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| Section | Weight | Objectives |
|---|---|---|
| Create intelligent applications | 25-30% | - Create model-driven apps
|
| Create a foundation for intelligent applications | 25-30% | - Build data models in Dataverse
|
| Build business application logic and automation | 40-45% | - AI prompts and models
|
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NEW QUESTION # 73
A developer creates an intelligent document processing solution that extracts invoice numbers, vendor information, and payment totals from scanned PDF files. The documents have different layouts depending on the supplier. The solution should minimize manual model training while supporting structured extraction. Which service should be selected?
Answer: D
Explanation:
Azure AI Document Intelligence is specifically designed for extracting structured information from documents with varying layouts. It provides prebuilt and custom models for invoices, receipts, and forms. OCR only extracts text without understanding document structure. Azure AI Search and embeddings improve retrieval scenarios but do not directly perform document field extraction.
NEW QUESTION # 74
A company uses a cloud flow to analyze incoming order form PDFs attached to emails.
The cloud flow must use an AI model to extract information from each order. The information must then be added to the enterprise resource planning (ERP) system.
You need to configure the flow.
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
The flow must add the AI Builder Process documents action and configure its model, document type, and document input. Process documents is the action associated with a trained document-processing model; it reads the attached PDF and returns the fields or tables defined by that model. The flow then maps those outputs to the ERP connector or API operation. Extract standard entities processes plain text for predefined entities and is not the document-field extraction operation described. Classify into standard categories assigns text categories rather than extracting order data. Specifying language and text belongs to text-processing actions and does not provide the required PDF/model configuration. The flow should retrieve attachment content, pass the binary document in a supported format, validate confidence scores or missing fields, and send failed documents to an exception path rather than silently writing incomplete ERP data. The Study Guide explicitly includes "consume an AI model in cloud flows" and "configure cloud flow actions." Options C and E provide the required model invocation and action configuration for document extraction.
Study Guide reference/topic: "Create prompts and models in AI Hub - Consume an AI model in cloud flows." AB-410 Study Guide | Microsoft Learn technical reference
NEW QUESTION # 75
A company uses Dataverse to manage customer accounts and related transaction records.
The company requires the following functionality:
Reference a single related record without performing any aggregations.
Store the relationship as part of the table definition, so the platform automatically maintains the link between records.
You need to configure a column.
Which type of column should you configure?
Answer: C
Explanation:
A Lookup column is designed to reference one row in another Dataverse table. The selected related row's identifier is stored as part of the relationship, and Dataverse maintains the association so forms, views, queries, and security behaviors can work with it consistently. The requirement explicitly excludes aggregation, which rules out a Rollup column; rollups calculate values such as sum, count, minimum, maximum, or average over related rows. A Calculated column evaluates an expression from other values, while a Formula column uses Power Fx to derive a result. Neither is the correct structural mechanism for selecting and maintaining a single related account or transaction row. The lookup's relationship should be configured with the appropriate required level, cascade behavior, and delete rule so it matches the record lifecycle. This is not merely a display field: it is a schema-level relationship component. The AB-410 Study Guide lists "create and modify columns" and "configure table relationships" under Build data models. A Lookup column is therefore the only option that natively represents the required one-record association.
Study Guide reference/topic: "Build data models - Create and modify columns." AB-410 Study Guide | Microsoft Learn technical reference
NEW QUESTION # 76
A company builds a canvas app that processes images submitted by users and must classify them using an existing AI Builder model.
The app must generate predictions during user interaction.
You need to consume the AI model natively.
What should you do?
Answer: B
Explanation:
The existing AI Builder classification model should be invoked from the canvas app through Power Fx. The maker adds the model as an app data source or AI capability and calls its prediction operation from the relevant user action, passing the current image. The returned classification and confidence can then be stored in variables, displayed to the user, or written to Dataverse. A static formula cannot reproduce a trained image model's inference. Refining the model with additional images is a training-lifecycle task and may improve quality later, but it does not consume the existing model during the current interaction. Exporting the containing solution transports components between environments; it does not execute a prediction. The app formula should validate that an image is present, handle service errors, and define what happens when confidence is below the accepted threshold. The Study Guide contains the exact objective "consume an AI model in apps." Option A is the native runtime integration that classifies each user-submitted image during the active canvas-app session.
Study Guide reference/topic: "Create prompts and models in AI Hub - Consume an AI model in apps." AB-
410 Study Guide | Microsoft Learn technical reference
NEW QUESTION # 77
A developer creates an AI application requiring semantic search over millions of product descriptions. Users should find relevant products even when search terms do not exactly match stored text. Which capability is required?
Answer: A
Explanation:
Vector embeddings convert text into numerical representations that capture semantic meaning.
Vector search can identify related concepts even when exact keywords differ. Traditional keyword indexing depends heavily on matching terms. SQL procedures and compression techniques do not provide semantic understanding required for intelligent search experiences.
NEW QUESTION # 78
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