Sample Questions from Microsoft Fabric Analytics Engineer (DP-600)

Preview a few questions below — answers are revealed when you take the exam.

1. A team needs to integrate real-time data processing with their existing data warehouse using Microsoft Fabric. What approach should they take to ensure seamless integration and minimal disruption to ongoing operations?

   • Implement a new data pipeline using Azure Data Factory to ingest real-time data and store it in a separate data lake, then use Azure Synapse Analytics to process and integrate the data with the existing data warehouse.
   • Utilize Azure Stream Analytics to process real-time data and directly feed the results into the existing data warehouse using PolyBase for data integration, ensuring minimal disruption.
   • Migrate the entire data warehouse to Azure Synapse Analytics and use its built-in real-time data processing capabilities to handle both historical and real-time data.
   • Develop a custom ETL process using Azure Functions to handle real-time data and integrate it with the existing data warehouse, ensuring all data is processed and stored in a consistent manner.

2. Consider the situation where a company wants to implement a robust data governance framework within Microsoft Fabric. Which method solves it best for ensuring data quality, security, and compliance across all data assets?

   • Deploy Azure Purview to catalog all data assets, implement data classification and sensitivity labels, and use Azure Policy to enforce compliance rules across the organization.
   • Use Azure Data Share to share data securely with external partners, implement row-level security in Azure Synapse Analytics, and rely on manual data quality checks.
   • Create a custom data governance portal using Power Apps to manage data policies and use Power BI to monitor data quality metrics, without integrating with existing Azure services.
   • Rely solely on Azure Active Directory for access control and use Excel for data quality management, without implementing a comprehensive data governance solution.

3. What strategy should be applied when designing a scalable data architecture in Microsoft Fabric to support both batch and real-time data processing?

   • Use Azure Data Factory for batch processing and Azure Synapse Analytics for real-time processing, ensuring both systems are integrated using Azure Data Lake Storage.
   • Implement a monolithic architecture where all data processing is handled by a single Azure service, avoiding the complexity of integrating multiple services.
   • Rely exclusively on Azure SQL Database for both batch and real-time data processing, using stored procedures for complex data transformations.
   • Deploy separate Azure services for batch and real-time processing without any integration, allowing each system to operate independently.

4. How would you decide the best method for automating data ingestion from multiple sources into Microsoft Fabric while ensuring data consistency and minimal latency?

   • Implement Azure Data Factory pipelines to automate data ingestion from various sources, use Azure Data Lake Storage for staging, and apply data quality rules before loading into Azure Synapse Analytics.
   • Manually upload data from each source into Azure Blob Storage and use Azure Databricks for data processing, relying on manual checks for data consistency.
   • Use Azure Logic Apps to automate data ingestion, store data in Azure SQL Database, and perform data transformations using T-SQL queries.
   • Deploy a custom ETL solution using Azure Functions to ingest data from sources, store it in Azure Cosmos DB, and use Azure Stream Analytics for real-time processing.

5. What configuration is most appropriate for implementing a data warehousing solution in Microsoft Fabric that supports both historical data analysis and real-time insights?

   • Configure Azure Synapse Analytics as the primary data warehouse, use Azure Data Factory for data ingestion, and integrate Azure Stream Analytics for real-time data processing.
   • Use Azure SQL Database as the data warehouse, implement Azure Data Share for data ingestion, and rely on Azure Monitor for real-time insights.
   • Deploy Azure Data Lake Storage as the primary data store, use Azure Databricks for data processing, and implement custom dashboards in Power BI for real-time insights.
   • Rely on Azure Blob Storage for data storage, use Azure Functions for data processing, and implement a custom reporting solution using Excel for insights.