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Free AI-300 – Microsoft AI-300 Practice Test Question

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Showing 1–3 of 9 questions

Question 1 (Topic 6)

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.

Select an option, then click Submit answer.

  • ○ A model that is optimized for conversational reasoning when deploying an interactive assistant
  • ○ The largest available model to simplify operational management
  • ○ The smallest available model to minimize the usage cost
  • ○ A model that supports multiple input types when workloads require combined text and image analysis
  • ○ A model that offers enterprise governance controls when workloads process regulated business data
Question 2 (Topic 6)

HOTSPOT - You are reviewing a dataset that will be used for an advanced fine-tuning job in Microsoft Foundry. The fine-tuning job uses preference comparison data. You review the following dataset excerpt. 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 is in the explanation below.

Question 3 (Topic 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?

Select an option, then click Submit answer.

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