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Microsoft AI-102 Exam is an essential certification for professionals who want to demonstrate their expertise in designing and implementing AI solutions in Azure. It is a challenging exam that requires candidates to have a deep understanding of AI concepts and practical experience in implementing AI solutions in Azure. By passing AI-102 Exam, candidates can demonstrate their proficiency in designing and implementing AI solutions in Azure and differentiate themselves in the highly competitive field of AI.
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The Microsoft AI-102 Exam covers a wide range of topics, including natural language processing, computer vision, and speech recognition. Candidates are required to have a thorough understanding of Azure Cognitive Services, Bot Services, and Azure Machine Learning. They must also be able to design and implement AI solutions that are scalable, secure, and reliable.
NEW QUESTION # 260
You are designing a conversation flow to be used in a chatbot.
You need to test the conversation flow by using the Microsoft Bot Framework Emulator.
How should you complete the .chat file? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Graphical user interface, text, application Description automatically generated
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-howto-add-media-attachments?view=azure-bot-se
NEW QUESTION # 261
You are building an app that will scan confidential documents and use the Language service to analyze the contents.
You provision an Azure Cognitive Services resource.
You need to ensure that the app can make requests to the Language service endpoint. The solution must ensure that confidential documents remain on-premises.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
You need to analyze confidential documents with the Language service while ensuring those documents never leave your premises. The supported pattern for Azure AI Services (Cognitive Services) is to run the Language service container on-premises and send the app's requests to that local container endpoint. The container performs inference locally; only metering/billing calls are sent to Azure.
To achieve this:
* Provision an on-premises Kubernetes cluster that has internet connectivity.Azure AI service containers running on-prem require outbound access to Azure for metering and billing unless you're using the special disconnected model (not implied here). Therefore, the cluster must have internet connectivity so the container can reach the Azure billing endpoint while keeping document content local.
* Pull an image from the Microsoft Container Registry (MCR).Official Azure AI service containers (including Language) are published in MCR, not Docker Hub. You must pull the Language (Text Analytics/Language) container image from MCR.
* Run the container and specify an API key and the Endpoint URL of the Cognitive Services resource.
When you start the container, you configure it with the API key and billing (endpoint) URL of your Azure Cognitive Services (multi-service or Language) resource. This enables the container to authenticate and report usage to Azure, while your data stays on-prem.
Why not the other options?
* AKS would place workloads in Azure, which isn't required when the requirement is to keep documents on-prem.
* On-prem cluster isolated from the internet would block the container's required calls to Azure's billing endpoint.
* Pull from Docker Hub is incorrect because Azure AI service containers are published to MCR.
* App ID and Client Secret are not the standard parameters for Azure AI service containers; they require API key + billing endpoint.
* Use Azure AI services in containers (billing/endpoint and API key requirements, data remains local):
Microsoft Learn - Install and run containers for Azure AI services and Use containers with Azure AI services (metering and billing).https://learn.microsoft.com/azure/ai-services/containers/
* Language service container images on Microsoft Container Registry (MCR):Microsoft Learn - Deploy language containers (Text Analytics/Language).https://learn.microsoft.com/azure/ai-services/language- service/how-to/use-containers
* Networking requirements (internet connectivity for metering):Microsoft Learn - Configure containers and network access for Azure AI services containers.https://learn.microsoft.com/azure/ai-services
/containers/container-requirements-and-limits
These documents collectively show that: (a) containers are pulled from MCR, (b) they are configured with API key + endpoint for billing, and (c) internet egress is required for metering while payload data remains on- premises.
NEW QUESTION # 262
You are developing an application that will use the Azure Al Vision client library. The application has the following code.
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:
The given code is using the Azure AI Vision client library with the method:
image_analysis = client.analyze_image_in_stream(
image=image_stream,
visual_features=[
VisualFeatureTypes.tags,
VisualFeatureTypes.description
]
)
Breakdown of the functionality:
* VisualFeatureTypes.tags # Extracts tags (labels) describing the image (e.g., "dog", "tree", "outdoor"), including confidence scores.
* VisualFeatureTypes.description # Generates captions (natural-language sentences) that describe the image.
* The code explicitly iterates through tags and captions, printing both with their confidence values.
* The image is read from the local file system using:
* with open(local_image, "rb") as image_stream:
* Face recognition is not included because the code does not request VisualFeatureTypes.faces.
Correct Answer Matrix:
* Face recognition # No
* Tags with confidence # Yes
* Read image from local file system # Yes
* Azure AI Vision features (tags, description, faces, etc.)
* analyze_image_in_stream method
NEW QUESTION # 263
You have an Azure subscription that contains an Azure Al Language custom question answering project named QA1.
You need to import question and answer pairs to QA1.
Which two file formats can you use? Each correct answer presents a complete solution NOTE; Each correct selection is worth one point.
Answer: C,D
Explanation:
For an Azure AI Language Custom Question Answering project (QA1), you can import Q & A pairs directly from Excel (.xls/.xlsx) or TSV files in Language Studio. Microsoft's guidance explicitly states that when importing question-answer sources into a project, you can choose Excel or TSV. JSON, LU, and CSV are not supported as import file types for Q & A pairs in Language Studio. Microsoft Learn Additional Microsoft guidance notes that structured TXT, TSV, and XLS files can be uploaded to create or augment a project, and clarifies import/export behavior for TSV and XLS. This reinforces that Excel and TSV are the supported formats here. Microsoft Learn Microsoft References:
Move projects and Q & A pairs - "Import question and answers... option to import either an Excel or TSV file." Microsoft Learn Format guidelines for Custom Question Answering - structured TXT, TSV, and XLS supported; import/export notes for TSV and XLS. Microsoft Learn
NEW QUESTION # 264
You have an Azure subscription that contains an Azure OpenA1 resource. You configure a model that has the following settings:
* Temperature: 1
* Top probabilities: 0.5
* Max response tokens: 100
You ask the model a question and receive the following response.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 265
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