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Microsoft AI-300 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Implement generative AI quality assurance and observability	10–15%	- Evaluate and test generative AI applications 1. Test for safety, accuracy, and relevance 2. Define evaluation metrics and criteria - Monitor generative AI systems 1. Implement logging and alerting 2. Track usage, performance, and errors
Topic 2: Optimize generative AI systems and model performance	15–20%	- Improve efficiency and cost-effectiveness 1. Optimize inference and deployment 2. Manage resource utilization - Optimize model selection and configuration 1. Tune prompts and generation settings 2. Choose appropriate models and parameters
Topic 3: Implement machine learning model lifecycle and operations	25–30%	- Register, version, and package models 1. Manage model registry 2. Create reusable model packages - Deploy models to production 1. Configure deployment options and scaling 2. Deploy to real-time and batch endpoints - Monitor and maintain models in production 1. Implement retraining and update workflows 2. Monitor data and model drift - Orchestrate model training and experimentation 1. Create and manage pipelines 2. Track experiments and metrics
Topic 4: Design and implement an MLOps infrastructure	15–20%	- Create and manage Machine Learning workspace resources and assets 1. Manage compute targets, datastores, and environments 2. Configure workspace settings and security - Implement infrastructure as code for Machine Learning 1. Automate infrastructure provisioning 2. Use Bicep or Azure CLI to deploy resources
Topic 5: Design and implement a GenAIOps infrastructure	20–25%	- Implement infrastructure for generative AI workloads 1. Design scalable and secure architecture 2. Integrate with Azure services and tools - Set up Microsoft Foundry environment 1. Manage compute and deployment resources 2. Configure projects, connections, and security

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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q60-Q65):

NEW QUESTION # 60
You have a Microsoft Foundry project.
You plan to use the Microsoft Foundry portal to fine-tune a base Azure OpenAI Service model that can accept both text and images as input.
You need to choose the suitable model.
Which model should you choose?

A. davinci-002
B. gpt-35-turbo
C. gpt-4
D. gpt-4o

Answer: D

Explanation:
GPT-4o is OpenAI ' s multimodal model that accepts both text and image inputs natively. The ' o ' in GPT-4o stands for ' omni, ' reflecting its ability to process multiple modalities in a single inference call. It is available in Microsoft Foundry for both inference and fine-tuning, and it supports vision inputs alongside text prompts, making it the correct choice for any fine-tuning scenario requiring multimodal input. Davinci-002 (option A) is a legacy text-completion model with no vision capabilities and limited fine-tuning support for modern chat formats. GPT-3.5-Turbo (option C) is a text-only chat model that does not accept image inputs. GPT-4 (option D) has a standard version that is text-only; the vision variant GPT-4V is distinct from GPT-4o and has more limited fine-tuning availability in Foundry. For multimodal text plus image fine-tuning, GPT-4o is the only correct choice.
Microsoft Learn Reference Topic: Azure OpenAI Service models available for fine-tuning - GPT-4o multimodal capabilities

NEW QUESTION # 61
You create an Azure Machine Learning workspace
You are developing a Python SDK v2 notebook to perform custom model training in the workspace. The notebook code imports all required packages.
You need to complete the Python SDK v2 code to include a training script. environment, and compute information.
How should you complete ten code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point

Answer:

Explanation:

Explanation:

NEW QUESTION # 62
You have an Azure Machine Learning workspace. You are running an experiment on your local computer.
You need to use MLflow Tracking to store metrics and artifacts from your local experiment runs in the workspace.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation:

NEW QUESTION # 63
You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupiter notebook in the workspace. The experiment must log string metrics.
You need to implement the method to log the string metrics.
Which method should you use?

A. mlflow.log-text0
B. mlflow.log. artifact0
C. mlflow.log-metric0
D. mlflow.log. dist0

Answer: A

NEW QUESTION # 64
A team is developing a generative AI assistant. The team is experimenting with multiple prompt variants to improve the user experience.
When comparing prompt variants, the team plans to assess whether the generated responses are grammatically correct.
You need to evaluate the quality of the language from the generated responses.
Which evaluator should you use?

A. Coherence
B. Grounded ness
C. Textual similarity
D. Fluency

Answer: D

Explanation:
The best evaluator within the Microsoft ecosystem for checking the grammatical correctness of generative AI responses--especially when testing multiple prompt variants--is the Azure AI Evaluator for Fluency, available within Azure AI Foundry.
Fluency Evaluator (builtin.fluency)
Purpose: Specifically designed to measure the effectiveness and clarity of written communication.
Grammatical Focus: It assesses grammatical accuracy, sentence structure, punctuation, and vocabulary usage in AI-generated text.
Result: It provides a 1-5 Likert scale score, allowing you to compare which prompt variants produce the most grammatically correct, natural-sounding responses.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/concept-model-monitoring- generative-ai-evaluation-metrics

NEW QUESTION # 65
......

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