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| Section | Weight | Objectives |
|---|---|---|
| Implement natural language processing solutions | 15-20% | - Perform text analysis, sentiment detection, and language detection - Build conversational AI and chatbots - Implement translation and summarization - Customize and deploy NLP models |
| Implement computer vision solutions | 10-15% | - Analyze images and detect objects/features - Process and index video content - Integrate vision capabilities into applications - Build and deploy custom vision models - Extract text and handwriting from images |
| Implement generative AI solutions | 15-20% | - Implement model monitoring and feedback - Orchestrate multiple models and containers - Deploy and manage generative models - Apply prompt engineering and fine-tuning - Integrate Azure OpenAI and other generative models |
| Plan and manage an Azure AI solution | 20-25% | - Select appropriate Microsoft Foundry Services - Select suitable AI models - Monitor, optimize, and secure AI solutions - Choose services for generative AI, computer vision, NLP, speech, information extraction, knowledge mining - Plan solutions aligned with responsible AI principles - Create and configure Azure AI resources |
| Implement an agentic solution | 5-10% | - Build agents with Microsoft Foundry Agent Service - Understand agent use cases and types - Test, deploy, and optimize agents - Develop multi-agent workflows and orchestration |
| Implement knowledge mining and information extraction solutions | 15-20% | - Extract entities, relationships, and key phrases - Ingest and process structured/unstructured data - Build knowledge bases and search indexes - Implement intelligent search and retrieval |
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NEW QUESTION # 259
Match the Azure Cosmos DB APIs to the appropriate data structures.
To answer, drag the appropriate API from the column on the left to its data structure on the right. Each API may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
Azure Cosmos DB supports multiple APIs, each aligned to a specific data model:
* Gremlin API
* Designed for graph data.
* Stores vertices (nodes) and edges (relationships).
* Query language: Gremlin.
* Correct mapping: Graph data.
* MongoDB API
* Designed for document data using JSON.
* Supports MongoDB wire protocol.
* Correct mapping: JSON documents.
* Table API
* Designed for key-value data.
* Compatible with Azure Table Storage.
* Correct mapping: Key/value data.
* Cassandra API
* Designed for wide-column (column-family) data.
* Uses CQL (Cassandra Query Language).
* Not listed in the answer area, but if included, it maps to columnar data, not the options shown here.
Correct Mapping:
* Graph data # Gremlin API
* JSON documents # MongoDB API
* Key/value data # Table API
* APIs in Azure Cosmos DB
* Choose the right API for Azure Cosmos DB
Microsoft References
NEW QUESTION # 260
You are using a Language Understanding service to handle natural language input from the users of a web-based customer agent.
The users report that the agent frequently responds with the following generic response: "Sorry, I don't understand that." You need to improve the ability of the agent to respond to requests.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. (Choose three.)
Answer:
Explanation:
NEW QUESTION # 261
You have an Azure subscription.
You plan to build a solution That will analyze scanned documents and export relevant fields to a database.
You need to recommend which Azure Al service to deploy for the following types of documents:
* Internal expenditure request authorization forms
* Supplier invoices
The solution must minimize development effort.
What should you recommend for each document type? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point
Answer:
Explanation:
Explanation:
NEW QUESTION # 262
You create a knowledge store for Azure Cognitive Search by using the following JSON.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic. NOTE Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
1. Number of Projection Groups
A projection group in a knowledge store is an array of projections ( tables , objects , or files ) that are stored together.
In the provided JSON, the projections array contains the following two top-level sections:
The array of tables :
JSON
" tables " : [
{ ... },
{ ... }
]
The array of objects :
JSON
" objects " : [
{ ... }
]
Each of these top-level arrays ( tables and objects ) represents a distinct projection group in the knowledge store. Therefore, there are two projection groups .
2. Image Saving
The files projection is used to save images, normalized images, or application files generated during the enrichment process.
In the provided JSON, the files array is present but is empty :
JSON
" files " : []
Since there are no file projections defined, the images (or any files) generated during the enrichment process will not be saved to the knowledge store. They are processed in the pipeline but not persisted in the output storage defined by the knowledgeStore configuration.
NEW QUESTION # 263
You have an Azure subscription that contains an Azure Al Document Intelligence resource named Aldoc1.
You have an app named App1 that uses Aldoc1. App1 analyzes business cards by calling business card model v2.1.
You need to update App1 to ensure that the app can interpret QR codes. The solution must minimize administrative effort.
What should you do first?
Answer: A
Explanation:
Your app currently calls the prebuilt Business Card model v2.1. In v2.1, the platform does not provide QR/barcode interpretation.
Support for decoding barcodes (including QR codes) was introduced with the v3.x API family as an add-on capability ( ocr.barcode ) that you can enable on analysis requests in API versions 2023-07-31 (GA) and later.
Therefore, the first step is to upgrade from v2.1 to v3.0 so your application can use the newer API surface that supports barcode/QR extraction.
This meets the "minimize administrative effort" requirement because you keep the same Azure AI Document Intelligence resource, keys, and endpoint-your change is primarily to the API/SDK version and request options.
After upgrading, you have two common implementation patterns:
Continue to use prebuilt Business Card for the contact fields, and in the same flow make an additional call with prebuilt Layout (or General Document) with the ocr.barcode add-on enabled to decode any QR code present on the card.
This is the recommended path because the Business Card model itself doesn't expose add- on features, while the platform's v3.x analysis APIs do support ocr.barcode on other models. Microsoft Learn Why the other options are not correct:
A). Deploy a custom model - unnecessary effort; barcode/QR decoding is available out-of-the-box in v3.x via ocr.barcode . Microsoft Learn
B). Implement the read model - Read (OCR) extracts text but does not interpret barcodes/QR codes; you need the barcode add-on introduced in v3.x. Microsoft Learn
D). Implement the contract model - Irrelevant to business cards or QR decoding.
Key Microsoft References
Add-on capabilities (including ocr.barcode with QR support) - available in API 2023-07-31 (GA) and later.
Microsoft Learn
Barcode/QR extraction behavior and schema (barcodes collection, kind, value, polygon). Microsoft Learn Migration guide from v2.1 to v3.x (why upgrade and what changes).
Azure Docs Business Card model documentation and versioning context (v2.1/v3.0/v3.1). Microsoft Learn SDK/blog announcement of barcode add-on in v3.x (what it returns and when it's supported).
Microsoft for Developers Code samples showing the barcodes add-on usage. github.com
NEW QUESTION # 264
......
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