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Microsoft AI-900 certification exam is among those popular IT certifications. It is also the dream of ambitious IT professionals. This part of the candidates need to be fully prepared to allow them to get the highest score in the AI-900 Exam, make their own configuration files compatible with market demand.
The Microsoft AI-900 Exam covers a range of topics related to AI, including machine learning, natural language processing, computer vision, and cognitive services. It also tests the individual's ability to identify the appropriate Azure service to solve a particular business problem, and to understand the ethical and responsible use of AI.
>> AI-900 Valid Test Objectives <<
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The AI-900 exam covers various topics, including fundamental concepts of AI, machine learning, natural language processing, computer vision, and conversational AI. AI-900 exam also assesses a candidate's understanding of Azure AI services, such as Azure Cognitive Services, Azure Machine Learning, and Azure Bot Services. Passing AI-900 exam demonstrates that a candidate has a solid understanding of AI concepts and can effectively use Azure AI services to build intelligent applications. Additionally, the AI-900 exam serves as a prerequisite for more advanced Azure AI certifications, such as the AI-100 Designing and Implementing an Azure AI Solution.
The Microsoft AI-900 Exam is ideal for individuals who are looking to kickstart their career in the field of AI or for professionals who want to enhance their knowledge of AI and its applications in Azure. AI-900 exam is also beneficial for students who are interested in learning about AI and its potential applications. Passing the AI-900 exam will not only validate one's understanding of AI and Azure but will also showcase their commitment to staying up-to-date with the latest technologies and trends in the industry.
NEW QUESTION # 148
Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right.
Each machine learning type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Regression
In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Box 2: Classification
Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.
Box 3: Clustering
Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.
Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression
NEW QUESTION # 149
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 Translator service, part of Microsoft Azure Cognitive Services, is designed specifically for text translation between multiple languages. It is a cloud-based neural machine translation service that supports more than 100 languages. According to Microsoft Learn's module "Translate text with the Translator service", this service provides two main capabilities: text translation and automatic language detection.
* "You can use the Translator service to translate text between languages." # YesThis statement is true.
The primary purpose of the Translator service is to translate text accurately and efficiently between supported languages, such as English to Spanish or French to Japanese. It maintains contextual meaning using neural machine translation models.
* "You can use the Translator service to detect the language of a given text." # YesThis statement is also true. The Translator service includes automatic language detection, which determines the source language before translation. For instance, if a user submits text in an unknown language, the service can identify it automatically before performing translation.
* "You can use the Translator service to transcribe audible speech into text." # NoThis statement is false.
Transcribing speech (audio) into text is a function of the Azure Speech service, specifically the Speech- to-Text API, not the Translator service.
Therefore, the Translator service is used for text translation and language detection, while speech transcription belongs to the Speech service.
NEW QUESTION # 150
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:
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-tutorial-add-qna
NEW QUESTION # 151
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
NEW QUESTION # 152
What should you implement to prevent hateful responses from being returned by a generative Al solution?
Answer: C
NEW QUESTION # 153
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