Azure Data Integration and Migration Flashcards

Which Azure service is designed specifically to migrate on-premises databases to Azure with minimal downtime?

Answer: Azure Database Migration Service

Azure Database Migration Service (DMS) is purpose-built for migrating on-premises databases to Azure SQL Database, Azure SQL Managed Instance, or SQL Server on Azure VMs.

Which tool should you run before migrating an on-premises SQL Server database to Azure SQL Database to identify compatibility issues?

Answer: Data Migration Assistant (DMA)

Data Migration Assistant (DMA) assesses SQL Server databases for compatibility issues and provides actionable recommendations before migration to Azure SQL Database.

Which migration mode in Azure Database Migration Service keeps the source database operational throughout the migration process?

Answer: Online migration

Online migration uses continuous data synchronization so the source database remains available, minimizing downtime to a brief cutover window at the end.

When migrating from MongoDB to Azure Cosmos DB for MongoDB API, which service provides managed migration with minimal downtime?

Answer: Azure Database Migration Service

Azure Database Migration Service supports online migration from MongoDB to Azure Cosmos DB for MongoDB API, handling continuous data sync until cutover.

What is the primary function of the Copy Activity in Azure Data Factory?

Answer: To move data from a source data store to a destination data store

Copy Activity in Azure Data Factory is optimized for data movement, copying data from a supported source data store to a sink (destination) data store.

Which network connectivity option is required by Azure Database Migration Service when migrating to Azure SQL Managed Instance?

Answer: Azure ExpressRoute or VPN Gateway

Azure Database Migration Service requires ExpressRoute or VPN Gateway to establish line-of-sight network connectivity between DMS and the on-premises source database when targeting SQL Managed Instance.

What is the primary factor that determines whether to use online versus offline migration in Azure Database Migration Service?

Answer: The acceptable amount of downtime during migration

Acceptable downtime is the key decision factor: offline migration requires the source to go offline, while online migration keeps it running with only a short cutover window.

What is the role of Integration Runtime (IR) in Azure Data Factory?

Answer: To provide the compute infrastructure that executes data integration activities

Integration Runtime is the compute infrastructure Azure Data Factory uses to run activities, providing data movement, data transformation, and SSIS package execution capabilities.

Which type of Azure Data Factory Integration Runtime must be installed on an on-premises machine to access private network data sources?

Answer: Self-hosted Integration Runtime

Self-hosted Integration Runtime is installed on on-premises or cloud VMs in a private network, allowing Azure Data Factory to reach data sources not exposed to the public internet.

What do Linked Services represent in Azure Data Factory?

Answer: Connection strings and authentication details for external data stores

Linked Services in Azure Data Factory function like connection strings, storing the endpoint and credentials required to connect ADF to external data sources and compute resources.

Which Azure Data Factory trigger type fires pipelines on a recurring wall-clock schedule, such as every day at 8:00 AM UTC?

Answer: Schedule trigger

Schedule triggers fire pipelines based on wall-clock time on a recurring basis (e.g., daily, weekly), making them suitable for time-based batch workloads.

Which Azure Data Factory feature provides a visually designed, code-free interface for building complex data transformation logic that runs on Spark?

Answer: Mapping Data Flow

Mapping Data Flow lets you visually design data transformations using a graph-based UI; ADF compiles these flows into Spark jobs that run on an auto-managed Spark cluster.

Which Azure Data Factory capability allows a single dataset definition to serve multiple data sources by accepting runtime-provided values?

Answer: Parameterization

Parameterization lets you define datasets and linked services with parameters so that a single definition can dynamically point to different tables, files, or databases at runtime.

In Azure Data Factory, which construct is a logical grouping of activities that together perform a unit of work?

Answer: Pipeline

A Pipeline in Azure Data Factory is a logical grouping of activities—such as Copy, Data Flow, and stored procedure activities—that together accomplish a data integration task.

Which columnar file format is natively optimized for Azure Data Lake Storage Gen2 and Azure Synapse Analytics analytics workloads?

Answer: Parquet

Parquet is a columnar storage format that provides efficient compression and fast analytical query performance, and is natively supported across Azure analytics services including Synapse and Databricks.

What is the key difference between Azure Data Factory's Copy Activity and Mapping Data Flow?

Answer: Copy Activity moves data without transformation; Data Flow performs complex transformations on Spark

Copy Activity is optimized for high-throughput data movement without transformation logic, while Mapping Data Flow provides a visual, code-free transformation environment backed by Spark.

What must be enabled on the source SQL Server to allow Azure Database Migration Service to perform an online (minimal-downtime) migration?

Answer: Change Data Capture (CDC)

Change Data Capture (CDC) must be enabled on the source SQL Server so that Azure Database Migration Service can continuously track and apply data changes during the online migration.

Which Azure Data Factory Integration Runtime type enables lift-and-shift of on-premises SSIS packages to run in the cloud?

Answer: Azure-SSIS Integration Runtime

Azure-SSIS Integration Runtime is a fully managed cluster that allows existing SSIS packages to be executed natively in Azure without modification.

Which approach should you use in Azure Data Factory to implement incremental data loading from a relational source database?

Answer: Watermark column tracking or Change Data Capture

Incremental loading requires a way to identify new or changed rows; ADF supports watermark columns (last-modified timestamps) and Change Data Capture for this purpose.

Which Azure Data Factory Mapping Data Flow feature automatically handles new or changed columns in the source without requiring pipeline changes?

Answer: Schema drift

Schema drift in Mapping Data Flow allows the data flow to dynamically adapt to changes in the source schema—such as added or renamed columns—without failing or requiring manual updates.