Getting Started With ADF

Question 1

What is Azure Data Factory?

Answer 1

A cloud-based ETL/ELT and data orchestration service.

Question 2

What is the main purpose of ADF?

Answer 2

To move, transform, and orchestrate data across systems

Question 3

Does ADF store data?

Answer 3

No - it only orchestrates and moves data

Question 4

What is a pipeline?

Answer 4

A logical container that groups activities into a workflow.

Question 5

What is an activity?

Answer 5

A single step inside a pipeline.

Question 6

What is a dataset?

Answer 6

A metadata reference pointing to actual data.

Question 7

What is a linked service?

Answer 7

The connection information to a data source or compute service.

Question 8

What is an Integration Runtime (IR)?

Answer 8

The compute engine ADF uses to run activities.

Question 9

What are the 3 types of IR?

Answer 9

Azure IR, Self-hosted IR, Azure SSIS IR.

Question 10

What is the ADF Studio?

Answer 10

The UI used to build, monitor, and manage ADF

Question 11

What is Author mode?

Answer 11

The workspace where you design pipelines.

Question 12

What is Monitor mode?

Answer 12

The area where you view pipeline runs, triggers, and logs.

Question 13

What is Git integration for ADF?

Answer 13

Allows version control and collaborative development

Question 14

What is Live mode?

Answer 14

The published version of your data factory.

Question 15

Do you edit directly in Live mode?

Answer 15

The published version of your data factory.

Question 16

Do you edit directly in Live mode?

Answer 16

No - you edit in Git mode and publish

Question 17

What is publishing in ADF?

Answer 17

Moving code from Git workspace > Live data factory

Question 18

What is an ARM template?

Answer 18

An infrastructure-as-code definition for ADF components.

Question 19

Can ADF work with on-prem data?

Answer 19

Yes - via Self-hosted IR.

Question 20

What is a trigger?

Answer 20

A scheduler that runs pipelines automatically.

Question 21

Does ADF support manual pipeline execution?

Answer 21

Yes - “Debug” and “Add Trigger > Now”

Question 22

Can ADF call APIs?

Answer 22

Yes - via Web Activity.

Question 23

Can ADF run SQL stored procedures?

Answer 23

Yes - via Stored Procedure Activity.

Question 24

Can ADF process files?

Answer 24

Yes - copy, delete, validate, metadata.

Question 25

What is Copy Activity used for?

Answer 25

Moving data from source to destination.

Question 26

Can ADF perform transformation?

Answer 26

Yes - using Mapping Data Flows or external compute.

Question 27

What is a Data Flow?

Answer 27

A Spark-based visual transformation engine.

Question 28

What is Debug mode in Data Flow?

Answer 28

Turns on a temporary Spark cluster for interactive testing.

Question 29

What is a parameter?

Answer 29

A value passed at runtime for dynamic pipelines.

Question 30

What is a variable?

Answer 30

A value kept in memory during pipeline execution.

Question 31

What is dynamic content?

Answer 31

Expressions used to build flexible pipeline logic.

Question 32

What is a pipeline run ID?

Answer 32

A unique ID for each pipeline execution.

Question 33

What is pipeline concurrency?

Answer 33

Max number of simultaneous pipeline runs.

Question 34

What is pipeline timeout?

Answer 34

Max runtime before forced stop (default 7 days).

Question 35

Can ADF integrate with Synapse or Databricks?

Answer 35

Yes - via dedicated activities.

Question 36

What languages does ADF use for logic?

Answer 36

ADF Expression Language (similar to JSON)