Azure Databricks Interview Questions
Azure Databricks Interview Questions
Azure Databricks Interview Questions
Azure Databricks interview questions typically cover a range of topics such as knowledge of Spark architecture and its components, experience with data processing and analysis using Databricks, familiarity with various data engineering and machine learning techniques, proficiency in programming languages like Python and SQL, understanding of Azure cloud services and resources, and problem-solving skills related to data manipulation and visualization. Additionally, questions may also focus on real-world scenarios, best practices for optimizing performance, managing resources, and implementing security measures within Databricks environments. Being prepared to discuss one's experience, technical skills, and problem-solving abilities in the context of using Azure Databricks is key to succeeding in such interviews.
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1 - What is Azure Databricks?
Azure Databricks is a unified analytics platform provided as a fully managed service on Microsoft Azure. It combines the functionality of Apache Spark with Databricks' collaborative environment, making it easier for data engineers, data scientists, and analysts to collaborate and work on big data and analytics tasks efficiently.
2) What are the key features of Azure Databricks?
Some key features of Azure Databricks include its collaborative workspace, built in notebook support, integration with popular BI tools, automated cluster management, integration with Azure services, scalability for big data processing, and security features like Azure Active Directory integration and role based access control.
3) How does Azure Databricks handle security and compliance?
Azure Databricks provides various security features such as network isolation, encryption at rest and in transit, IAM integration with Azure AD, fine grained access control, audit logging, and compliance certifications like ISO, SOC, and HIPAA to ensure data protection and regulatory compliance for sensitive workloads.
4) What is the difference between Azure Databricks and Apache Spark?
While Apache Spark is an open source cluster computing framework, Azure Databricks is a fully managed analytics platform built on top of Spark. Azure Databricks simplifies Spark cluster management, provides a collaborative workspace, and integrates seamlessly with other Azure services, making it easier to build and deploy big data analytics solutions.
5) How does Azure Databricks optimize performance?
Azure Databricks optimizes performance through features like automatic cluster management, optimized virtual machines for compute instances, caching and optimization of data storage, adaptive query execution, and integration with Azure Synapse Analytics for data integration and processing at scale. These optimizations help improve query performance and reduce processing times for data analytics tasks.
6) How can one integrate Azure Databricks with other Azure services?
Azure Databricks can be seamlessly integrated with other Azure services such as Azure Synapse Analytics, Azure Data Lake Storage, Azure Blob Storage, Azure SQL Database, Azure Event Hubs, and Azure Machine Learning. This integration allows users to leverage the capabilities of these services in conjunction with Azure Databricks for end to end data processing and analytics workflows.
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