Best and important databricks interview questions and answers
Here are some common Databricks interview questions and answers to help you prepare.
Basic Level Questions
1. What is Databricks?
Databricks is an Apache Spark-based unified analytics platform. It provides a cloud-based environment for data engineering, data science and machine learning.
2. What are the benefits of using Databricks?
Databricks offers several benefits, including scalability, ease of use, integration with other tools and a collaborative platform.
3. What are the different types of clusters in Databricks?
Databricks offers various types of clusters, including All-Purpose, Machine Learning and Job clusters. Each type is optimized for a specific the amount of work.
4. What are notebooks in Databricks?
Notebooks are collaborative environments for data exploration, visualization and coding in Python, Scala, R, and SQL.
5. What are workspaces in Databricks?
Workspaces are isolated environments for managing different projects and teams.
6. What is the difference between a dataframe and a dataset in Databricks?
A dataframe is a distributed collection of data organized into columns and rows. A dataset is a reference to a data source, like a file or table.
7. What are the different ways to access data in Databricks?
You can access data in Databricks from various sources, including cloud storage, databases, and streaming platforms.
8. What is Delta Lake?
Delta Lake is an open-source storage format for data lakes that offers ACID transactions, data versioning, and high performance.
9. What are the different libraries available in Databricks?
Databricks provides access to various libraries for data processing, machine learning, and other tasks.
10. What are some use cases for Databricks?
Databricks can be used for various tasks, including data warehousing, data analytics, machine learning, and real-time processing.
Intermediate Level Questions
1. What are the different ways to manage clusters in Databricks?
Clusters can be managed through the UI, REST API, CLI, and notebooks.
2. How do you handle security in Databricks?
Databricks offers various security features, including access control, encryption, and monitoring.
3. How do you scale clusters in Databricks?
Clusters can be scaled automatically based on workload or manually using the UI or API.
4. What are Jobs in Databricks?
Jobs are scheduled workflows that automate data processing tasks.
5. How do you handle streaming data in Databricks?
Databricks supports various streaming protocols and libraries, including Kafka and Spark Streaming.
6. How do you share code and collaborate in Databricks?
Databricks integrates with Git for version control and collaboration.
7. How do you monitor and troubleshoot applications in Databricks?
Databricks offers various monitoring tools and logs for debugging and troubleshooting.
8. What are the different ways to deploy models in Databricks?
Models can be deployed as MLflow models, REST APIs, or as part of a pipeline.
9. How do you optimize performance in Databricks?
Databricks provides various tools for optimizing performance, including tuning cluster configurations and using libraries like Koalas.
10. What are the different pricing models for Databricks?
Databricks offers various pricing models based on usage and resources.
Advanced Level Questions
1. How do you use Delta Lake for data warehousing?
Delta Lake offers features like time travel and schema evolution, making it suitable for data warehousing.
2. How do you build real-time applications with Databricks?
Databricks provides tools like Structured Streaming and MLflow for building real-time applications.
3. How do you integrate Databricks with other services?
Databricks integrates with various services, including Azure services, AWS services, and third-party tools.
4. How do you build and deploy machine learning models with Databricks?
Databricks provides tools for building and deploying ML models, including MLflow and AutoML.
5. How do you use Databricks for data quality management?
Databricks offers various tools for data quality management, including data validation, testing, and monitoring.
6. How do you implement data governance in Databricks?
Databricks provides features like access control, encryption, and auditing for data governance.
7. How do you manage and optimize costs in Databricks?
Databricks offers various tools for cost management, including resource optimization and workload scheduling.
8. How do you use Databricks for deep learning?
Databricks supports various deep learning libraries, including TensorFlow and PyTorch.
9. How do you build and deploy pipelines with Databricks?
Databricks offers tools like Jobs and MLflow for building and deploying pipelines.
Expert Level Questions
1. How do you use Databricks with Kubernetes?
Databricks integrates with Kubernetes for containerized deployments and scaling.
2. How do you build and deploy serverless applications with Databricks?
Databricks offers serverless features for running tasks without managing infrastructure.
3. How do you use Databricks for graph processing?
Databricks supports various graph processing libraries, including GraphX and GraphFrames.
4. How do you build and deploy a recommendation system with Databricks?
Databricks provides tools for building and deploying recommendation systems, including Spark MLlib and MLflow.
5. How do you use Databricks for anomaly detection?
Databricks offers various libraries and tools for anomaly detection, including Spark MLlib and MLflow.
6. How do you build and deploy a chatbot with Databricks?
Databricks provides tools for building and deploying chatbots, including Spark NLP and MLflow.
7. How do you use Databricks for natural language processing (NLP)?
Databricks supports various NLP libraries, including Spark NLP and Databricks Text.
8. How do you build and deploy a sentiment analysis system with Databricks?
Databricks provides tools for building and deploying sentiment analysis systems, including Spark MLlib and MLflow.
9. How do you use Databricks for image recognition?
Databricks supports various image recognition libraries, including OpenCV and TensorFlow.
10. How do you build and deploy a computer vision application with Databricks?
Databricks provides tools for building and deploying computer vision applications, including Spark MLlib and MLflow.
Challenging Questions
1. How do you secure Databricks in a multi-tenant environment?
Databricks offers various security features for multi-tenant environments, including isolation, access control, and encryption.
2. How do you manage and monitor the performance of large-scale Databricks clusters?
Databricks offers various tools for managing and monitoring performance, including cluster monitoring tools and performance profiling libraries.
3. How do you troubleshoot performance issues in Databricks applications?
Troubleshooting performance issues requires analyzing logs, profiling applications, and identifying bottlenecks.
4. How do you build and deploy a machine learning model in production with Databricks?
Deploying models in production requires considerations like model serving, monitoring, and autoscaling.
5. How do you build and deploy a large-scale data pipeline with Databricks?
Building and deploying large-scale pipelines requires careful planning, orchestration, and monitoring.
6. How do you build and deploy a real-time streaming application with Databricks?
Building real-time streaming applications requires robust infrastructure, low-latency processing, and fault tolerance.
7. How do you integrate Databricks with other big data technologies?
Databricks integrates with various big data technologies, including Hadoop, Kafka, and AWS services.
8. How do you build and deploy a distributed training system with Databricks?
Building and deploying distributed training systems requires expertise in distributed computing and machine learning frameworks.
9. How do you use Databricks to build and deploy a recommendation system for a large e-commerce platform?
This scenario requires understanding recommender systems, user behavior analysis and real-time recommendations.
10. How do you use Databricks for data lineage and provenance tracking?
Databricks provides features like lineage tracking and audit logs to understand data origin and transformation steps.
11. How do you use Databricks for collaborative data exploration and visualization?
Databricks notebooks and workspace features enable collaborative data exploration and visualization among teams.
12. How do you build and deploy a secure and scalable API gateway with Databricks and Delta Lake?
This requires expertise in API design, security best practices, and integration with Databricks REST APIs.
13. How do you use Databricks for machine learning model Explainability (XAI)?
Databricks offers tools and libraries like SHAP and LIME for understanding model predictions and reasoning.
14. How do you build and deploy a serverless machine learning model with Databricks Serverless Spark?
This requires understanding serverless architecture, model packaging, and integration with Databricks Serverless Spark APIs.
15. How do you use Databricks for data governance in a multi-cloud environment?
This requires implementing consistent data governance policies across different cloud platforms where Databricks is deployed.
16. How do you build and deploy a data lakehouse with Databricks and Delta Lake?
This involves using Delta Lake for data storage and Databricks for data processing and analytics, creating a unified data lakehouse architecture.
17. How do you use Databricks for real-time anomaly detection in sensor data streams?
This requires applying machine learning algorithms and anomaly detection techniques to streaming sensor data in real-time.
18. How do you build and deploy a custom distributed processing engine with Databricks Runtime for ML (DBR)?
This requires expertise in distributed computing frameworks, Spark internals, and custom code development.
19. How do you use Databricks for data privacy and compliance with regulations like GDPR and CCPA?
This involves implementing data privacy controls, access management, and data anonymization techniques within Databricks.
Bonus Questions
1. What are some performance optimizations you can suggest for a Databricks notebook?
Analyze code for unnecessary operations, consider caching intermediate data, and optimize data partitioning.
2. How do you integrate Databricks with external data sources like social media APIs or web scraping?
Utilize libraries like Spark SQL, Spark Streaming, and custom libraries to access and process external data.
3. How do you build and deploy a multi-modal machine learning model with Databricks?
This involves integrating different types of data (text, image, audio) into a single machine learning model for enhanced prediction accuracy.
4. How do you use Databricks for data monetization and building data products?
Utilize Databricks capabilities for data analysis, machine learning, and API development to create valuable data products.
5. How can you contribute to the open-source community around Databricks and Apache Spark?
Participate in forums, contribute to codebases, and share knowledge to improve the open-source ecosystem.
This collection provides a comprehensive set of Databricks interview questions and answers covering various levels of difficulty. It helps you prepare for your interview and demonstrate your knowledge and skills in using Databricks for various data-driven tasks. Remember to adapt your answers to specific job requirements and highlight your relevant experience with Databricks.
