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| Section | Weight | Objectives |
|---|---|---|
| Describe features of natural language processing (NLP) workloads on Azure | 15-20% | - Azure NLP services
|
| Describe features of generative AI workloads on Azure | 10-15% | - Azure OpenAI and generative services
|
| Describe Artificial Intelligence workloads and considerations | 20-25% | - Responsible AI principles
|
| Describe fundamental principles of machine learning on Azure | 25-30% | - Azure Machine Learning basics
|
| Describe features of computer vision workloads on Azure | 15-20% | - Image analysis
|
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NEW QUESTION # 40
Hotspot Question
You are developing an application that analyzes invoices by using Azure Content Understanding in Foundry Tools.
You have a custom analyzer named invoiceAnalyzer.
You need to use the analyzer to process invoice files.
How should you complete the Python code? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box: analyzer_id
Core Method Signature
poller = client.begin_analyze(
analyzer_id="YOUR_ANALYZER_ID",
inputs=[AnalysisInput(url="FILE_URL")],
**kwargs
)
Note:
client.begin_analyze is the primary method used in the official Azure AI Content Understanding Python SDK (azure-ai-contentunderstanding) to kick off asynchronous, long-running operations (LRO) for extracting structured data and layout information from multimodal files.
Formerly a part of standalone Azure AI services, Content Understanding is fully integrated into the Microsoft Foundry ecosystem. This service utilizes foundational large language models deployed inside your Foundry resource to analyze documents, images, audio, and video streams based on prebuilt or custom schemas.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/quickstart/use-rest-api
NEW QUESTION # 41
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
A schema defines which fields to extract when analyzing content.
In Azure Content Understanding, the schema or fieldSchema defines the structured data that the analyzer extracts from content, including field names, types, and extraction behavior. Microsoft documentation states that Content Understanding lets you define a schema to extract, classify, or generate field values from unstructured content.
The other options are incorrect:
A keyword list does not define the complete structured output fields.
OCR-only processing extracts text, but it does not define structured fields.
A synchronous API call describes a request pattern, not the extraction schema.
NEW QUESTION # 42
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 43
For each of the following statements, select Yes if the statement is true. Otherwise, select NOTE Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Statement 1: Azure Content Understanding in Foundry Tools can analyze only PDF documents. = No Azure Content Understanding is not limited to PDF documents. It can analyze multiple content types, including documents, forms, images, audio, and video.
Statement 2: Azure Content Understanding in Foundry Tools results are returned in the JSON format. = Yes Azure Content Understanding returns structured analysis results in JSON format. This is how extracted fields, values, confidence scores, and other analysis results are represented.
Statement 3: Azure Content Understanding in Foundry Tools can extract structured fields from documents and forms. = Yes Azure Content Understanding can use analyzers and schemas to extract structured fields from documents and forms, such as invoices, receipts, contracts, and other business documents.
NEW QUESTION # 44
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
A system prompt, also called a system message, is used to guide how a generative AI model behaves, including its tone, role, response style, and output format. Microsoft states that system messages help steer an Azure OpenAI chat model toward the desired behavior, tone, and output format.
Statement 1: System prompts can be used to authorize users. = No
Authorization is handled by application identity, access control, authentication, and security configuration. A system prompt can guide model behavior, but it is not an authorization mechanism.
Statement 2: A system prompt is used to reduce tokens per minute (TPM). = No TPM is a quota or throughput setting for model deployments. A system prompt does not reduce or configure TPM.
Statement 3: A system prompt guides the behavior of a generative AI model. = Yes This is correct. Microsoft documentation says system messages help steer the model's behavior, tone, and output format.
NEW QUESTION # 45
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