a Spark application that is suffering from severe data skew and frequent OutOfMemory (OOM) errors during a large join operation.
#Apache Spark
#Performance Tuning
#Distributed Computing
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