] AI-300 Training Online: Operationalizing Machine Learning and Generative AI Solutions & Operationalizing Machine Learning and Generative AI Solutions Dumps Torrent

Microsoft Operationalizing Machine Learning and Generative AI Solutions : AI-300

  • Exam Code: AI-300
  • Exam Name: Operationalizing Machine Learning and Generative AI Solutions
  • Updated: Jun 03, 2026
  • Q & A: 77 Questions and Answers

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Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. A company requires that only models meeting predefined performance thresholds are registered and deployed. The solution must be fully automated within the ML workflow. What should you implement?

A) GitHub pull request checks
B) Azure Monitor alerts
C) Conditional logic in pipeline
D) Manual approval gate


2. You are fine-tuning a base language model to analyze customer feedback.
You label examples of support tickets. You must improve classification accuracy by configuring and fine-tuning the base model in Microsoft Foundry.
You need to configure and run fine-tuning.
What should you do first?

A) Deploy the base model to an online endpoint before starting fine-tuning.
B) Use prompt flow to generate multiple prompt templates for evaluation.
C) Format the dataset as a JSONL file with prompt-completion pairs and upload the file.
D) Enable tracing for all inference calls in the evaluation pipeline.


3. During training, pipelines occasionally fail due to schema mismatch caused by upstream data changes. You need a robust and automated solution that prevents invalid data from reaching training steps. What is the BEST approach?

A) Ignore schema differences
B) Use larger compute
C) Retrain manually when failure occurs
D) Add a data validation component in pipeline


4. 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-4
C) gpt-4o
D) gpt-35-turbo


5. Drag and Drop Question
A team deploys a generative AI application that uses a model deployed in Microsoft Foundry. The application must support latency monitoring under production load.
You need to enable performance observability.
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.


Solutions:

Question # 1
Answer: C
Question # 2
Answer: D
Question # 3
Answer: D
Question # 4
Answer: C
Question # 5
Answer: Only visible for members

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