MCQs on Introduction to Azure Data Factory | Azure Data Factory MCQs Question
Azure Data Factory (ADF) is a cloud-based data integration service that enables seamless movement and transformation of data across various sources. With its powerful features, ADF supports complex workflows, data pipelines, and integrations for modern data warehousing and analytics. These Azure Data Factory MCQs questions explore ADF’s architecture, core concepts, features, pricing, and comparisons with other tools, providing foundational knowledge for certifications and practical use.
MCQs: What is Azure Data Factory (ADF)?
- What is the primary purpose of Azure Data Factory?
a) To store data in SQL databases
b) To enable data integration and orchestration workflows
c) To analyze large datasets in real-time
d) To provide data visualization services - Azure Data Factory is best suited for:
a) Database management only
b) Real-time analytics
c) Data integration and transformation tasks
d) Building virtual machines - What type of service is Azure Data Factory classified as?
a) Infrastructure as a Service (IaaS)
b) Platform as a Service (PaaS)
c) Software as a Service (SaaS)
d) Database as a Service (DBaaS) - Which of the following is NOT a feature of Azure Data Factory?
a) Data pipeline orchestration
b) Real-time data visualization
c) Data movement between on-premises and cloud
d) Integration with third-party services - Azure Data Factory supports which types of data sources?
a) Only on-premises databases
b) Only Azure cloud services
c) Both on-premises and cloud data sources
d) Only streaming data
MCQs: Core Concepts and Architecture
- What are the key components of an Azure Data Factory pipeline?
a) Activities, datasets, linked services, triggers
b) Dashboards, queries, data flows, alerts
c) Functions, triggers, apps, flows
d) Connections, nodes, scripts, stages - What is a linked service in ADF?
a) A secure connection to a data source or destination
b) A visualization tool for data pipelines
c) An authentication mechanism
d) A method for storing logs - Which concept in ADF is used to define the structure of data?
a) Activities
b) Datasets
c) Triggers
d) Linked services - What is the purpose of triggers in ADF?
a) To define runtime schedules or events for pipeline execution
b) To store metadata about data sources
c) To automate data flow generation
d) To configure security policies - Which integration runtime is required for on-premises data movement?
a) Self-hosted integration runtime
b) Azure-hosted integration runtime
c) Cloud-native runtime
d) Kubernetes runtime
MCQs: Key Features and Use Cases
- Which feature of ADF allows for building scalable ETL pipelines?
a) Linked services
b) Data flows
c) Blob storage
d) Event hubs - ADF is commonly used for:
a) Creating mobile applications
b) Managing infrastructure resources
c) Building and orchestrating data pipelines
d) Designing machine learning models - What is the role of mapping data flows in ADF?
a) Visual data transformation
b) Real-time monitoring
c) Network configuration
d) Application hosting - Which ADF feature supports data transformation without coding?
a) Copy activity
b) Mapping data flows
c) Integration runtime
d) Data triggers - What is the maximum size of a file that can be copied in ADF pipelines?
a) 10 GB
b) 50 GB
c) 100 GB
d) Unlimited
MCQs: Comparison with Other Data Integration Tools
- Which tool is ADF most closely compared to for ETL workflows?
a) Power BI
b) SQL Server Integration Services (SSIS)
c) Azure Monitor
d) Microsoft Excel - What makes ADF unique compared to other integration tools?
a) It supports both cloud and on-premises data integration
b) It only supports structured data
c) It has limited connectivity options
d) It requires high computational resources - How does ADF differ from Azure Logic Apps?
a) ADF focuses on data pipelines; Logic Apps focus on workflow automation
b) ADF supports real-time event processing
c) Logic Apps are designed for data transformation
d) ADF has fewer integration features - Compared to SSIS, ADF offers:
a) Less flexibility for cloud data integration
b) Serverless architecture with global scale
c) Limited monitoring capabilities
d) Dedicated hardware resources - Which of the following is NOT a use case for ADF?
a) Data warehousing
b) Data pipeline orchestration
c) Real-time chat application development
d) Cloud-based ETL workflows
MCQs: ADF Pricing and Cost Optimization
- How is Azure Data Factory pricing calculated?
a) Based on the number of data records processed
b) Based on pipeline activity runs and data movement
c) Fixed monthly subscription
d) By the amount of stored data - What is the best way to optimize costs in ADF?
a) Use redundant pipelines
b) Implement autoscaling and monitor pipeline activity
c) Increase the frequency of data processing
d) Store data in multiple regions - Which integration runtime option is cost-effective for intermittent workloads?
a) Self-hosted integration runtime
b) Azure-hosted integration runtime
c) Autoscaling runtime
d) SQL-based runtime - What feature in ADF helps reduce unnecessary resource utilization?
a) Activity-level error handling
b) Pipeline monitoring
c) Trigger-based execution
d) High availability zones - Which cost optimization strategy is unique to ADF?
a) Dynamic scaling of compute resources
b) Prepaying for pipeline execution
c) Purchasing dedicated hardware
d) Disabling monitoring features
General Knowledge on ADF
- What is the default retention period for ADF activity logs?
a) 7 days
b) 30 days
c) 90 days
d) 365 days - Which Azure service integrates seamlessly with ADF for big data processing?
a) Azure Synapse Analytics
b) Azure Active Directory
c) Azure DevOps
d) Azure Virtual Machines - What is the first step in creating an ADF pipeline?
a) Configure a dataset
b) Create a linked service
c) Define pipeline triggers
d) Configure monitoring settings - Which ADF component manages pipeline activities across different regions?
a) Integration runtime
b) Activity handler
c) Dataset manager
d) Storage explorer - What is the primary goal of ADF monitoring tools?
a) Improve data visualization
b) Detect and resolve pipeline errors
c) Host machine learning models
d) Build advanced data dashboards
Answers Table
| Qno | Answer (Option with Text) |
|---|---|
| 1 | b) To enable data integration and orchestration workflows |
| 2 | c) Data integration and transformation tasks |
| 3 | b) Platform as a Service (PaaS) |
| 4 | b) Real-time data visualization |
| 5 | c) Both on-premises and cloud data sources |
| 6 | a) Activities, datasets, linked services, triggers |
| 7 | a) A secure connection to a data source or destination |
| 8 | b) Datasets |
| 9 | a) To define runtime schedules or events for pipeline execution |
| 10 | a) Self-hosted integration runtime |
| 11 | b) Data flows |
| 12 | c) Building and orchestrating data pipelines |
| 13 | a) Visual data transformation |
| 14 | b) Mapping data flows |
| 15 | c) 100 GB |
| 16 | b) SQL Server Integration Services (SSIS) |
| 17 | a) It supports both cloud and on-premises data integration |
| 18 | a) ADF focuses on data pipelines; Logic Apps focus on workflow automation |
| 19 | b) Serverless architecture with global scale |
| 20 | c) Real-time chat application development |
| 21 | b) Based on pipeline activity runs and data movement |
| 22 | b) Implement autoscaling and monitor pipeline activity |
| 23 | b) Azure-hosted integration runtime |
| 24 | c) Trigger-based execution |
| 25 | a) Dynamic scaling of compute resources |
| 26 | c) 90 days |
| 27 | a) Azure Synapse Analytics |
| 28 | b) Create a linked service |
| 29 | a) Integration runtime |
| 30 | b) Detect and resolve pipeline errors |