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Microsoft AI-102 Certification Exam is an excellent opportunity for professionals who want to showcase their skills and knowledge in designing and implementing AI solutions using Microsoft Azure services. AI-102 exam covers various topics related to AI and Azure services, and passing the exam validates the candidate's ability to solve real-world problems. Designing and Implementing a Microsoft Azure AI Solution certification also provides a competitive edge to professionals and helps them advance their careers in the field of Artificial Intelligence.
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The Microsoft AI-102 exam is intended for AI solution architects, AI developers, and data scientists who have experience in designing and implementing AI solutions on the Microsoft Azure platform. Candidates are expected to have a solid understanding of programming languages such as Python, R, and C#, as well as experience in designing and implementing AI solutions using these languages. AI-102 Exam is a great way for professionals to demonstrate their skills and knowledge in AI solution design and implementation on the Azure platform, and to gain recognition as a Microsoft Certified Azure AI Engineer Associate.
NEW QUESTION # 103
You plan to use containerized versions of the Anomaly Detector API on local devices for testing and in on-premises datacenters.
You need to ensure that the containerized deployments meet the following requirements:
* Prevent billing and API information from being stored in the command-line histories of the devices that run the container.
* Control access to the container images by using Azure role-based access control (Azure RBAC).
Which four 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. (Choose four.) NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Answer:
Explanation:
Explanation:
Step 1: Pull the Anomaly Detector container image.
Step 2: Create a custom Dockerfile
Step 3: Build the image
Step 4: Push the image to an Azure container registry.
https://docs.microsoft.com/en-us/azure/cognitive-services/containers/container-reuse-recipe
NEW QUESTION # 104
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation
NEW QUESTION # 105
You are building a social media messaging app.
You need to identify in real time the language used in messages.
Which SDK package should you install?
Answer: D
Explanation:
The "azure-ai-translation-text" SDK is a client library for using the Azure AI Translator service, which includes the Language Detection API. This API allows you to determine the language of a given text string. The SDK simplifies integration of this API into applications, providing tools and libraries for various programming platforms like C#/.NET, Java, JavaScript, and Python.
Reference:
https://learn.microsoft.com/en-us/azure/ai-services/translator/text-translation/sdk-overview
NEW QUESTION # 106
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it as a result, these questions will not appear in the review screen.
You are building a chatbot that will use question answering in Azure Cognitive Service for Language.
You upload Doc1.pdf and train that contains a product catalogue and a price list.
During testing, users report that the chatbot responds correctly to the following question: What is the price of
<product>?
The chatbot fails to respond to the following question: How much does <product> cost?
You need to ensure that the chatbot responds correctly to both questions.
Solution: from Language Studio, you create an entity for price, and then retrain and republish the model.
Does this meet the goal?
Answer: B
Explanation:
Creating an entity for price is a feature of Conversational Language Understanding (CLU/LUIS), not Question Answering. Custom question answering does not use entities for extraction or intent resolution; it matches user utterances to Q/A pairs using alternates, synonyms, and ranking. Therefore, defining an "entity for price" would not make the chatbot correctly answer "How much does <product> cost?". The correct fix is to author additional alternate phrasings (as in Q266).
References
* Custom question answering capabilities (no entity modeling).https://learn.microsoft.com/azure/ai- services/language-service/question-answering/overview
* CLU entities vs. QnA authoring-separate features and workflows.https://learn.microsoft.com/azure/ai- services/language-service/conversational-language-understanding/overview
NEW QUESTION # 107
You are building content for a video training solution.
You need to create narration to accompany the video content. The solution must use Custom Neural Voice.
What should you use to create a custom neural voice, and which service should you use to generate the narration? To answer, select the appropriate options in the answer area.
NOTE: Each correct answer is worth one point.
Answer:
Explanation:
NEW QUESTION # 108
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