Question 1

What is the PRIMARY benefit of using Dynamic Column Mapping in Azure Data Factory Copy Activity?

Accepted Answer

A. Improves data transformation efficiency by automatically mapping source and sink columns.

Answer

C. Requires pre-defined mappings but simplifies complex transformations.

Answer

D. Enables real-time data transfer between on-premise and cloud data sources.

Answer

B. Enforces strict schema matching between source and destination datasets.

Question 2

When would Dynamic Column Mapping be the LEAST suitable option for a Copy Activity?

Accepted Answer

A. When the source and destination datasets have identical schemas.

Answer

C. When source data contains columns with unpredictable or dynamic names.

Answer

D. When data quality checks require manual inspection of column mappings.

Answer

B. When complex data manipulation like aggregations are needed.

Question 3

How does Dynamic Column Mapping handle situations where the source data has more columns than the destination?

Accepted Answer

A. It throws an error and the copy activity fails.

Answer

C. It dynamically creates additional columns in the destination dataset.

Answer

D. It allows user-defined logic to determine how extra columns are handled.

Answer

B. It maps only the columns present in the destination schema.

Question 4

What Azure Data Factory service is primarily used for configuring Dynamic Column Mapping within a Copy Activity?

Accepted Answer

A. Data Lake Analytics

Answer

D. Mapping Data Flows

Answer

C. Data Flow

Answer

B. Azure Functions

Question 5

Syntax for referencing dynamic column names in the destination schema within a Copy Activity might involve:

Accepted Answer

A. Pre-defined macros like @source.<column_name>.

Answer

C. User-defined functions for dynamic column selection.

Answer

B. Regular expressions to match source column patterns.

Answer

D. All of the above.

Question 6

Which of the following statements is INCORRECT regarding performance considerations with Dynamic Column Mapping?

Accepted Answer

A. It can be less performant compared to static mappings for simple data transfers.

Answer

D. Dynamic Column Mapping is always the most efficient approach for data copying.

Answer

B. Caching mechanisms can be used to optimize performance for frequently used dynamic mappings.

Answer

C. Increased complexity of data transformations can negate performance benefits.

Question 7

Security best practices for using Dynamic Column Mapping might involve:

Accepted Answer

A. Granting unrestricted access to all source data columns.

Answer

C. Disabling encryption for improved data processing speed.

Answer

B. Implementing data filtering to limit exposure to sensitive data.

Answer

D. Bypassing data validation checks for faster copy operations.

Question 8

Testing the functionality of Dynamic Column Mappings within a Copy Activity can be achieved through:

Accepted Answer

A. Manually inspecting the copied data after each run.

Answer

C. Writing custom scripts to compare source and destination data.

Answer

D. A combination of B and C.

Answer

B. Leveraging built-in data preview and validation tools.

Question 9

When troubleshooting errors related to Dynamic Column Mapping, a good starting point would be to:

Accepted Answer

A. Review the activity logs for detailed error messages.

Answer

D. All of the above.

Answer

B. Increase the logging verbosity level for the Copy Activity.

Answer

C. Manually inspect the source data schema for inconsistencies.

Question 10

Dynamic Column Mapping offers a powerful approach for handling data with:

Accepted Answer

A. Highly structured and predictable schemas.

Answer

B. Complex transformations requiring scripting languages.

Answer

D. Limited data volume and simple copy operations.

Answer

C. Unforeseen or dynamically generated column names.

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