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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Speech - Describe capabilities of Azure Translator - Identify types of NLP solutions - Describe capabilities of Azure Language |
| Topic 2: Features of computer vision workloads on Azure | 15–20% | - Identify types of computer vision solutions - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Face - Describe capabilities of Azure Form Recognizer |
| Topic 3: Features of generative AI workloads on Azure | 20–25% | - Describe responsible AI practices for generative AI - Describe use cases for generative AI - Describe capabilities of Azure OpenAI Service - Describe generative AI concepts |
| Topic 4: Fundamental principles of machine learning on Azure | 15–20% | - Describe machine learning pipelines - Describe core concepts of machine learning - Describe capabilities of Azure Machine Learning - Describe automated machine learning |
| Topic 5: Artificial Intelligence workloads and considerations | 15–20% | - Identify types of AI workloads - Describe responsible AI principles - Describe considerations for developing AI solutions |
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NEW QUESTION # 232
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn documentation on GitHub Copilot, the GitHub Copilot extension for Microsoft Visual Studio Code (VS Code) is powered by the OpenAI Codex model, a specialized descendant of the GPT (Generative Pre-trained Transformer) family of models. The OpenAI Codex model is specifically fine-tuned for programming tasks- it understands and generates code in multiple programming languages such as Python, JavaScript, C#, and more.
GitHub Copilot functions as an AI-powered coding assistant that suggests complete lines or blocks of code, helps write comments, and can even generate functions based on natural language prompts entered by the developer. When a user types a comment like "// sort a list of numbers in ascending order," Copilot uses the Codex model to understand the intent and generate an appropriate code implementation.
The integration with Visual Studio Code allows developers to work efficiently without needing to switch between documentation and their coding environment. GitHub Copilot leverages context-aware suggestions, meaning it understands the surrounding code, function definitions, and variable names to provide relevant completions.
The other options are incorrect because:
* GitHub source control manages code repositories but doesn't use AI models.
* IntelliSense is a built-in VS Code feature for code completion but doesn't rely on OpenAI models.
* Microsoft 365 Copilot uses large language models (like GPT-4) for productivity tools such as Word, Excel, and Outlook-not for code generation.
Therefore, the correct choice that uses the OpenAI Codex model in Visual Studio Code is GitHub Copilot, aligning with Microsoft's AI-900 learning objectives under "Identify common use cases for Azure OpenAI and GitHub Copilot."
NEW QUESTION # 233
You have an Azure Machine Learning model that predicts product quality. The model has a training dataset that contains 50,000 records. A sample of the data is shown in the following table.
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:
This question tests the understanding of features and labels in machine learning, a core concept covered in the Microsoft Azure AI Fundamentals (AI-900) syllabus under "Describe fundamental principles of machine learning on Azure." In supervised machine learning, data is divided into features (inputs) and labels (outputs).
* Features are the independent variables - measurable properties or characteristics used by the model to make predictions.
* Labels are the dependent variables - the target outcome the model is trained to predict.
From the provided dataset, the goal of the Azure Machine Learning model is to predict product quality (Pass or Fail). Therefore:
* Mass (kg) is a feature - Yes"Mass (kg)" represents an input variable used by the model to learn patterns that influence product quality. It helps the algorithm understand how variations in mass might correlate with passing or failing the quality test. Thus, it is correctly classified as a feature.
* Quality Test is a label - YesThe "Quality Test" column indicates the outcome of the manufacturing process, marked as either Pass or Fail. This is the target the model tries to predict during training. In Azure ML terminology, this column is the label, as it represents the dependent variable.
* Temperature (C) is a label - No"Temperature (C)" is an input that helps the model determine quality outcomes, not the outcome itself. It influences the quality result but is not the value being predicted.
Therefore, temperature is another feature, not a label.
In conclusion, per Microsoft Learn and AI-900 study materials, features are measurable inputs (like mass and temperature), while the label is the target output (like the quality test result).
NEW QUESTION # 234
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:
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.
NEW QUESTION # 235
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:
NEW QUESTION # 236
What ate two common use cases for generative Al solutions? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
NEW QUESTION # 237
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