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Answer: Use Azure Data Factory to create a secure and scalable data integration pipeline, leveraging Azure Data Lake Storage and Azure SQL Database for data storage and management.
Option B is the correct answer. Using Azure Data Factory to create a secure and scalable data integration pipeline allows you to efficiently manage data access and integration across various data sources. Leveraging Azure Data Lake Storage and Azure SQL Database for data storage and management ensures secure and efficient data handling, while maintaining access controls and security measures.
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Your company is implementing an Azure AI service that requires integration with various data sources, including relational databases, NoSQL databases, and file storage. What steps should you take to ensure secure and efficient data access and integration?
A
Directly connect the AI service to all data sources without implementing any security measures or access controls.
B
Use Azure Data Factory to create a secure and scalable data integration pipeline, leveraging Azure Data Lake Storage and Azure SQL Database for data storage and management.
C
Manually copy data from each data source to the AI service without using any automation or data integration tools.
D
Disable data access controls and rely on the AI service's built-in security features to protect the data during integration.
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