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| Section | Weight | Objectives |
|---|---|---|
| Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Implement agentic solutions | 20-25% | - Build AI agents
|
| Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
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NEW QUESTION # 146
You plan to build an agent that will combine and process multiple files uploaded by users.
You are evaluating whether to use the Azure AI Agent Service to develop the agent.
What is the maximum size of each file that can be uploaded to the service?
Answer: B
Explanation:
https://learn.microsoft.com/en-us/azure/ai-services/agents/quotas-limits
NEW QUESTION # 147
You have a Microsoft Foundry project. You need to deploy a model from the model catalog to support real- time inference. The solution must meet the following requirements:
* Use key-based authentication
* Support real-time REST API access
* Not consume the vCPU quota of the virtual machines in the Azure subscription Which type of deployment should you use?
Answer: C
Explanation:
A serverless API deployment exposes a model from the Microsoft Foundry model catalog as a managed inference endpoint without hosting the model on virtual-machine compute in the customer's subscription.
Consequently, it does not consume the subscription's VM-family vCPU quota. Capacity is managed by the service and controlled through deployment-level token and request rate limits rather than customer-managed compute instances.
The generated serverless endpoint uses key authentication. Foundry provides a target URI and associated primary or secondary credentials that the application uses to authorize inference requests. Serverless deployments support the Azure AI Model Inference API, enabling real-time application access through a consistent API interface suitable for REST-based prediction requests.
A self-hosted container requires customer-managed infrastructure and therefore consumes the compute capacity on which it runs. A compute-backed standard deployment generally requires provisioned hosting resources and applicable quota. A batch deployment is designed for asynchronous, high-volume processing rather than interactive real-time inference.
The serverless option therefore satisfies all three requirements simultaneously: key-based authorization, online API inference, and no dependency on the subscription's virtual-machine vCPU quota.
Study Guide alignment: Planning and managing Azure AI solutions - select model deployment options, deploy catalog models, configure endpoint authentication, and evaluate compute and quota requirements.
NEW QUESTION # 148
You have 10,000 ASCII files and need to identify files containing specific phrases by using cosine similarity.
Answer: B
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
text-embedding-ada-002 is an embedding model . Embedding models transform text into dense numerical vectors representing semantic characteristics of the input.
The application can generate an embedding for each relevant file or text chunk and store those vectors. When a user supplies a phrase, the application generates an embedding for that phrase by using the same embedding model. It can then calculate cosine similarity between the query vector and stored vectors.
Cosine similarity measures the angular similarity between vectors. Values representing semantically similar text will generally be closer even when the exact wording differs. This makes embeddings appropriate for semantic file retrieval, clustering, similarity search, and RAG.
GPT-4, GPT-35 Turbo, and GPT-4-32k are generative language models rather than dedicated vector- embedding models. They can reason about text but are not the appropriate options for producing the vector representations required by the question.
Although newer embedding-model generations exist in current Azure model catalogs, among the choices presented, text-embedding-ada-002 is the correct model family and answer.
Study Guide alignment: Build retrieval and grounding pipelines, configure vector search, and generate representations used for semantic retrieval . Microsoft Learn
NEW QUESTION # 149
You are building a speech processing solution in Microsoft Foundry for a customer support platform.
The platform will transcribe live phone calls, so that supervisors at your company can view call transcripts and detect issues while the calls are in progress. The call audio will arrive as a continuous stream from the telephony system.
You need to ensure that the call transcripts appear within only a few seconds of the audio stream.
What should you do?
Answer: A
Explanation:
You should use Real-Time Speech-to-Text (Streaming STT) for this project.
Why Real-Time STT is Required
Low Latency: It transcribes audio within seconds.
Continuous Input: It processes live, ongoing audio streams.
Live Monitoring: Supervisors need immediate text to flag issues.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/speech-service/get-started-speech-to-text
NEW QUESTION # 150
A legal team must extract clauses and inferred fields from unstructured contracts. One required field is the contract end date, which is not stated explicitly and must be derived from the start date plus the term. The team has no labelled training data. Which tool best fits?
Answer: D
Explanation:
Azure Content Understanding handles unstructured documents, works zero-shot without labelled data, and can infer fields that are not explicitly present, such as deriving a contract end date from the start date and term. That matches every requirement in the scenario.
NEW QUESTION # 151
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