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Section 1
Create Dataverse plug-ins, custom APIs, and business events
Preview
Preview: Extend the platform
Preview includes- 4 of 4 lesson topics
- 1 overview segment
- 3 core concepts
- 2 exam tips
Lesson Topics
- Plug-in Stages: PreValidation, PreOperation, PostOperation
- Execution Context: SharedVariables, User Information, Trace Diagnostics, Depth, Correlation, Initiating User, Executing User
- Dataverse APIs: Custom Operations, Record-Scoped Invocation
- Asynchronous Operations: Transaction Isolation
Overview
This objective covers designing Dataverse plug-ins that leverage different execution stages and context information to manage data modifications efficiently and reliably. Understanding the distinctions between PreValidation, PreOperation, and PostOperation, along with the execution context's capabilities, is crucial for building robust and performant plug-ins. Dataverse custom APIs define reusable operations with request parameters and can be bound to a table for record-scoped invocation.
Core Concepts
- Transaction Management: PreValidation normally occurs before the initial database transaction and is appropriate for early rejection; PreOperation executes inside the transaction before the main operation; synchronous PostOperation runs after the main operation but remains within the transaction; asynchronous PostOperation executes later outside the original transaction.
- Execution Context: Provides shared variables, user information, depth, correlation, initiating user, executing user, and tracing data for plug-in logic.
- Dataverse APIs: Custom APIs enable reusable operations with request parameters and record-scoped invocation.
Exam Tips
- Understand the implications of each plug-in stage on transaction isolation. Asynchronous operations are critical for non-critical tasks.
- Check the execution context – it's the primary source of information for plug-in logic.
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Section 2
Design technical architecture, security, and business logic placement
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Section 3
Implement solutions, dependencies, environment variables, layers, pipelines, and CI/CD
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Section 4
Publish and consume Dataverse events through webhooks and Azure messaging
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Section 5
Synchronize Dataverse data with change tracking, alternate keys, and upsert
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Section 6
Implement model-driven client scripting, commands, Web API calls, and navigation
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Section 7
Optimize and troubleshoot canvas and model-driven apps
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Section 8
Build, package, deploy, and consume PCF code components
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Section 9
Implement advanced canvas logic and reusable components
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Section 10
Configure robust Power Automate cloud flows
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Section 11
Create custom connectors and transform connector traffic
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Section 12
Use Dataverse Web API and Organization service efficiently
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Section 13
Configure environments, troubleshoot security, and apply least privilege
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Section 14
Design reusable components, connectors, code components, and integrations
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Section 15
Process long-running, scheduled, and event-driven work with Azure Functions
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