Operationalizing Machine Learning and Generative AI Solutions (AI-300) Practice Test
Gana confianza para Operationalizing Machine Learning and Generative AI Solutions (AI-300). Practica los conceptos, comprende las respuestas y refuerza tus conocimientos pregunta a pregunta.
Probar una pregunta123
Preguntas de práctica
2 horas 3 minutos
Tiempo de Práctica
El listón a superar
700/1000
Puntuación mínima publicada para obtener esta certificación.
Objetivos oficiales de MicrosoftMicrosoft123 preguntas de práctica122 respuestas con una referencia verificable
Descripción y detalles del examen
El examen de certificación Operationalizing Machine Learning and Generative AI Solutions (AI-300) valida las habilidades avanzadas necesarias para diseñar, implementar y gestionar sistemas de IA de calidad de producción a escala empresarial. Esta credencial está diseñada para ingenieros de IA, profesionales de MLOps y arquitectos de la nube responsables de trasladar cargas de trabajo de aprendizaje automático e IA generativa de la experimentación a entornos de producción confiables, gobernados y observables. Los candidatos son evaluados en su capacidad para arquitectar infraestructura MLOps de extremo a extremo, implementar la gestión del ciclo de vida completo del modelo y construir pipelines de GenAIOps que soporten la integración continua, entrega y monitoreo de aplicaciones de modelos de lenguaje grande.
Temario 1.0
Preguntas de Muestra
Elige una respuesta y consulta la explicación para ver cómo funciona la práctica.
122 de 123 respuestas incluyen una referencia verificable.
Design and implement an MLOps infrastructure
You need to create a new pipeline in Azure Data Factory. Which steps should you take in Data Factory Studio?
Design and implement a GenAIOps infrastructure
A company is evaluating Microsoft Foundry and wants to understand how it is monetized. Which of the following best describes the pricing model?
Design and implement an MLOps infrastructure
An organization wants to adopt a modular approach for its machine learning workflows. Which outcome is a direct benefit of this approach?
Design and implement an MLOps infrastructure
When creating an endpoint with a system-assigned managed identity, what permissions are required for accessing the workspace storage account and container registry?
Implement machine learning model lifecycle and operations
A team of data scientists wants to build many machine learning models quickly without sacrificing quality. Which approach should they use?
Qué temas cubre este examen
Usa las ponderaciones publicadas de los dominios para planificar tu estudio. Los resultados de práctica no predicen tu puntuación en el examen de certificación.
01Implement machine learning model lifecycle and operations
27-28%
Temas
- Configure experiment tracking with MLflow
- Use automated machine learning to explore optimal models
- Use notebooks for experimentation and exploration
- Automate hyperparameter tuning
- Run model training scripts
- Manage distributed training for large and deep learning models
- Implement training pipelines
- Compare model performance across jobs
- Package a feature retrieval specification with the model artifact
- Register an MLflow model
- Evaluate a model by using responsible AI principles
- Manage model lifecycle, including archiving models
- Deploy models as real-time or batch endpoints with managed inference options
- Test and troubleshoot model endpoints
- Implement progressive rollout and safe rollback strategies
- Detect and analyze data drift
- Monitor performance metrics of models deployed to production
- Configure retraining or alert triggers when thresholds are exceeded
Objetivos de aprendizaje
- Configure experiment tracking with MLflow
- Use automated machine learning to explore optimal models
- Use notebooks for experimentation and exploration
- Automate hyperparameter tuning
- Run model training scripts
- Manage distributed training for large and deep learning models
- Implement training pipelines
- Compare model performance across jobs
- Package a feature retrieval specification with the model artifact
- Register an MLflow model
- Evaluate a model by using responsible AI principles
- Manage model lifecycle, including archiving models
- Deploy models as real-time or batch endpoints with managed inference options
- Test and troubleshoot model endpoints
- Implement progressive rollout and safe rollback strategies
- Detect and analyze data drift
- Monitor performance metrics of models deployed to production
- Configure retraining or alert triggers when thresholds are exceeded
02Design and implement a GenAIOps infrastructure
22-23%
Temas
- Create and configure Foundry resources and project environments
- Configure identity and access management with managed identities and role-based access control (RBAC)
- Implement network security and private networking configurations
- Deploy infrastructure using Bicep templates and Azure CLI
- Deploy foundation models by using serverless API endpoints and managed compute options
- Select appropriate models for specific use cases
- Implement model versioning and production deployment strategies
- Configure provisioned throughput units for high-volume workloads
- Design and develop prompts
- Create prompt variants and compare performance across different prompts
- Implement version control for prompts by using Git repositories
Objetivos de aprendizaje
- Create and configure Foundry resources and project environments
- Configure identity and access management with managed identities and role-based access control (RBAC)
- Implement network security and private networking configurations
- Deploy infrastructure using Bicep templates and Azure CLI
- Deploy foundation models by using serverless API endpoints and managed compute options
- Select appropriate models for specific use cases
- Implement model versioning and production deployment strategies
- Configure provisioned throughput units for high-volume workloads
- Design and develop prompts
- Create prompt variants and compare performance across different prompts
- Implement version control for prompts by using Git repositories
03Design and implement an MLOps infrastructure
17-18%
Temas
- Create and manage a workspace
- Create and manage datastores
- Create and manage compute targets
- Configure identity and access management for workspaces
- Create and manage data assets
- Create and manage environments
- Create and manage components
- Share assets across workspaces by using registries
- Configure GitHub integration with Machine Learning to enable secure access
- Deploy Machine Learning workspaces and resources by using Bicep and Azure CLI
- Automate resource provisioning by using GitHub Actions workflows
- Restrict network access to Machine Learning workspaces
- Manage source control for machine learning projects by using Git
Objetivos de aprendizaje
- Create and manage a workspace
- Create and manage datastores
- Create and manage compute targets
- Configure identity and access management for workspaces
- Create and manage data assets
- Create and manage environments
- Create and manage components
- Share assets across workspaces by using registries
- Configure GitHub integration with Machine Learning to enable secure access
- Deploy Machine Learning workspaces and resources by using Bicep and Azure CLI
- Automate resource provisioning by using GitHub Actions workflows
- Restrict network access to Machine Learning workspaces
- Manage source control for machine learning projects by using Git
04Implement generative AI quality assurance and observability
12-13%
Temas
- Create test datasets and data mapping for comprehensive model evaluation
- Implement AI quality metrics, including groundedness, relevance, coherence, and fluency
- Configure risk and safety evaluations for harmful content detection
- Set up automated evaluation workflows by using built-in and custom evaluation metrics
- Examine continuous monitoring in Foundry
- Monitor performance metrics, including latency, throughput, and response times
- Track and optimize cost metrics, including token consumption and resource usage
- Configure detailed logging, tracing, and debugging capabilities for production troubleshooting
Objetivos de aprendizaje
- Create test datasets and data mapping for comprehensive model evaluation
- Implement AI quality metrics, including groundedness, relevance, coherence, and fluency
- Configure risk and safety evaluations for harmful content detection
- Set up automated evaluation workflows by using built-in and custom evaluation metrics
- Examine continuous monitoring in Foundry
- Monitor performance metrics, including latency, throughput, and response times
- Track and optimize cost metrics, including token consumption and resource usage
- Configure detailed logging, tracing, and debugging capabilities for production troubleshooting
05Optimize generative AI systems and model performance
12-13%
Temas
- Optimize retrieval performance by tuning similarity thresholds, chunk sizes, and retrieval strategies
- Select and fine-tune embedding models for domain-specific use cases and accuracy improvements
- Implement and optimize hybrid search approaches combining semantic and keyword-based retrieval
- Evaluate and improve RAG system performance by using relevance metrics and A/B testing frameworks
- Design and implement advanced fine-tuning methods
- Create and manage synthetic data for fine-tuning
- Monitor and optimize fine-tuned model performance
- Manage a fine-tuned model from development through production deployment
- Last updated on 03/05/2026
- Purpose of this document
- About the exam
- Skills measured
- Study resources
- High contrast
- AI Disclaimer
- Previous Versions
- Contribute
- Privacy
- Consumer Health Privacy
- Terms of Use
- Trademarks
- © Microsoft 2026
Objetivos de aprendizaje
- Optimize retrieval performance by tuning similarity thresholds, chunk sizes, and retrieval strategies
- Select and fine-tune embedding models for domain-specific use cases and accuracy improvements
- Implement and optimize hybrid search approaches combining semantic and keyword-based retrieval
- Evaluate and improve RAG system performance by using relevance metrics and A/B testing frameworks
- Design and implement advanced fine-tuning methods
- Create and manage synthetic data for fine-tuning
- Monitor and optimize fine-tuned model performance
- Manage a fine-tuned model from development through production deployment
- Last updated on 03/05/2026
- Purpose of this document
- About the exam
- Skills measured
- Study resources
- High contrast
- AI Disclaimer
- Previous Versions
- Contribute
- Privacy
- Consumer Health Privacy
- Terms of Use
- Trademarks
- © Microsoft 2026
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