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Microsoft AI-300 Exam Syllabus Topics:

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

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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q41-Q46):

NEW QUESTION # 41
An Azure Machine Learning workspace contains multiple registered versions of a model that is used in production.
An older model version must no longer be deployable, but it must remain available for compliance review and potential rollback.
You need to change the state of the model version to meet the requirements.
What should you do?

A. Delete the model version.
B. Archive the model version.
C. Archive the training dataset for the model version.
D. Unregister the model version.

Answer: B

NEW QUESTION # 42
Hotspot Question
You have an Azure Machine Learning workspace.
You plan to use Azure Machine Learning Python SDK v2 to define a pipeline component that trains an image classification model. The execution logic of the component is contained in the train() function in the file named model_train.py.
You write code to import all required libraries and store it as train_component.py in the same folder that contains model_train.py.
You need to complete the remaining code in train_component.py.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: command_component
The @command_component decorator transforms a standard Python function into a reusable pipeline component within Azure ML SDK v2.from model_ Box 2: model_train model_train import train: Because model_train.py resides in the same directory as train_component.py, you import the file directly by its module name (model_train) to access its execution logic inside the component function.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-component-pipeline-python

NEW QUESTION # 43
Hotspot Question
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
The default datastore of workspace1 contains a folder named sample_data. The folder structure contains the following content:

You write Python SDK v2 code to materialize the data from the files in the sample_data folder into a Pandas data frame.
You need to complete the Python SDK v2 code to use the MLTable folder as the materialization blueprint.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point

Answer:

Explanation:

NEW QUESTION # 44
You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log a list of numerical metrics.
You need to implement a method to log a list of numerical metrics.
Which method should you use?

A. mlflow.log_metric()
B. mlflow.log_artifact()
C. mlflow.log_batch()
D. mlflow.log_image()

Answer: A

Explanation:
To log a list of numerical metrics using the Azure Machine Learning Python SDK v2, you should use the mlflow.log_metric() method within a loop, or mlflow.log_metrics() to log them simultaneously as a dictionary.
Reference:
https://learn.microsoft.com/en-us/answers/questions/1456554/downloading-azureml-experiment-metrics-logged-with

NEW QUESTION # 45
A team is working in Microsoft Foundry to test and compare large language model (LLM) prompt variants in a development environment.
The team requires consistent inputs to evaluate prompt variants without relying on live user traffic.
You need to create a controlled evaluation of input data.
Which action should you perform first?

A. Apply a blocklist.
B. Generate synthetic interaction data.
C. Configure content filters.
D. Enable observability metrics.

Answer: B

NEW QUESTION # 46
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