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microsoft learning siteAI-102 Skills measuredPublish a Machine Learning Experiment with Microsoft Azure Machine Learning StudioProcess and translate speech with Azure Cognitive Speech Services
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If you are looking to demonstrate your skills in designing and implementing Microsoft Azure AI solutions, then the Microsoft AI-102: Designing and Implementing a Microsoft Azure AI Solution exam is the right choice for you. AI-102 exam covers all the essential topics related to designing and implementing AI solutions using Azure services and tools. Passing AI-102 exam will help you earn the Microsoft Certified: Azure AI Engineer Associate certification, which is highly valued by employers and recognized globally.
To pass the Microsoft AI-102 Exam, candidates must have a deep understanding of AI fundamentals, including machine learning models, data preparation, feature engineering, and model selection. They must also have practical experience in designing and implementing AI solutions in Azure, including deploying models, monitoring performance, and troubleshooting issues.
NEW QUESTION # 250
You have an Azure subscription.
You plan to build an app that will use the Azure OpenAI DALL-E model.
You need to deploy the model.
What should you use?
Answer: D
Explanation:
To deploy the Azure OpenAI DALL-E model, the most appropriate tools to use are Azure AI Studio for model management and interaction, and the Azure Command-Line Interface (CLI) for deployment and configuration tasks. Azure AI Studio provides an interactive interface for working with Azure AI models, and the Azure CLI allows you to manage resources, deployments, and other Azure services from the command line.
NEW QUESTION # 251
You have an Azure Cognitive Search solution and a collection of blog posts that include a category field. You need to index the posts. The solution must meet the following requirements:
* Include the category field in the search results.
* Ensure that users can search for words in the category field.
* Ensure that users can perform drill down filtering based on category.
Which index attributes should you configure for the category field?
Answer: C
Explanation:
For the category field in Azure Cognitive Search:
searchable # allows users to search by words in the category field.
facetable # enables drill-down filtering (facets).
retrievable # ensures the field appears in search results.
filterable/sortable/key are not required here based on the scenario.
NEW QUESTION # 252
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
NEW QUESTION # 253
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 have an Azure Cognitive Search service.
During the past 12 months, query volume steadily increased.
You discover that some search query requests to the Cognitive Search service are being throttled.
You need to reduce the likelihood that search query requests are throttled.
Solution: You migrate to a Cognitive Search service that uses a higher tier.
Does this meet the goal?
Answer: B
Explanation:
Explanation
A simple fix to most throttling issues is to throw more resources at the search service (typically replicas for query-based throttling, or partitions for indexing-based throttling). However, increasing replicas or partitions adds cost, which is why it is important to know the reason why throttling is occurring at all.
Reference:
https://docs.microsoft.com/en-us/azure/search/search-performance-analysis
NEW QUESTION # 254
You are building a chatbot that will use question answering in Azure Cognitive Service for Language.
You have a PDF named Docl.pdf that contains a product catalogue and a price list You upload Docl.pdf and train the model.
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 cost, and then retrain and republish the model.
Does this meet the goal?
Answer: A
Explanation:
In Azure Cognitive Service for Language (Question Answering), when users ask natural language questions, the service tries to match them to knowledge base content and synonyms.
* In the scenario, the model correctly answers "What is the price of <product>?" but fails on "How much does <product> cost?".
* This indicates that the service does not automatically recognize "price" and "cost" as equivalent terms.
* Creating an entity for cost will not fix this issue. Entities in custom text classification or conversational language understanding are used for extracting structured data (like dates, names, or locations) from text, not for synonym matching in question answering.
The correct solution is to add alternate phrasing (synonyms/alternate questions) in the QnA project for each knowledge base entry. For example, under the QnA pair that contains "What is the price of <product>?", you would add "How much does <product> cost?" as an alternate question. This way, both variations trigger the same answer.
Therefore, the proposed solution (create an entity for cost) does not meet the goal.
The answer: B. No
* Add alternative questions to improve matching - Azure AI Language Question Answering
* Improve accuracy with synonyms and alternate phrasing
* Overview of Question Answering in Azure AI Language
NEW QUESTION # 255
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