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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe features of computer vision workloads on Azure | 15-20% | - Azure AI Vision services
|
| Topic 2: Describe features of generative AI workloads on Azure | 10-15% | - Generative AI concepts
|
| Topic 3: Describe features of natural language processing (NLP) workloads on Azure | 15-20% | - Text analysis
|
| Topic 4: Describe fundamental principles of machine learning on Azure | 25-30% | - Machine learning concepts
|
| Topic 5: Describe Artificial Intelligence workloads and considerations | 20-25% | - Identify features of common AI workloads
|
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NEW QUESTION # 45
Hotspot Question
You are developing an application that analyze invoices by using Azure Content Understanding in Foundry Tools.
You need to ensure that the application retrieves the analysis results after processing completes.
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 # 46
You have a Microsoft Foundry project that contains a vision-enabled model deployment.
You need to create a prompt that ensures the model produces a relevant and useful response.
What should you include in the prompt?
Answer: C
Explanation:
A description of the task must be included in the prompt to ensure the model produces a relevant and useful response.
A prompt's core purpose is to tell the model exactly what it needs to accomplish. To get a useful and relevant output, you must explicitly provide instructions, context, rules, or the objective of the task (such as "extract the total price from this receipt" or "describe the objects in this photo").
Reference:
https://learn.microsoft.com/en-us/azure/foundry/foundry-models/how-to/deploy-foundry-models
NEW QUESTION # 47
You are developing an application that uses the Azure Language SDK and a Microsoft Foundry resource.
You need to return a list of important topics from unstructured text.
Which Python method should you use?
Answer: A
Explanation:
To return a list of important topics from unstructured text using the Azure Language Python SDK within a Microsoft Foundry resource, you should use the extract_key_phrases method.
Depending on whether you are running synchronous or asynchronous operations for large batches of documents, choose one of the following methods from the TextAnalyticsClient:
client.extract_key_phrases(documents)
Used for immediate, synchronous processing of text data. It returns results in real time and is ideal for smaller or interactive application workloads.client.begin_extract_key_phrases(documents) Used for asynchronous (batch) processing. It returns a long-running operation poller, which is highly recommended when analyzing large volumes of unstructured text or multiple documents simultaneously.
Reference:
https://notes.kodekloud.com/docs/AI-102-Microsoft-Certified-Azure-AI-Engineer-Associate/Analyzing-Text/Working-with-Azure-AI-Language-Services/page
NEW QUESTION # 48
Which natural language processing feature can be used to identify the main talking points in customer feedback surveys?
Answer: A
Explanation:
To identify the main talking points in customer feedback surveys, use key phrase extraction . This feature identifies important words and phrases in text, which is useful for summarizing themes in feedback.
NEW QUESTION # 49
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:
Explanation:
Azure Content Understanding analyzers define the type of content to process, such as documents, images, audio, or video , and what to extract, such as text, layout, fields, transcripts, and structured JSON output.
Microsoft lists base analyzer types such as prebuilt-document, prebuilt-audio, prebuilt-video, and prebuilt- image.
For scanned invoices in PDF format , the correct choice is document analyzer , because invoices are document/form content. Microsoft's Content Understanding quickstart specifically uses the prebuilt-invoice analyzer to extract structured data from an invoice PDF.
For voicemail recordings in WAV format , the correct choice is audio analyzer , because voicemail is audio content. Content Understanding supports structured data extraction from audio files, and the analyzer reference identifies prebuilt-audio as the base analyzer for audio content.
NEW QUESTION # 50
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