PL-7002: Create and Manage Automated Processes by using Power Automate
Agent-led course that prepares learners for the Microsoft PL-7002 Applied Skills credential. Build automated, instant, and scheduled Power Automate cloud flows against Dataverse and SharePoint; design approvals with parallel branches; and master trigger filters for efficient, production-grade automation.
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About This Course
Build production-ready cloud flows in Microsoft Power Automate from automated triggers that react to Dataverse and SharePoint events, to approval workflows with parallel branches, instant on-demand flows, scheduled recurrences, and precise trigger filters that keep flows efficient. By the end of the course you will be able to design, test, and share the full set of cloud-flow patterns assessed by the Microsoft PL-7002 Applied Skills credential, grounded in a single continuous Ridgefield Properties work-orders scenario.
Course Curriculum
14 Lessons
Power Automate fundamentals — cloud flows, triggers, templates, Copilot
Open the course with a focused teaching lesson on Power Automate cloud flow fundamentals. Learn what a cloud flow is, when to use each of the three trigger types (automated, instant, scheduled), the three paths for creating one (template, Copilot, blank), what connectors do, and how to monitor flow runs — all grounded in Ridgefield Properties scenarios so the vocabulary carries forward into every hands-on lab in this course.
Create cloud flows from template, Copilot, and blank - Lab Exercises
Note: This lab pre-provisions a per-student Power Platform environment at start — allow up to 10 minutes for the environment to become ready before beginning the exercises. Your environment URL appears on the Environment tab under Lab Resources.
Build four cloud flows in your Ridgefield environment and prove you can switch between creation paths. You'll start from a published template (daily weather forecast), ask Copilot to build you a daily weather email, hand-assemble an automated flow that posts a Teams message when a high-importance email arrives in your mailbox, and open the Cloud flow activity view to inspect the runs you just produced.
Dataverse as a flow data source — tables, columns, choices, relationships
Before any flow in this course can read or write records, we need a shared data store. Learn Microsoft Dataverse from a Power Automate author's point of view: what a table is, what columns and data types are available, how choice columns and lookup relationships work, and what a Dataverse row looks like from the Dataverse connector in a cloud flow — all grounded in the Ridgefield Opportunity table you'll build hands-on in the paired lab.
Build the Ridgefield Dataverse data model - Lab Exercises
Note: This lab pre-provisions a per-student Power Platform environment at start — allow up to 10 minutes for the environment to become ready before beginning the exercises. Your environment URL appears on the Environment tab under Lab Resources.
Build the Ridgefield Opportunity table from scratch in your per-student Dataverse environment. You'll create the custom table with its primary column, add seven strongly-typed columns (including a Currency column, a Date-only column, a multi-line text column, and a local Choice column for Opportunity Status with values New / Open / Closed), add a Lookup column to the built-in Account table so the model is relational, and seed four realistic Ridgefield opportunity rows. This is the data model every downstream flow in this course reads from and writes to.
SharePoint as a flow data source — sites, lists, columns
Learn how SharePoint sites and lists serve as the lightweight structured data source behind Power Automate cloud flows. You will contrast SharePoint lists with Dataverse tables, learn the five SharePoint column types that matter for automation (Single line, Multiple lines, Date and time, Choice, People), and master the two SharePoint triggers that drive almost every list-based flow: When an item is created and When an item is created or modified. By the end you will know which trigger to pick for a given business event, how choice columns surface in a flow run as both a label and an integer, and what a flow sees when the first SharePoint item lands in a list.
Build the Ridgefield SharePoint list - Lab Exercises
Note: This lab pre-provisions a per-student Power Platform environment and a per-student SharePoint site at start — allow up to 10 minutes for the environment to become ready before beginning the exercises. Your environment URL and SharePoint site URL appear on the Environment tab under Lab Resources.
Build the Ridgefield Properties Tasks list inside your pre-provisioned SharePoint site. You'll open the site from the Lab Resources card, add a Tasks list, add the four columns (Description, Owner Name, Deadline, Approval Status), and seed three Ridgefield property-management rows. You'll finish with the exact SharePoint data source the next lab's approval flow is wired against.
Approval flows — Start-and-wait, approval types, parallel branches
Learn the Microsoft Power Automate Approvals connector end to end — the five approval types (Approve/Reject - First to respond, Approve/Reject - Everyone must approve, Custom responses - Wait for all responses, Custom responses - Wait for one response, Sequential approval), the Start and wait for an approval action, how approval outcomes map to Condition action branches, and when a parallel-branch flow earns its complexity. By the end you'll know which approval type fits which business event, how to read the response outputs the flow can act on, and when to parallelize approver branches so three different stakeholders can act concurrently.
Build the Tasks approval flow - Lab Exercises
Note: This lab pre-provisions a per-student Power Platform environment and a per-student SharePoint site at start — allow up to 10 minutes for the environment to become ready before beginning the exercises. Your environment URL and SharePoint site URL appear on the Environment tab under Lab Resources. The Ridgefield Tasks list is pre-populated inside the site at lab-start — you do not need to re-create it.
Wire a Microsoft Power Automate approval flow against the Ridgefield Tasks SharePoint list. You will create an automated cloud flow triggered on When an item is created on the Tasks list, resolve the task-creator's manager via Get manager (V2), route the row to the manager using Start and wait for an approval with Approve/Reject - First to respond, branch on Outcome with a Condition action, and write Approved or Declined back onto the Tasks row with two Update item actions. You will finish by adding a test row to the Tasks list, clicking Approve in the Teams Approvals app, and watching the row's Approval Status flip to Approved end-to-end.
Instant / button flows — manual triggers, inputs, mobile
Teaching lesson on Power Automate instant (button) flows. Learn when to pick a manual trigger over an automated or scheduled one, add typed user inputs (Text, Number, Yes/No, File) to a "Manually trigger a flow" trigger, run flows on demand from the Power Automate mobile app, and wire a Dataverse "When a row is selected" trigger so a model-driven app view can hand selected Opportunity rows to a flow.
Build the on-demand opportunity button flow - Lab Exercises
Note: This lab pre-provisions a per-student Power Platform environment at start — allow up to 10 minutes for the environment to become ready before beginning the exercises. Your environment URL appears on the Environment tab under Lab Resources.
Build the Ridgefield "Create opportunity" button flow from scratch. You'll create an instant cloud flow with the Manually trigger a flow trigger, add Text and Number inputs for the customer name, comments, and potential sale amount, wire a Microsoft Dataverse — Add a new row action that writes a new row to the pre-provisioned Opportunity table, test the flow with real values, and verify the opportunity row appears in Dataverse.
Scheduled flows — recurrence, list iteration, condition action
Teaching lesson on Power Automate scheduled flows. Learn how the Recurrence trigger's frequency, interval, time zone, and start time control when a flow fires; use SharePoint Get items + Apply to each + Condition to iterate a list and act on filtered rows; understand run-after settings so downstream actions can react to failures without stopping the flow; and compare client-side Apply to each + Condition filtering to a server-side OData Filter Query at Get items.
Build the daily task-reminder scheduled flow - Lab Exercises
Note: This lab pre-provisions a per-student Power Platform environment and SharePoint site at start — allow up to 10 minutes for the environment and site to become ready before beginning the exercises. Your environment URL and the SharePoint site URL (which hosts the pre-provisioned Tasks list) appear on the Environment tab under Lab Resources.
Build the Ridgefield daily task-reminder scheduled flow from scratch. You'll create a scheduled cloud flow with the Recurrence trigger set to fire daily at 08:00, add a SharePoint Get items action that reads from the pre-provisioned Tasks list, loop over the returned rows with Apply to each, use a Condition action to keep only rows whose Deadline is today, and send the task owner an HTML email via Office 365 Outlook — Send an email (V2). You'll finish by running the flow manually and verifying the email arrives.
Trigger filters — schema names, filter columns, OData filter rows
Learn why unfiltered Dataverse flow triggers wreck throughput and API capacity, and master the three precision levers that fix it: column schema names (publisher prefix + logical name), the Select columns trigger filter, and the OData Filter rows expression. Grounded in the Ridgefield Properties opportunity-flow scenario.
Add precise trigger filters to the opportunity flow - Lab Exercises
Note: This lab pre-provisions a per-student Power Platform environment at start — allow up to 10 minutes for the environment to become ready before beginning the exercises. Your environment URL appears on the Environment tab under Lab Resources.
In this lab you'll make a Ridgefield Properties opportunity flow fire only when it actually matters — when the opportunity moves to Closed. You'll find the schema name of the Opportunity Status column, build an automated Dataverse flow with the Select columns filter and an OData Filter rows expression, then prove the filter works by changing an opportunity row's status to Closed and watching the flow fire.