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| Section | Weight | Objectives |
|---|---|---|
| Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%) | 15-20% | - Identify features of common NLP workload scenarios
|
| Describe features of computer vision workloads on Azure (15-20%) | 15-20% | - Identify Azure tools and services for computer vision tasks
|
| Describe Artificial Intelligence workloads and considerations (15-20%) | 15-20% | - Identify guiding principles for responsible AI
|
| Describe fundamental principles of machine learning on Azure (15-20%) | 15-20% | - Identify common machine learning techniques
|
| Describe features of generative AI workloads on Azure (20-25%) | 20-25% | - Identify features of generative AI solutions
|
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NEW QUESTION # 114
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: transcription
Azure Content Understanding in Foundry Tools uses transcription preprocessing to convert spoken words from audio and video files into text-based transcripts.
Optimized Preprocessing: Transcription runs as an initial extraction step to provide rich textual context before passing data to generative AI models.
Transcript Formatting: Conversational audio is turned into searchable WebVTT transcripts containing word-level or sentence-level timestamp.
Speaker Separation: Features like diarization identify and distinguish different speakers, while speaker role detection can pinpoint specific roles (such as agent and customer).
Multilingual Handling: It supports automatic language identification and multi-language phrase transcription.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/content-understanding/video/overview
NEW QUESTION # 115
Which statement is an example oi a Microsoft responsible Al principle?
Answer: C
Explanation:
"AI systems must be understandable" is an example of the Microsoft responsible AI principle of transparency
. Microsoft's responsible AI framework includes fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
The other options are incorrect: AI systems should not merely protect company interests, should not be limited only to public data, and must not keep personal details public.
NEW QUESTION # 116
What should you use to explore pretrained generative AI models available from Microsoft and third-party providers?
Answer: C
Explanation:
Azure AI Foundry Models gives you access to flagship models in Azure AI Foundry to consume them as APIs with flexible deployment options.
Depending on what kind of project you're using in Azure AI Foundry, you might see a different selection of these models. Specifically, if you're using a Foundry project, built on an Azure AI Foundry resource, you see the models that are available for standard deployment to a Foundry resource. Alternatively, if you're using a hub-based project, hosted by an Azure AI Foundry hub, you see models that are available for deployment to managed compute and serverless APIs.
These model selections do overlap in many cases, since many models support the multiple deployment options.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-models/concepts/models
NEW QUESTION # 117
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The Model catalog is used for comparing and deploying a wide range of models for generative AI development.
Microsoft states that the model catalog in Foundry portal is the hub for discovering and using a wide range of models to build generative AI applications. Microsoft training also states that the model catalog is used to compare models by benchmark metrics and deploy models to endpoints.
NEW QUESTION # 118
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:
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 different 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 output in JSON format.
Statement 3: Azure Content Understanding in Foundry Tools can extract structured fields from documents and forms. = Yes Azure Content Understanding can extract structured fields from documents and forms by using analyzers and schemas.
NEW QUESTION # 119
......
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