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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Describe responsible AI principles - Identify types of AI workloads |
| Topic 2: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Form Recognizer - Identify types of computer vision solutions - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Face - Describe capabilities of Azure Computer Vision |
| Topic 3: Features of generative AI workloads on Azure | 20–25% | - Describe generative AI concepts - Describe capabilities of Azure OpenAI Service - Describe responsible AI practices for generative AI - Describe use cases for generative AI |
| Topic 4: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Translator - Describe capabilities of Azure Language - Identify types of NLP solutions - Describe capabilities of Azure Speech |
| Topic 5: Fundamental principles of machine learning on Azure | 15–20% | - Describe core concepts of machine learning - Describe capabilities of Azure Machine Learning - Describe automated machine learning - Describe machine learning pipelines |
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NEW QUESTION # 334
You use Azure Machine Learning designer to publish an inference pipeline.
Which two parameters should you use to consume the pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,C
Explanation:
https://docs.microsoft.com/en-in/learn/modules/create-regression-model-azure-machine-learning-designer/deploy-service
NEW QUESTION # 335
You need to predict the sea level in meters for the next 10 years.
Which type of machine learning should you use?
Answer: B
Explanation:
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.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression
NEW QUESTION # 336
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:
Box 1: No
Box 2: Yes
Box 3: Yes
Anomaly detection encompasses many important tasks in machine learning:
Identifying transactions that are potentially fraudulent.
Learning patterns that indicate that a network intrusion has occurred.
Finding abnormal clusters of patients.
Checking values entered into a system.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/anomaly-detection
NEW QUESTION # 337
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
NEW QUESTION # 338
Which OpenAI model does GitHub Copilot use to make suggestions for client-side JavaScript?
Answer: D
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) learning path and Microsoft Learn documentation on GitHub Copilot, GitHub Copilot is powered by OpenAI Codex, a specialized language model derived from the GPT-3 family but fine-tuned specifically on programming languages and code data.
OpenAI Codex was designed to translate natural language prompts into executable code in multiple programming languages, including JavaScript, Python, C#, TypeScript, and Go. It can understand comments, function names, and code structure to generate relevant code suggestions in real time.
When a developer writes client-side JavaScript, GitHub Copilot uses Codex to analyze the context of the file and generate intelligent suggestions, such as completing functions, writing boilerplate code, or suggesting improvements. Codex can also explain what specific code does and provide inline documentation, which enhances developer productivity.
* Option A (GPT-4): While some newer versions of GitHub Copilot (Copilot X) may integrate GPT-4 for conversational explanations, the core code completion engine remains based on Codex, as per the AI-
900-level content.
* Option C (DALL-E): Used for image generation, not for programming tasks.
* Option D (GPT-3): Codex was fine-tuned from GPT-3 but has been further trained specifically for code generation tasks.
Therefore, the verified and official answer from Microsoft's AI-900 curriculum is B. Codex - the OpenAI model used by GitHub Copilot to make suggestions for client-side JavaScript and other programming languages.
NEW QUESTION # 339
......
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