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Microsoft AI-102 exam covers various topics related to designing and implementing AI solutions, such as data preparation, machine learning models, natural language processing, computer vision, and conversational AI. It also includes topics related to deploying and managing AI solutions on the Azure platform. AI-102 exam consists of both multiple-choice and lab-based questions, and candidates are required to demonstrate their practical skills in designing and implementing AI solutions on Azure. Passing AI-102 Exam requires a deep understanding of Azure AI services and the ability to design and implement AI solutions to solve real-world business problems.
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Microsoft AI-102 is a certification exam that is designed to test the skills and knowledge of candidates in designing and implementing solutions that use Microsoft Azure AI. Designing and Implementing a Microsoft Azure AI Solution certification exam is intended for professionals who have a strong understanding of Microsoft Azure and its services, as well as experience in the design and deployment of AI solutions. AI-102 Exam is designed to assess a candidate's ability to use Azure AI services to solve business problems and create intelligent applications.
NEW QUESTION # 173
You are building a Language Understanding model for purchasing tickets.
You have the following utterance for an intent named PurchaseAndSendTickets.
Purchase [2 audit business] tickets to [Paris] [next Monday] and send tickets to [email@domain.com] You need to select the entity types. The solution must use built-in entity types to minimize training data whenever possible.
Which entity type should you use for each label? To answer, drag the appropriate entity types to the correct labels. Each entity 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.
Answer:
Explanation:
Explanation:
Box 1: GeographyV2
The prebuilt geographyV2 entity detects places. Because this entity is already trained, you do not need to add example utterances containing GeographyV2 to the application intents.
Box 2: Email
Email prebuilt entity for a LUIS app: Email extraction includes the entire email address from an utterance.
Because this entity is already trained, you do not need to add example utterances containing email to the application intents.
Box 3: Machine learned
The machine-learning entity is the preferred entity for building LUIS applications.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-geographyv2
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-email
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/reference-entity-machine-learned-entity
NEW QUESTION # 174
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:
Platform as a service (PaaS) database offerings in Azure require less setup and configuration effort than Infrastructure as a Service (IaaS) database offerings.
Yes
PaaS abstracts away the OS, patching, backups, and much of the infrastructure management. IaaS requires users to configure and manage VMs and databases manually.
Platform as a service (PaaS) database offerings in Azure provide end users with the ability to control and update the operating system version.
No
In PaaS, the OS and platform management are handled by Azure. Users cannot directly control or update the OS version. This is a responsibility difference between IaaS and PaaS.
All relational and non-relational platform as a service (PaaS) database offerings in Azure can be paused to reduce costs.
No
Not all PaaS database services support pausing. For example, Azure SQL Database serverless can auto-pause, but Azure Cosmos DB and provisioned SQL databases cannot. Hence this statement is incorrect.
Correct Responses:
Yes
No
No
Microsoft References
Azure SQL Database - PaaS overview
What is PaaS?
Serverless compute tier in Azure SQL Database (auto-pause)
NEW QUESTION # 175
You have an Azure subscription that contains an Azure OpenAI resource.
You need to build an app that will suggest product names from a given product description.
Which model should you use?
Answer: A
Explanation:
To suggest product names from a given product description, the ideal model is GPT-4. GPT-4 is a powerful language model that can understand and generate human-like text based on given inputs, making it well-suited for tasks such as generating creative suggestions like product names from descriptions.
Whisper is used for speech-to-text conversion, not for generating text suggestions.
DALL-E is designed for generating images from text prompts, not for text generation tasks like naming products.
Embeddings are used for tasks involving similarity search and semantic understanding but not for generating new text like product names.
NEW QUESTION # 176
What is a primary characteristic of a relational database?
Answer: C
NEW QUESTION # 177
You are developing a streaming Speech to Text solution that will use the Speech SDK and MP3 encoding.
You need to develop a method to convert speech to text for streaming MP3 data.
How should you complete the code? 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, email Description automatically generated
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/how-to-use-codec-compressed-audio-in
NEW QUESTION # 178
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