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Microsoft AI-900, also known as Microsoft Azure AI Fundamentals, is a certification exam that tests the foundational knowledge of candidates in the field of artificial intelligence (AI) and machine learning (ML). Microsoft Azure AI Fundamentals certification exam is designed for individuals who are interested in learning about AI and ML concepts and how they can be implemented in Microsoft Azure. AI-900 Exam covers a wide range of topics related to AI, including cognitive services, natural language processing (NLP), computer vision, and conversational AI.
NEW QUESTION # 130
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:
NEW QUESTION # 131
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:
Full Detailed Explanation (250-300 words):
* "You can fine-tune some Azure OpenAI models by using your own data." - YESThis statement is true.
Azure OpenAI allows customers to fine-tune certain models like GPT-3, GPT-3.5, and some embedding models with their own data. Fine-tuning customizes a model to perform better on specific tasks or match a company's domain terminology, tone, or context. According to Microsoft Learn's AI-
900 and Azure OpenAI documentation, fine-tuning is supported for approved use cases while maintaining Microsoft's Responsible AI oversight and compliance process.
* "Pretrained generative AI models are a component of Azure OpenAI." - YESThis statement is also true. Azure OpenAI provides access to pretrained large language and generative AI models such as GPT-3.5, GPT-4, Codex, and DALL E. These models are pretrained on vast datasets and made available via APIs, allowing developers to generate text, code, and images without needing to train their own models. This is a core feature of Azure OpenAI's service offering.
* "To build a solution that complies with Microsoft responsible AI principles, you must build and train your own model." - NOThis statement is false. Compliance with Microsoft Responsible AI principles (Fairness, Reliability & Safety, Privacy & Security, Inclusiveness, Transparency, Accountability) does not require building custom models. Prebuilt Azure AI and OpenAI services already align with Responsible AI standards. Developers simply need to use these services responsibly, applying governance and ethical design practices.
NEW QUESTION # 132
You need to provide content for a business chatbot that will help answer simple user queries.
What are three ways to create question and answer text by using QnA Maker? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,B,E
Explanation:
Automatic extraction
Extract question-answer pairs from semi-structured content, including FAQ pages, support websites, excel files, SharePoint documents, product manuals and policies.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/content-types
NEW QUESTION # 133
You are developing a model to predict events by using classification.
You have a confusion matrix for the model scored on test data as shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: 11
TP = True Positive.
The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative. The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively. Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN).
Box 2: 1,033
FN = False Negative
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance Finding TP is easy. It basically means the value where Predicted and True value is 1 and that is 11 in this case.
False Negative means where true value was 1 but predicted value was 0 and that is 1033 in this case The confusion matrix shows cases where both the predicted and actual values were 1 (known as true positives) at the top left, and cases where both the predicted and the actual values were 0 (true negatives) at the bottom right. The other cells show cases where the predicted and actual values differ (false positives and false negatives).
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer
/evaluate-model
NEW QUESTION # 134
In which two scenarios can you use the Form Recognizer service? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,C
Explanation:
Reference:
https://azure.microsoft.com/en-gb/services/cognitive-services/form-recognizer/#features
NEW QUESTION # 135
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