The DP-203 : Data Engineering on Microsoft Azure Training course is designed for professionals aiming to build and implement data solutions on Azure. Participants will learn to integrate, transform, and consolidate data from various structured and unstructured data systems into structures suitable for building analytics solutions. Key topics include working with Azure Data Factory, Azure Stream Analytics, Azure SQL Database, and Azure Blob Storage.
DP-203 Data Engineering on Microsoft Azure Intermediate-Level Questions
- What is Azure Data Lake?
- Azure Data Lake is a scalable data storage and analytics service that allows you to analyze big data.
- Explain Azure Data Factory (ADF).
- ADF is a cloud-based data integration service that allows you to create, schedule, and orchestrate data workflows.
- Describe the difference between Data Lake and Data Warehouse.
- Data Lakes support unstructured, semi-structured, and structured data, ideal for big data and real-time analytics. Data Warehouses are optimized for structured data and are used for business intelligence and reporting.
- What is Azure Databricks?
- Azure Databricks is an Apache Spark-based analytics platform optimized for Azure, designed for big data and machine learning.
- How does Azure Stream Analytics work?
- It processes large streams of real-time data from sources like devices, sensors, websites, and social media, and derives insights using query language.
- What are the main components of Azure Synapse Analytics?
- It integrates big data and data warehouse technologies into a single service, featuring on-demand or provisioned resources, integrated security, and analytics capabilities.
- Can you explain data partitioning in Azure Cosmos DB?
- Data partitioning in Cosmos DB involves distributing data across multiple partitions for scalability and performance, based on a partition key.
- What is PolyBase in Azure Synapse Analytics?
- PolyBase allows you to query relational and non-relational databases in your data warehouse using T-SQL, making it easier to integrate data from multiple sources.
- How do you secure data in Azure Data Lake Storage Gen2?
- You secure data using Azure Active Directory, access control lists (ACLs), and encryption at rest and in transit.
- What are the benefits of using Azure Data Lake Storage Gen2?
- It offers large-scale data storage, high-performance analytics, and hierarchical namespace, optimizing big data analytics workloads.
- Describe how Azure Data Factory's Mapping Data Flow works.
- Mapping Data Flow in ADF provides a visual design interface to transform and process data without writing code, using a drag-and-drop experience.
- How can you achieve real-time analytics in Azure?
- By using Azure Stream Analytics, Azure Databricks, and Event Hubs to process and analyze data in real-time.
- What is event sourcing in Azure Event Hubs?
- Event sourcing is a design pattern in which state changes are logged as a sequence of events in an append-only store, enabling event replay.
- Explain the concept of sharding in Azure SQL Database.
- Sharding involves distributing a database across multiple servers to improve performance and scalability.
- What is the role of Azure Blob Storage in data engineering?
- It provides scalable, cost-effective cloud storage for both unstructured data and big data analytics.
- How do you implement disaster recovery in Azure SQL Database?
- By using automated backups, active geo-replication, and Azure SQL Data Sync for geo-redundancy and data recovery.
- What are Azure Data Factory's Integration Runtime (IR) types?
- Azure, Self-hosted, and Azure-SSIS IRs, enabling data movement and activity dispatch in different network environments.
- How does Azure Monitor work with data services?
- Azure Monitor collects, analyzes, and acts on telemetry data from Azure services, providing insights into performance and health.
- What is Time Series Insights in Azure?
- It's a service that stores, visualizes, and queries large amounts of time-series data generated by IoT devices and applications.
- Explain data masking in Azure SQL Database.
Data masking hides sensitive data in the database from non-privileged users, showing masked data instead of actual data.
DP-203 Data Engineering on Microsoft Azure Advance-Level Questions
- What are the core components of Azure Data Factory?
- Answer: Azure Data Factory consists of four key components: Pipeline, Activities, Datasets, and Linked Services. Pipelines are data-driven workflows, while Activities are tasks within the pipelines. Datasets represent data structures, and Linked Services are connections to external resources.
- How does Azure Databricks integrate with Azure Data Lake Storage?
- Answer: Azure Databricks can integrate directly with Azure Data Lake Storage using the DBFS (Databricks File System) mount points. This allows for direct reading and writing to Data Lake Storage, leveraging its big data capabilities and enabling large-scale analytics.
- Explain the role of PolyBase in Azure SQL Data Warehouse.
- Answer: PolyBase allows Azure SQL Data Warehouse to query big data stored in Azure Blob Storage or Azure Data Lake using T-SQL. It enables the integration of SQL queries with external data, which can be used for federated queries across relational and non-relational data.
- What is Time Series Insights, and how is it used in Azure?
- Answer: Azure Time Series Insights is an analytics service used to store, visualize, and query large amounts of time-series data. It's particularly useful for IoT applications, providing real-time analysis and insights on temporal data.
- How do you ensure data security in Azure Data Lake Storage Gen2?
- Answer: Security in Azure Data Lake Storage Gen2 is managed through multiple layers including network security, access control, encryption of data at rest using Azure-managed keys or customer-managed keys in Azure Key Vault, and file and folder level security using POSIX-like ACLs.
- Describe the process of stream analytics in Azure.
- Answer: Stream Analytics in Azure processes large streams of real-time data using simple SQL-like language. It can ingest data from sources like Event Hubs and IoT Hubs, process it in real time, and output data to services like Azure SQL Database, Cosmos DB, or even back to an Event Hub.
- What are the best practices for disaster recovery in Azure Cosmos DB?
- Answer: Best practices for disaster recovery in Azure Cosmos DB include using geo-redundancy with multi-region writes, defining failover priorities, and periodically testing failover mechanisms to ensure data availability and application resilience.
- Explain the significance of partitioning in Azure Synapse Analytics.
- Answer: Partitioning in Azure Synapse Analytics is crucial for performance optimization. It divides large datasets into smaller, manageable parts, enabling faster queries and load operations by distributing data across multiple nodes.
- How does Azure manage data consistency across globally distributed databases?
- Answer: Azure uses multiple well-defined consistency models in Cosmos DB, such as strong, bounded staleness, session, consistent prefix, and eventual consistency, allowing developers to choose the right balance between consistency and performance based on their application requirements.
- What is Azure Event Grid and how is it different from Azure Event Hubs?
Answer:
Azure Event Grid is an event routing service that enables scalable event handling based on publisher-subscriber model. It's ideal for automating reactions to status changes or user actions. Azure Event Hubs is a big data streaming platform and event ingestion service, designed for capturing large volumes of event data to be processed or stored by downstream services.