MCQs on Real-Time and Incremental Data Processing | Azure Data Factory MCQs Question
Azure Data Factory (ADF) is a comprehensive data integration service that enables efficient data movement and transformation across diverse sources. Chapter 8 delves into advanced topics like event-based architectures, incremental data load patterns, and Change Data Capture (CDC) with ADF. Additionally, it explores integration with Event Hubs, IoT data, and the management of real-time data pipelines. These Azure Data Factory MCQs questions help you test your expertise and prepare for practical implementations and certifications.
Multiple-Choice Questions (MCQs)
Working with Event-Based Architectures
- What triggers an event-based pipeline in Azure Data Factory?
a) User request
b) External system events
c) Scheduled time intervals
d) Database query - Which Azure service is commonly used with ADF for event-based architectures?
a) Azure Event Grid
b) Azure Monitor
c) Azure Active Directory
d) Azure Kubernetes Service - What type of trigger is used for event-based data processing in ADF?
a) Schedule trigger
b) Event trigger
c) Manual trigger
d) Tumbling window trigger - Event triggers in ADF are designed to respond to changes in:
a) File systems
b) Blob storage
c) Database entries
d) All of the above - How do event-based triggers improve efficiency?
a) By automating repetitive tasks
b) By reducing idle pipeline execution
c) By enhancing data security
d) By increasing compute power
Incremental Data Load Patterns
- Incremental data load in ADF is used for:
a) Processing all historical data
b) Processing only new or changed data
c) Deleting old records
d) Generating real-time insights - Which property is commonly used for tracking incremental data loads?
a) Primary key
b) Timestamp column
c) Partition key
d) Data type - What type of activity in ADF is often used to implement incremental data loads?
a) Lookup activity
b) Copy activity
c) Delete activity
d) Filter activity - What is the main advantage of incremental data loading?
a) Simplifies schema design
b) Reduces storage costs
c) Speeds up data processing
d) Improves data governance - How is incremental load typically implemented for relational databases?
a) Using a watermark table
b) Using data compression techniques
c) Running batch jobs
d) Storing data in JSON format
CDC (Change Data Capture) with ADF
- What does CDC stand for in data processing?
a) Centralized Data Control
b) Change Data Capture
c) Comprehensive Data Collection
d) Continuous Data Configuration - Which ADF activity is suitable for CDC pipelines?
a) Data Flow activity
b) Mapping activity
c) Lookup activity
d) Copy activity - How does Change Data Capture work in ADF?
a) By replacing entire datasets
b) By identifying and processing only updated or new data
c) By duplicating records
d) By combining multiple tables - What is required to configure CDC for a SQL database in ADF?
a) An active network endpoint
b) Enabled CDC features in the source database
c) High-performance computing resources
d) Blob storage integration - What is the main benefit of CDC pipelines in Azure Data Factory?
a) Faster real-time data processing
b) Enhanced logging capabilities
c) Simplified pipeline debugging
d) Easier key management
Integration with Event Hubs and IoT Data
- Which Azure service is ideal for handling IoT data in ADF?
a) Azure Event Hubs
b) Azure Logic Apps
c) Azure Storage Accounts
d) Azure Kubernetes Service - How does Azure Event Hubs help with real-time data integration in ADF?
a) By storing raw files
b) By ingesting and streaming large volumes of data
c) By monitoring pipeline performance
d) By managing access control - What type of binding is required for Event Hubs in ADF?
a) Data source binding
b) Dataset configuration
c) Linked service
d) Direct query binding - What is the advantage of integrating IoT data with ADF pipelines?
a) Real-time insights from sensor data
b) Secure file storage
c) Enhanced query performance
d) Lower operational costs - How is event-driven IoT data processed in ADF?
a) Using batch pipelines
b) Through real-time triggers and integration runtime
c) Using static schemas
d) By transforming data into SQL tables
Managing Real-Time Data Pipelines
- What is required for managing real-time pipelines in Azure Data Factory?
a) Event-based triggers
b) Manual interventions
c) Debugging tools
d) Static linked services - Which activity is typically used for real-time processing in ADF?
a) Copy activity
b) Wait activity
c) Web activity
d) Trigger activity - What does a pipeline run in real-time data processing indicate?
a) Execution status of all pipeline activities
b) Configuration settings for triggers
c) Storage location of processed data
d) Security permissions - How can you monitor real-time pipelines effectively in ADF?
a) Using the Monitor tab
b) Exporting logs to Azure Log Analytics
c) Setting up alerts
d) All of the above - What happens when a real-time pipeline fails during execution?
a) It retries automatically if configured
b) It stops the data flow permanently
c) It deletes all related resources
d) It continues without logging errors
Additional Questions
- What is the primary output format of ADF IoT data pipelines?
a) JSON
b) CSV
c) Parquet
d) All of the above - Which runtime is most suited for real-time data pipelines in ADF?
a) Azure IR
b) Self-hosted IR
c) Managed IR
d) Cloud-native IR - How can you improve the efficiency of real-time data pipelines in ADF?
a) By using partitioned data
b) By minimizing trigger frequency
c) By increasing CPU cores
d) By reducing linked services - What is the default retry policy for real-time pipelines in ADF?
a) 1 attempt
b) 3 attempts
c) 5 attempts
d) No retries - How can you optimize CDC pipelines in ADF?
a) By using incremental load
b) By indexing frequently queried columns
c) By partitioning source data
d) All of the above
Answers
| QNo | Answer (Option with text) |
|---|---|
| 1 | b) External system events |
| 2 | a) Azure Event Grid |
| 3 | b) Event trigger |
| 4 | d) All of the above |
| 5 | b) By reducing idle pipeline execution |
| 6 | b) Processing only new or changed data |
| 7 | b) Timestamp column |
| 8 | b) Copy activity |
| 9 | c) Speeds up data processing |
| 10 | a) Using a watermark table |
| 11 | b) Change Data Capture |
| 12 | d) Copy activity |
| 13 | b) By identifying and processing only updated or new data |
| 14 | b) Enabled CDC features in the source database |
| 15 | a) Faster real-time data processing |
| 16 | a) Azure Event Hubs |
| 17 | b) By ingesting and streaming large volumes of data |
| 18 | c) Linked service |
| 19 | a) Real-time insights from sensor data |
| 20 | b) Through real-time triggers and integration runtime |
| 21 | a) Event-based triggers |
| 22 | a) Copy activity |
| 23 | a) Execution status of all pipeline activities |
| 24 | d) All of the above |
| 25 | a) It retries automatically if configured |
| 26 | d) All of the above |
| 27 | a) Azure IR |
| 28 | a) By using partitioned data |
| 29 | b) 3 attempts |
| 30 | d) All of the above |