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Microsoft AI-300 dumps - in .pdf

AI-300 pdf
  • Exam Code: AI-300
  • Exam Name: Operationalizing Machine Learning and Generative AI Solutions
  • Updated: Jul 31, 2026
  • Q & A: 159 Questions and Answers
  • Convenient, easy to study.
    Printable Microsoft AI-300 PDF Format. It is an electronic file format regardless of the operating system platform.
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  • Exam Code: AI-300
  • Exam Name: Operationalizing Machine Learning and Generative AI Solutions
  • Updated: Jul 31, 2026
  • Q & A: 159 Questions and Answers
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  • Exam Code: AI-300
  • Exam Name: Operationalizing Machine Learning and Generative AI Solutions
  • Updated: Jul 31, 2026
  • Q & A: 159 Questions and Answers
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Microsoft AI-300 Exam Syllabus Topics:

SectionObjectives
Implement machine learning model lifecycle and operations- Deploy models to real-time and batch endpoints
- Retrain, update, and manage model versions in production
- Train, register, and version models using Azure Machine Learning
- Monitor model performance, data drift, and operational health
Design and implement an MLOps infrastructure- Implement security, governance, and compliance for MLOps
- Configure source control, CI/CD pipelines, and automation for ML workflows
- Manage environments, data stores, and model registries
- Set up Azure Machine Learning workspace and compute targets
Implement generative AI quality assurance and observability- Conduct red teaming, adversarial testing, and content filtering
- Evaluate generative AI outputs for quality, safety, and grounding
- Monitor latency, token usage, cost, and error rates
- Implement logging, tracing, and telemetry for GenAI applications
Optimize generative AI systems and model performance- Implement cost management and scaling strategies for GenAI workloads
- Optimize inference performance, caching, and throughput
- Tune prompts, system messages, and grounding strategies
- Fine-tune and distill models for specific use cases
Design and implement a GenAIOps infrastructure- Manage API keys, rate limits, and responsible AI guardrails
- Configure prompt orchestration, prompt flows, and agent frameworks
- Set up Microsoft Foundry and Azure AI services for generative AI workloads
- Implement RAG (Retrieval-Augmented Generation) pipelines and vector search

Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

1. You deploy a new model version to a managed online endpoint. You must test it with 10% traffic and automatically roll back if latency or error rate increases beyond threshold. What should you configure?

A) Traffic splitting with monitoring alerts
B) Batch endpoint validation
C) Manual testing workflow
D) Separate endpoint for testing


2. Hotspot Question
A biomedical research company plans to enroll people in an experimental medical treatment trial.
You create and train a binary classification model to support selection and admission of patients to the trial. The model includes the following features: Age, Gender, and Ethnicity.
The model returns different performance metrics for people from different ethnic groups.
You need to use Fairlearn to mitigate and minimize disparities for each category in the Ethnicity feature.
Which technique and constraint should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


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


4. Hotspot Question
A team is preparing a generative AI application for production deployment. The application generates structured responses that must be evaluated for quality before each release.
The organization requires repeatable evaluation results that can be compared across builds and environments.
You need to configure evaluation inputs so quality metrics can be reliably calculated across test runs.
How should you prepare the evaluation inputs? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


5. A data science team trains a classification model that predicts loan approval outcomes.
Before registering the model, the team must ensure the following:
- Predictions must not disproportionately impact protected groups.
- Prediction errors can be evaluated across different data segments.
You need to assess whether the model meets Responsible AI expectations.
Which two approaches should you use? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.

A) Validate inference schema compatibility.
B) Analyze error rates across the global cohort.
C) Evaluate feature importance for prediction transparency.
D) Analyze error rates across defined demographic cohorts.
E) Measure endpoint latency under load.


Solutions:

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

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