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Microsoft AI-300 Exam Syllabus Topics:

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

Question 1

Drag and Drop Question
A team maintains Infrastructure as Code (IaC) templates to provision Azure Machine Learning resources.
Provisioning must be triggered by changes in the templates and executed without manual intervention.
You need to automate resource provisioning.
Which action should you take for each requirement? To answer, move the appropriate actions to the correct requirements. You may use each action once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Question 2

An organization is deploying several generative AI workloads by using Microsoft Foundry. Each workload must meet different requirements related to data governance, task specialization, and operational cost control.
The organization requires models that meet the following requirements:
- Model behavior aligns with the task being performed.
- Data handling aligns with internal governance policies.
- Operational complexity and cost are justified by workload needs.
You need to select the foundation model options that meet the requirements.
Which three models can you select? Each correct answer presents a complete solution. Choose three.
NOTE: Each correct selection is worth one point.

A. A model that offers enterprise governance controls when workloads process regulated business data
B. The smallest available model to minimize the usage cost
C. A model that is optimized for conversational reasoning when deploying an interactive assistant
D. The largest available model to simplify operational management
E. A model that supports multiple input types when workloads require combined text and image analysis

Question 3

A team deploys a model to a real-time endpoint in Azure Machine Learning. You deploy some updates to the endpoint.
The endpoint returns errors after the new deployment is released.
You need to restore the service as quickly as possible.
What should you do first?

A. Increase the compute size.
B. Delete the endpoint and immediately redeploy it.
C. Change the authentication type to Azure Machine Learning token-based authentication.
D. Roll back traffic to the previous deployment.

Question 4

Drag and Drop Question
You are fine-tuning an LLM base model by using Microsoft Foundry. You have a labeled dataset of customer emails.
You need to improve task-specific prediction accuracy so that the model can be tested and deployed later.
Which three 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.

Question 5

You are a data scientist working for a hotel booking website company. You use the Azure Machine Learning service to train a model that identifies fraudulent transactions.
You must deploy the model to an Azure Machine Learning online endpoint by using the Azure Machine Learning Python SDK v2. The deployed model must return real-time predictions of fraud based on transaction data input.
You need to create the script that is specified as the scoring_script parameter for the CodeConfiguration class used to deploy the model.
What should the entry script do?

A. Create a Conda environment for the online endpoint compute and install the necessary Python packages.
B. Register the model with appropriate tags and properties.
C. Specify the number of cores and the amount of memory required for the online endpoint compute.
D. Load the model and use it to predict labels from input data.
E. Start a node on the inference cluster where the model is deployed.

Solutions: