A user-defined function and a component library solve different forms of duplication in the canvas-app estate. A user-defined function centralizes reusable Power Fx logic behind a named, parameterized function, so formulas do not need to be copied and maintained independently. A component library stores reusable canvas components that can be consumed by multiple apps and updated through a controlled component lifecycle. That directly supports the requirement for reusable UI elements across applications. A collection is an in-memory table used during an app session, not a cross-application logic package. A context variable is screen scoped and cannot standardize behavior across apps. A named formula can centralize a reusable calculation within an app, but the question’s paired requirements emphasize reusable logic and reusable UI across multiple applications; the function and component library provide the complete combination. The components should be packaged in solutions and governed through ALM so dependent apps receive tested versions. The Study Guide names “user-defined functions” and “component libraries” within its exact objective to “create reuseable components,” making B and D the aligned selections.

Study Guide reference/topic: “Create canvas apps — Create reusable components.” AB-410 Study Guide | Microsoft Learn technical reference

Technician name on one screen — Context variable; Work orders — Collection; Logic statements — Named formula

A context variable should hold the technician’s name because the value is required only on the home screen. Context variables are screen scoped, which limits the state to the place where it is needed and avoids creating wider application dependencies. A collection is the correct structure for work orders because it stores a table of records that a gallery can enumerate, filter, and display. A named formula is appropriate for reusable logic statements that should be calculated consistently without imperatively setting a variable. The original answer image selects a global variable for the technician name, but that scope is broader than required; the image is retained unchanged while the verified mapping uses Context variable. A global variable is appropriate when a scalar value must be available across screens, not when the requirement explicitly limits persistence to one screen. The app should also avoid loading an unnecessarily large work-order collection and should delegate filtering to the data source where possible. The Study Guide directly lists “manage variables and collections” and “named formulas” under Create canvas apps. The verified mapping applies each state mechanism at its intended scope and data shape.

Study Guide reference/topic: “Create canvas apps — Manage variables and collections; Create reusable components.” AB-410 Study Guide | Microsoft Learn technical reference

A custom prompt is the native low-code mechanism for generating an inspection report from current app data while following the company’s existing template instructions. The maker can define inputs for the inspection fields, specify required sections, tone, order, and formatting, and invoke the prompt from the new app. This keeps the report-generation experience within Power Platform and minimizes custom AI development. A paginated report is well suited to deterministic, pixel-precise reporting, but it does not perform the requested generative composition and generally presents a separate reporting artifact. A Word template performs deterministic field substitution and is useful when no generative rewriting is required; it does not supply the AI-driven custom narrative described in the case. The prompt should be grounded only with approved inspection data, tested for missing inputs, and reviewed for output consistency before production use. The Study Guide covers “build prompts,” “add inputs to a prompt,” and “consume a prompt in apps.” Option C is therefore the component that directly implements native generative inspection-report creation.

Study Guide reference/topic: “Create prompts and models in AI Hub — Consume a prompt in apps.” AB-410 Study Guide | Microsoft Learn technical reference

Calling the prompt from a Power Automate cloud flow provides a reusable service-level entry point that can be invoked by different Power Platform experiences. The flow can accept parameters from each caller, pass those values into the prompt, and then route the generated result to an app, Dataverse, email, or another process. This addresses reuse more effectively than binding the prompt directly to one canvas app. An input variable is necessary for dynamic data, but by itself it does not establish cross-service orchestration; the flow exposes and manages those inputs for multiple callers. Returning JSON can standardize output handling but does not make the prompt reusable by itself. Direct canvas-app invocation is valid for an app-only requirement, not for the stated need to span Power Platform services. The flow should define explicit input types and validate required values so the case’s inconsistent-input problem is not repeated. The AB-410 Study Guide lists both “add inputs to a prompt” and “consume a prompt in cloud flows.” Option B provides the reusable execution layer while allowing dynamic values to be supplied at runtime.

Study Guide reference/topic: “Create prompts and models in AI Hub — Consume a prompt in cloud flows.” AB-410 Study Guide | Microsoft Learn technical reference