MCQs on CI/CD and Automation | Azure Data Factory MCQs Question
In modern data engineering, continuous integration (CI) and continuous deployment (CD) play a crucial role in automating workflows and ensuring streamlined data pipeline management. Azure Data Factory (ADF) enables seamless integration with tools like Azure DevOps and GitHub to set up source control and automate pipeline deployments. This chapter focuses on implementing CI/CD pipelines in ADF, managing templates and artifacts, automating deployments using ARM templates, and testing pipeline deployments. These Azure Data Factory MCQs Questions will help you understand the fundamental concepts of automation and CI/CD in ADF.
Setting Up Source Control with Azure DevOps or GitHub
- Which of the following is required to set up source control in Azure Data Factory?
a) Azure Key Vault
b) Azure DevOps or GitHub repository
c) Azure Blob Storage
d) Azure Virtual Machine - What is the purpose of linking Azure Data Factory to GitHub or Azure DevOps?
a) To track database changes
b) To manage version control for ADF pipelines
c) To store unstructured data
d) To manage data flow configurations - How do you initialize source control in Azure Data Factory?
a) By creating an ARM template
b) By setting up a Git repository in ADF settings
c) By configuring an Integration Runtime
d) By uploading a CSV file - What happens when you commit changes in a GitHub repository linked to ADF?
a) The ADF pipeline gets automatically executed
b) A new version of the pipeline is created in the repository
c) The changes are automatically deployed to production
d) The linked Git repository is overwritten - What feature does ADF provide to allow collaboration in source control?
a) Integration with Power BI
b) User role-based access control
c) Version control and branch management
d) Real-time data monitoring
Implementing CI/CD Pipelines for ADF
- What is the first step in implementing a CI/CD pipeline for ADF?
a) Creating an ARM template
b) Configuring the source control in ADF
c) Setting up an Azure Virtual Network
d) Defining pipeline triggers - Which tool is commonly used for CI/CD pipeline automation in Azure DevOps for ADF?
a) Azure Monitor
b) Azure Pipelines
c) Azure Logic Apps
d) Azure Synapse Analytics - How does Azure DevOps facilitate CI/CD for ADF?
a) By automating the backup process
b) By automating pipeline deployment and testing
c) By creating data lakes
d) By managing data flow scheduling - Which task in Azure DevOps is used to deploy ADF pipelines?
a) Data Flow task
b) ARM template deployment task
c) Azure SQL task
d) Copy Data task - What does the release pipeline in Azure DevOps for ADF primarily handle?
a) Managing source control
b) Automating deployments to different environments
c) Monitoring pipeline executions
d) Creating reports
Managing Templates and Artifacts in ADF
- What is an artifact in Azure Data Factory?
a) A data set
b) A snapshot of a pipeline definition
c) An Integration Runtime
d) A data flow debug session - How are ADF templates used in automation?
a) By allowing pipeline definitions to be exported and reused
b) By managing Azure resources
c) By importing data into ADF
d) By creating real-time dashboards - What can be managed and exported as a template in ADF?
a) Data Lake structures
b) Pipeline configurations
c) Integration Runtime configurations
d) All of the above - How does exporting a template benefit CI/CD automation?
a) It helps with creating data lakes
b) It allows for easy replication of pipeline structures across environments
c) It speeds up data ingestion
d) It enables data visualization - Which service can be used to store ADF templates for reuse?
a) Azure Blob Storage
b) Azure Data Lake
c) Azure DevOps Repository
d) Azure Resource Manager
Automating Deployment Using ARM Templates
- What does an ARM template in Azure Data Factory define?
a) Storage account configuration
b) Network security settings
c) The infrastructure and resources required for ADF deployment
d) Data migration scripts - How do ARM templates help in automating ADF pipeline deployments?
a) By deploying virtual machines for ADF
b) By defining the configuration of resources like pipelines, datasets, and linked services
c) By storing log files
d) By backing up pipeline data - Which of the following can be deployed using ARM templates in ADF?
a) Only datasets
b) Only data flows
c) Only pipeline triggers
d) Pipelines, datasets, linked services, and triggers - How are ARM templates deployed to ADF environments?
a) Using Azure Resource Manager Console
b) By uploading through ADF Studio
c) By running a script in PowerShell
d) Using Azure DevOps pipeline automation - Which of these is a benefit of using ARM templates in ADF?
a) Simplified data flow transformations
b) Faster data ingestion
c) Consistency in deployment across different environments
d) Automatic data monitoring
Testing and Validating Pipeline Deployments
- How can you test an ADF pipeline before deployment?
a) By using the Debug feature in ADF Studio
b) By exporting the pipeline to GitHub
c) By connecting ADF to Power BI
d) By running a full production job - What is the purpose of validating a pipeline deployment in ADF?
a) To check for errors in pipeline configurations
b) To increase the number of datasets
c) To automate data monitoring
d) To deploy new Azure services - What can be used to automate the testing process in Azure DevOps for ADF?
a) Automated PowerShell scripts
b) Azure Pipelines test tasks
c) Manually triggered builds
d) Azure Machine Learning - How does Azure DevOps help in testing ADF pipeline deployments?
a) By automatically running data flows
b) By validating configuration files and templates
c) By storing historical data for testing
d) By importing data for validation - What is a best practice for testing ADF pipelines in production environments?
a) Use mock data for testing
b) Directly deploy to production without testing
c) Avoid debugging features
d) Use only Azure functions for testing
Additional CI/CD Practices for ADF
- What is the benefit of using release pipelines for ADF deployments?
a) They allow for deploying to multiple environments automatically
b) They only support manual deployment
c) They improve data lake performance
d) They provide real-time monitoring - What is a common practice for versioning ADF pipelines in a GitHub repository?
a) Storing data flows as scripts
b) Creating a version branch for each pipeline update
c) Merging all pipelines into one file
d) Ignoring pipeline changes - What is the main advantage of using Git for source control in ADF?
a) It provides versioning and collaboration for pipeline development
b) It allows real-time monitoring of data flows
c) It enhances data ingestion speeds
d) It offers built-in machine learning capabilities - Which of the following is an important aspect of a successful ADF CI/CD strategy?
a) Scheduling pipelines manually
b) Automating deployments across environments
c) Using only one linked service per pipeline
d) Avoiding source control - How do CI/CD pipelines improve ADF deployments?
a) By reducing the number of data flows
b) By ensuring faster manual deployments
c) By automating deployments, reducing errors, and increasing efficiency
d) By requiring fewer pipelines
Answers Table
| Qno | Answer (Option with the text) |
|---|---|
| 1 | b) Azure DevOps or GitHub repository |
| 2 | b) To manage version control for ADF pipelines |
| 3 | b) By setting up a Git repository in ADF settings |
| 4 | b) A new version of the pipeline is created in the repository |
| 5 | c) Version control and branch management |
| 6 | b) Configuring the source control in ADF |
| 7 | b) Azure Pipelines |
| 8 | b) By automating pipeline deployment and testing |
| 9 | b) ARM template deployment task |
| 10 | b) Automating deployments to different environments |
| 11 | b) A snapshot of a pipeline definition |
| 12 | a) By allowing pipeline definitions to be exported and reused |
| 13 | b) Pipeline configurations |
| 14 | b) It allows for easy replication of pipeline structures across environments |
| 15 | c) Azure DevOps Repository |
| 16 | c) The infrastructure and resources required for ADF deployment |
| 17 | b) By defining the configuration of resources like pipelines, datasets, and linked services |
| 18 | d) Pipelines, datasets, linked services, and triggers |
| 19 | d) Using Azure DevOps pipeline automation |
| 20 | c) Consistency in deployment across different environments |
| 21 | a) By using the Debug feature in ADF Studio |
| 22 | a) To check for errors in pipeline configurations |
| 23 | b) Azure Pipelines test tasks |
| 24 | b) By validating configuration files and templates |
| 25 | a) Use mock data for testing |
| 26 | a) They allow for deploying to multiple environments automatically |
| 27 | b) Creating a version branch for each pipeline update |
| 28 | a) It provides versioning and collaboration for pipeline development |
| 29 | b) Automating deployments across environments |
| 30 | c) By automating deployments, reducing errors, and increasing efficiency |