DP-203 Azure Data Engineer Associate DP-203 Azure Data Engineer Associate Data Integration and Pipelines 2 — Questions and Answers
Question 1: What is the key difference between a Tumbling Window Trigger and a Schedule Trigger in Azure Data Factory?
- Tumbling Window Triggers maintain state and support retry; Schedule Triggers do not (Correct answer)
- Schedule Triggers are free; Tumbling Window Triggers cost extra
- Tumbling Window Triggers only work with Blob Storage events
- Schedule Triggers support dependency chains; Tumbling Window Triggers do not
Correct answer: Tumbling Window Triggers maintain state and support retry; Schedule Triggers do not
Tumbling Window Triggers maintain state, support backfill of historical windows, and allow dependency on other tumbling window triggers, unlike simple Schedule Triggers.
Question 2: Which ADF Mapping Data Flow transformation is used to combine rows from two datasets based on a matching condition?
- Union
- Join (Correct answer)
- Aggregate
- Pivot
Correct answer: Join
The Join transformation in ADF Mapping Data Flows combines rows from two input streams based on a matching key condition, similar to a SQL JOIN.
Question 3: In ADF, which parameter scope allows you to pass dynamic values into a pipeline at runtime?
- Dataset parameters
- Pipeline parameters (Correct answer)
- Global parameters
- Activity parameters
Correct answer: Pipeline parameters
Pipeline parameters allow you to define dynamic values that callers must supply when triggering or invoking the pipeline, enabling reusability.
Question 4: What does the 'Self-hosted Integration Runtime' enable in Azure Data Factory?
- Running SSIS packages on Azure-SSIS IR
- Connecting to on-premises or private network data sources (Correct answer)
- Scaling out Spark execution across cloud nodes
- Automating dataset schema detection
Correct answer: Connecting to on-premises or private network data sources
The Self-hosted Integration Runtime is installed on an on-premises machine or VM inside a private network, allowing ADF to securely access private data sources.
Question 5: Which ADF feature allows you to visually debug a Mapping Data Flow without running the full pipeline?
- Data preview in debug mode (Correct answer)
- Activity run history
- Validate pipeline
- Publish all
Correct answer: Data preview in debug mode
Turning on Debug Mode in Mapping Data Flows spins up a small Spark cluster and allows you to preview data at each transformation step interactively.
Question 6: In Azure Data Factory, what is the role of a Dataset?
- Describes a named view of the data that refers to a Linked Service (Correct answer)
- Defines compute resources for activity execution
- Schedules recurring pipeline runs
- Stores pipeline execution logs
Correct answer: Describes a named view of the data that refers to a Linked Service
A Dataset represents a named view or pointer to the data you want to use in your pipeline activities, referencing a Linked Service for connection details.
DP-203 Azure Data Engineer Associate DP-203 Azure Data Engineer Associate Data Integration and Pipelines 2
What is the key difference between a Tumbling Window Trigger and a Schedule Trigger in Azure Data Factory?