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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of generative AI workloads on Azure | 10-15% | - Generative AI concepts
|
| Topic 2: Describe Artificial Intelligence workloads and considerations | 20-25% | - Responsible AI principles
|
| Topic 3: Describe features of computer vision workloads on Azure | 15-20% | - Azure AI Vision services
|
| Topic 4: Describe fundamental principles of machine learning on Azure | 25-30% | - Azure Machine Learning basics
|
| Topic 5: Describe features of natural language processing (NLP) workloads on Azure | 15-20% | - Azure NLP services
|
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NEW QUESTION # 24
You are developing an application that extracts fields from PDFs by using Azure Content Understanding in Foundry Tools.
You need to use the Python SDK to submit a PDF for analysis and retrieve the extraction results.
What should you do?
Answer: A
Explanation:
Azure Content Understanding analysis operations are long-running operations in the Python SDK. Microsoft's Python SDK documentation states that analysis operations return a poller, and the SDK provides LROPoller types that handle polling automatically when you call .result().
Therefore, the correct workflow is to submit the PDF by calling begin_analyze(), receive a poller, and then call:
result = poller.result()
Option B is incorrect because extraction results are not read from request headers.
Option C is incorrect because the requirement is to use Azure Content Understanding extraction, not build a manual OCR-only mapping pipeline.
Option D is incorrect because the SDK analysis pattern is asynchronous/long-running, not a simple synchronous analyze() call that returns all extracted fields in the same request.
NEW QUESTION # 25
You are developing an application that extracts structured information from different types of content by using Azure Content Understanding in Foundry Tools.
You need to extract scanned invoices in the PDF format and voicemail recordings in the WAV format.
Which type of analyzer should you use for each content type? To answer, drag the appropriate analyzer types to the correct content types. Each analyzer type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 26
What are two purposes of instructions when prompting a generative AI model? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
Giving a generative AI model clear, structural instructions (commonly referred to as Prompt Engineering) is the standard method used to define its persona, guide its behavior, and place strict operational boundaries on its responses.This is typically achieved through explicit instruction sets like System Instructions and targeted Prompt Guidelines.
1. Defining the Agent's Role and BehaviorEstablishing a persona prevents the AI from wandering into areas outside its intended scope and ensures the delivery style matches your needs.
2. Defining Constraints on ResponsesConstraints act as guardrails that prevent the AI from hallucinating, rambling, or providing incorrect information.
Reference:
https://ai.google.dev/gemini-api/docs/prompting-strategies
NEW QUESTION # 27
You are developing an application that converts text into spoken audio and saves the synthesized audio to a file by using Azure Speech in Foundry Tools.
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:
NEW QUESTION # 28
Which feature of the Azure Language in Foundry Tools service should you use to automate the masking of names and phone numbers in text data?
Answer: B
Explanation:
To automate masking of names and phone numbers in text data, use Personally Identifiable Information (PII) detection in Azure Language.
PII detection identifies sensitive personal information such as names, phone numbers, email addresses, addresses, and other personal identifiers. It can be used to detect and redact or mask that information from text.
The other options are incorrect:
A). custom text classification categorizes documents or text into labels.
C). entity linking identifies entities and links them to knowledge base entries.
D). custom named entity recognition (NER) can extract custom entities, but for built-in masking of names and phone numbers, PII detection is the correct feature.
NEW QUESTION # 29
......
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