MCQs on Orchestration and Workflow Management | Azure Data Factory MCQs Question
Azure Data Factory (ADF) is a powerful cloud-based tool for data integration and orchestration. It provides robust features for building scalable workflows, managing pipelines, and automating data movement across hybrid environments. These Azure Data Factory MCQs questions focus on orchestration, activity chaining, conditional branching, error handling, and triggers, helping you gain expertise for certifications and real-world use cases.
MCQs: Understanding Pipeline Orchestration
- What does pipeline orchestration in Azure Data Factory enable?
a) Hosting web applications
b) Managing complex data workflows
c) Monitoring database activity
d) Analyzing real-time data streams - Which feature in ADF allows for combining multiple activities into a single workflow?
a) Triggers
b) Pipelines
c) Data flows
d) Linked services - A pipeline in ADF can include:
a) Only copy activities
b) Only triggers
c) A mix of multiple activities
d) Only SQL queries - What is the purpose of the integration runtime in ADF?
a) Data visualization
b) Data orchestration across environments
c) Storing data temporarily
d) Managing ADF logs - Which ADF feature supports scaling workflows to handle large datasets?
a) Integration runtime
b) Pipeline parallelism
c) Trigger dependencies
d) Data flows
MCQs: Chaining Activities with Dependencies
- Activity chaining in ADF is achieved using:
a) Event handlers
b) Activity dependencies
c) Trigger conditions
d) Data mapping - Which dependency condition ensures that an activity runs only after a preceding activity succeeds?
a) Failure
b) Completion
c) Success
d) Skipped - How can you handle conditional activity execution in pipelines?
a) By using linked services
b) By defining activity dependencies
c) By configuring pipeline triggers
d) By modifying integration runtimes - What happens if a dependency condition is set to “Failure”?
a) The activity runs if the preceding activity succeeds
b) The activity runs only if the preceding activity fails
c) The activity always runs
d) The activity never runs - What is the best use case for chaining activities in ADF?
a) Debugging code
b) Data visualization
c) Building sequential workflows
d) Creating database schemas
MCQs: Conditional and Parallel Branching
- Conditional branching in ADF allows:
a) Sequential execution of activities
b) Splitting pipelines based on defined conditions
c) Copying data from multiple sources
d) Storing logs in blob storage - Parallel branching is used to:
a) Execute activities in a sequential order
b) Handle errors in workflows
c) Run multiple activities simultaneously
d) Monitor pipeline execution - Which ADF feature supports conditional workflows?
a) Dataset mappings
b) If-condition activity
c) Integration runtimes
d) Data triggers - What is required to configure parallel branching in a pipeline?
a) Multiple linked services
b) Separate integration runtimes
c) Independent activity branches
d) Event-based triggers - Conditional branching in ADF is defined using:
a) JSON expressions
b) SQL queries
c) Python scripts
d) Pre-built templates
MCQs: Error Handling and Retries in Workflows
- Which activity setting in ADF specifies the number of retries upon failure?
a) Retry count
b) Timeout duration
c) Execution frequency
d) Error mapping - How can you log errors in ADF pipelines?
a) By configuring error handling policies
b) By using retry mechanisms
c) By enabling activity monitoring
d) By creating custom logs - What happens when the retry policy is exceeded in ADF?
a) The pipeline execution is skipped
b) The activity fails and triggers an error
c) The pipeline completes successfully
d) The data is reset - What is the purpose of the fault tolerance feature in ADF?
a) Ensuring data integrity in case of activity failure
b) Reducing integration runtime costs
c) Improving data visualization
d) Automating trigger creation - Which setting allows you to continue a pipeline despite activity failure?
a) Activity timeout
b) On-failure condition
c) Continue-on-error flag
d) Success-only condition
MCQs: Using Event-Based Triggers and Tumbling Windows
- Event-based triggers in ADF are primarily used for:
a) Scheduling pipelines on a fixed interval
b) Executing pipelines in response to file creation events
c) Running SQL queries on demand
d) Managing integration runtimes - What is the primary function of tumbling window triggers?
a) Executing pipelines on a recurring schedule
b) Monitoring real-time events
c) Enabling parallel workflows
d) Performing data validation tasks - Which service does ADF use for event-based triggers?
a) Azure Event Grid
b) Azure Monitor
c) Azure DevOps
d) Azure Synapse Analytics - Tumbling window triggers are best suited for:
a) On-demand data transformation
b) Batch data processing at fixed intervals
c) Real-time streaming analytics
d) Monitoring pipeline activity - Event-based triggers can monitor changes in:
a) SQL tables only
b) Blob storage and Azure Data Lake
c) Local files
d) API responses
General Knowledge on Orchestration and Workflow Management
- What is the maximum number of activities allowed in a single pipeline?
a) 10
b) 20
c) 40
d) 50 - Which integration runtime is used for running ADF pipelines in a hybrid environment?
a) Cloud-based runtime
b) Self-hosted runtime
c) Dedicated runtime
d) Kubernetes runtime - How can you monitor ADF pipeline execution?
a) Using Azure Monitor or the ADF portal
b) By enabling advanced logging
c) By creating dashboards in Power BI
d) By running SQL queries - What is the role of ADF’s activity duration setting?
a) Defines the total pipeline execution time
b) Sets the maximum execution time for an activity
c) Configures activity dependency conditions
d) Enables fault tolerance - Which tool helps in debugging workflows in ADF?
a) Data preview
b) Pipeline logs
c) Integration runtime tracker
d) Trigger analyzer
Answers Table
| Qno | Answer (Option with Text) |
|---|---|
| 1 | b) Managing complex data workflows |
| 2 | b) Pipelines |
| 3 | c) A mix of multiple activities |
| 4 | b) Data orchestration across environments |
| 5 | b) Pipeline parallelism |
| 6 | b) Activity dependencies |
| 7 | c) Success |
| 8 | b) By defining activity dependencies |
| 9 | b) The activity runs only if the preceding activity fails |
| 10 | c) Building sequential workflows |
| 11 | b) Splitting pipelines based on defined conditions |
| 12 | c) Run multiple activities simultaneously |
| 13 | b) If-condition activity |
| 14 | c) Independent activity branches |
| 15 | a) JSON expressions |
| 16 | a) Retry count |
| 17 | a) By configuring error handling policies |
| 18 | b) The activity fails and triggers an error |
| 19 | a) Ensuring data integrity in case of activity failure |
| 20 | c) Continue-on-error flag |
| 21 | b) Executing pipelines in response to file creation events |
| 22 | b) Batch data processing at fixed intervals |
| 23 | a) Azure Event Grid |
| 24 | b) Batch data processing at fixed intervals |
| 25 | b) Blob storage and Azure Data Lake |
| 26 | d) 50 |
| 27 | b) Self-hosted runtime |
| 28 | a) Using Azure Monitor or the ADF portal |
| 29 | b) Sets the maximum execution time for an activity |
| 30 | b) Pipeline logs |