Developing AI Cloud Solutions on Azure (AI-200) Practice Test
Gana confianza para Developing AI Cloud Solutions on Azure (AI-200). Practica los conceptos, comprende las respuestas y refuerza tus conocimientos pregunta a pregunta.
Probar una preguntaDescripción y detalles del examen
El examen AI-200: Developing AI Cloud Solutions on Azure valida las competencias técnicas avanzadas necesarias para diseñar, construir y gestionar aplicaciones inteligentes dentro del ecosistema de Microsoft Azure. Esta exhaustiva evaluación mide la competencia de un candidato a lo largo del ciclo completo de desarrollo de soluciones de IA, desde la contenedorización y gestión de datos hasta la integración de servicios y seguridad. Los candidatos son evaluados en su capacidad para desarrollar soluciones contenedorizadas utilizando Azure Kubernetes Service y Azure Container Apps, implementar estrategias robustas de gestión de datos con Azure Cosmos DB y Azure SQL, y conectarse sin problemas a Azure Cognitive Services y otros servicios nativos de la nube.
Preguntas de Muestra
Elige una respuesta y consulta la explicación para ver cómo funciona la práctica.
A developer is creating a new Azure Storage account and chooses to use Azure DNS zone endpoints (preview). What happens when the account is created?
A cloud architect is designing a storage solution for an Azure SQL Database. The database requires fast random read-write operations. Which Azure Blob storage type should the architect use?
A developer wants to use Azure Cosmos DB for a small production workload without incurring costs for the first 1000 RU/s and 25 GB of storage. What must they do?
A candidate with a visual impairment uses a screen reader and needs additional time to complete the exam. What should the candidate do to ensure these needs are met?
A data engineering team is evaluating Azure Data Lake Storage for a new analytics platform. What is the primary purpose of this service?
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.
01Develop AI solutions by using Azure data management services
Covers connecting to Azure Cosmos DB for NoSQL via SDK, running queries, optimizing performance, request units consumption, indexing policies, consistency levels, and storing and retrieving embeddings for vector similarity search.
Temas
- Connect to Azure Cosmos DB for NoSQL by using the SDK and run queries
- Optimize query performance and Request Units (RUs) consumption by using indexing policies and consistency levels
- Store and retrieve embeddings and execute vector similarity search for semantic retrieval
- Implement a change feed processor to detect and handle new or updated items
- Connect and query Azure Database for PostgreSQL by using SDKs
- Model schemas and implement indexing strategies, including designing tables and choosing appropriate data types
- Implement indexing strategies, including optimizing query latency and reducing pgvector compute overhead
- Configure compute, memory, and storage resources to support vector workloads
- Run vector similarity search, including storing embeddings, semantic retrieval, and implementing retrieval-augmented generation (RAG) patterns by using metadata filter
- Implement connection optimization to improve throughput and minimize latency
- Implement Azure Managed Redis data operations, including caching, expiration, and invalidation
- Implement vector indexing to enable similarity search
Objetivos de aprendizaje
- Connect to Azure Cosmos DB for NoSQL by using the SDK and run queries
- Optimize query performance and Request Units (RUs) consumption by using indexing policies and consistency levels
- Store and retrieve embeddings and execute vector similarity search for semantic retrieval
- Implement a change feed processor to detect and handle new or updated items
- Connect and query Azure Database for PostgreSQL by using SDKs
- Model schemas and implement indexing strategies, including designing tables and choosing appropriate data types
- Implement indexing strategies, including optimizing query latency and reducing pgvector compute overhead
- Configure compute, memory, and storage resources to support vector workloads
- Run vector similarity search, including storing embeddings, semantic retrieval, and implementing retrieval-augmented generation (RAG) patterns by using metadata filter
- Implement connection optimization to improve throughput and minimize latency
- Implement Azure Managed Redis data operations, including caching, expiration, and invalidation
- Implement vector indexing to enable similarity search
02Connect to and consume Azure services
Involves queuing and processing back-end operations with Azure Service Bus, handling dead-letter queues, messages, topics, subscriptions, event-driven workflows with Azure Event Grid, and building serverless APIs with functions.
Temas
- Queue and process back-end operations by using Azure Service Bus, including dead-letter queue handling, messages, topics, and subscriptions
- Implement event-driven workflows by using Azure Event Grid, including filters, custom events, and retries
- Build serverless APIs, including implementing triggers and bindings
- Configure and deploy function apps
Objetivos de aprendizaje
- Queue and process back-end operations by using Azure Service Bus, including dead-letter queue handling, messages, topics, and subscriptions
- Implement event-driven workflows by using Azure Event Grid, including filters, custom events, and retries
- Build serverless APIs, including implementing triggers and bindings
- Configure and deploy function apps
03Develop containerized solutions on Azure
Focuses on building, storing, versioning, and managing container images using Azure Container Registry, running registry tasks, and deploying containers to Azure App Service with environment variables and secrets.
Temas
- Build, store, version, and manage container images by using Azure Container Registry
- Build and run images by using Azure Container Registry Tasks
- Deploy containers to Azure App Service, including configuring App Service to supply environment variables and secrets
- Deploy applications to Azure Container Apps, including environment configuration and revision management
- Implement event-driven scaling by using Kubernetes Event‑driven Autoscaling (KEDA) in Container Apps
- Deploy and manage applications to Azure Kubernetes Service (AKS) by using manifest files
- Monitor and troubleshoot solutions on AKS and Container Apps by inspecting logs, events, and end-to-end connectivity
Objetivos de aprendizaje
- Build, store, version, and manage container images by using Azure Container Registry
- Build and run images by using Azure Container Registry Tasks
- Deploy containers to Azure App Service, including configuring App Service to supply environment variables and secrets
- Deploy applications to Azure Container Apps, including environment configuration and revision management
- Implement event-driven scaling by using Kubernetes Event‑driven Autoscaling (KEDA) in Container Apps
- Deploy and manage applications to Azure Kubernetes Service (AKS) by using manifest files
- Monitor and troubleshoot solutions on AKS and Container Apps by inspecting logs, events, and end-to-end connectivity
04Secure, monitor, troubleshoot Azure solutions
This domain covers securing cloud solutions, monitoring application health, and troubleshooting infrastructure issues to ensure optimal performance and high availability across all deployed Azure resources during daily operations.