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Microsoft AI-300 Exam Syllabus Topics:

Section	Objectives
Topic 1: Plan and design AI solutions using Azure AI services	- Requirements gathering and solution architecture 1. Identify business requirements for AI solutions 2. Select appropriate Azure AI services - Responsible AI design 1. Responsible AI mitigation strategies 2. Fairness, transparency, and accountability considerations
Topic 2: Implement secure and scalable AI systems	- Scalability and performance optimization 1. Cost optimization strategies 2. Autoscaling AI workloads - Security and governance 1. Data privacy and compliance considerations 2. Identity and access management for AI services
Topic 3: Design and implement generative AI solutions	- RAG (Retrieval Augmented Generation) solutions 1. Knowledge grounding and retrieval design 2. Vector search integration - Large language model integration 1. Prompt engineering and prompt flow design 2. Use Azure OpenAI Service capabilities
Topic 4: Operationalizing machine learning solutions	- Deployment and monitoring 1. Monitor performance and drift 2. Deploy models to endpoints - ML lifecycle management 1. Model versioning and registry usage 2. Model training and evaluation in Azure Machine Learning

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. You are preparing training data for a fine-tuning job in Microsoft Foundry.
Real production conversations cannot be used due to compliance requirements.
You need to generate synthetic interaction data that can be used for fine-tuning a generative model.
What should you do?

A) Use a simulator to generate prompt-response interaction data that matches the target task.
B) Enable A/B testing and capture live user traffic for data generation.
C) Export model evaluation logs and use them directly as training data.
D) Run a simulator to produce telemetry logs and trace data from user interactions.

2. You have a deployment of an Azure OpenAI Service base model.
You plan to fine-tune the model.
You need to prepare a file that contains training data.
Which file format should you use?

A) JSON
B) TSV
C) CSV
D) JSONL

3. Hotspot Question
You manage an Azure Machine Learning workspace named workspace1.
You must register an Azure Blob storage datastore in workspace1 by using an access key. You develop Python SDK v2 code to import all modules required to register the datastore.
You need to complete the Python SDK v2 code to define the datastore.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

4. A data science team plans to evaluate multiple hyperparameter values automatically while training a model in Azure Machine Learning.
The tuning process must run multiple training trials without manually modifying the training script for each run.
You need to automate hyperparameter tuning for the training job.
What should you do?

A) Adjust hyperparameters after model deployment.
B) Duplicate the training script for each parameter combination.
C) Create a tuning job that runs multiple trials with different parameter values.
D) Manually change hyperparameter values between training runs.

5. Drag and Drop Question
You have an existing GitHub repository containing Azure Machine Learning project files.
You need to clone the repository to your Azure Machine Learning shared workspace file system.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.