Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB Practice Test
Gana confianza para Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB. Practica los conceptos, comprende las respuestas y refuerza tus conocimientos pregunta a pregunta.
Probar una preguntaDescripción y detalles del examen
This practice test is designed for IT professionals preparing to validate their expertise in designing and implementing cloud-native applications using Azure Cosmos DB for NoSQL. The exam covers five core domains: designing and implementing a Cosmos DB for NoSQL solution, implementing server-side programming constructs such as stored procedures, triggers, and user-defined functions, integrating the solution with other Azure services and client applications, maintaining the solution through monitoring, backup, and disaster recovery, and optimizing performance, cost, and throughput. The test includes 66 questions that mirror the complexity and breadth of the official certification exam. It is intended for developers, solution architects, and database administrators who have hands-on experience with Azure Cosmos DB and are seeking to formalize their skills. Candidates will gain a thorough understanding of how to model data for NoSQL, configure indexing policies, manage consistency levels, implement change feed processing, and tune request units (RUs) for cost efficiency. Successfully mastering this content equips learners to build scalable, globally distributed applications that meet enterprise requirements for latency, availability, and throughput.
Preguntas de Muestra
Elige una respuesta y consulta la explicación para ver cómo funciona la práctica.
During an Azure Cosmos DB architecture review, the topic is worldwide read/write availability. Which recommendation should be made?
Durante una revisión de la arquitectura de Azure Cosmos DB, el tema es la disponibilidad de lectura y escritura a nivel mundial. ¿Cuál recomendación debería hacerse?
A es correcto porque la cita respalda el uso de 99,999% de disponibilidad de lectura y escritura a nivel mundial. B es incorrecto porque la cita dice 99,999% a nivel mundial. C es incorrecto porque la cita incluye la disponibilidad de lectura y escritura. D es incorrecto porque la fuente proporciona un valor.
During an Azure Cosmos DB architecture review, the topic is throughput divided across physical partitions. Which recommendation should be made?
A es correcto porque la cita respalda dividir 18,000 RU/s equitativamente entre las tres particiones físicas. B es incorrecto porque la cita indica que cada partición usa un tercio. C es incorrecto porque la cita menciona un tercio del rendimiento total. D es incorrecto porque la cita describe la división por particiones físicas.
During an Azure Cosmos DB architecture review, the topic is vector and hybrid search. Which recommendation should be made?
Una es correcta porque la cita respalda el almacenamiento de embeddings con datos operativos para la búsqueda de similaridad vectorial y híbrida. B es incorrecto porque la cita dice embeddings con datos operativos. C es incorrecto porque la cita menciona la búsqueda de similaridad vectorial y híbrida. D es incorrecto porque la cita menciona esos escenarios.
During an Azure Cosmos DB architecture review, the topic is partition key for throughput distribution. Which recommendation should be made?
A es incorrecto porque la cita indica que es importante. B es incorrecto porque la fuente advierte que los campos de baja cardinalidad pueden crear una distribución desigual o particiones calientes bajo carga. C es correcto porque la cita respalda Choose a partition key that evenly distributes throughput consumption. D es incorrecto porque la fuente dice que la distribución uniforme es importante.
During an Azure Cosmos DB architecture review, the topic is regular partition keys. Which recommendation should be made?
A es incorrecto porque la fuente vincula la elección de la clave con predicados de consulta comunes o patrones de filtro. B es correcto porque la cita respalda el uso de una clave de partición regular cuando tiene alta cardinalidad y se alinea con los patrones de consulta. C es incorrecto porque la fuente advierte que los campos de baja cardinalidad pueden crear una distribución desigual o particiones calientes bajo carga. D es incorrecto porque la fuente indica evaluar opciones GA como claves sintéticas o jerárquicas primero.
Oportunidades profesionales y salario
Los rangos son cifras del mercado de EE. UU. salvo que se muestre un rango local.
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.
01Design and implement data models
Temas
- Design and implement a non-relational data model for Azure Cosmos DB for NoSQL
- Design a data partitioning strategy for Azure Cosmos DB for NoSQL
- Plan and implement sizing and scaling for a database created with Azure Cosmos DB
Objetivos de aprendizaje
- Design and implement a non-relational data model for Azure Cosmos DB for NoSQL: Develop a design by storing multiple entity types in the same container; Develop a design by storing multiple related entities in the same document; Develop a model that denormalizes data across documents; Develop a design by referencing between documents; Identify partition key, id, and unique keys; Identify data and associated access patterns; Specify a default time to live (TTL) on a container for a transactional store; Develop a design for versioning documents; Develop a design for document schema versioning
- Design a data partitioning strategy for Azure Cosmos DB for NoSQL: Choose a partitioning strategy based on a specific workload; Choose a partition key; Plan for transactions when choosing a partition key; Evaluate the cost of using a cross-partition query; Calculate and evaluate data distribution based on partition key selection; Calculate and evaluate throughput distribution based on partition key selection; Construct and implement a synthetic partition key; Design and implement a hierarchical partition key; Design partitioning for workloads that require multiple partition keys
- Plan and implement sizing and scaling for a database created with Azure Cosmos DB: Evaluate the throughput and data storage requirements for a specific workload; Choose between serverless, provisioned and free tier; Choose when to use database-level provisioned throughput; Design for granular scale units and resource governance; Evaluate the cost of the global distribution of data
02Maintain an Azure Cosmos DB solution
Temas
- Monitor and troubleshoot an Azure Cosmos DB solution
- Implement backup and restore for an Azure Cosmos DB solution
- Implement security for an Azure Cosmos DB solution
- Implement data movement for an Azure Cosmos DB solution
- Implement a DevOps process for an Azure Cosmos DB solution
Objetivos de aprendizaje
- Monitor and troubleshoot an Azure Cosmos DB solution: Evaluate response status code and failure metrics; Monitor the Normalized RU Consumption metric by using Azure Monitor; Monitor server-side latency metrics by using Azure Monitor; Monitor data replication in relation to latency and availability; Configure Azure Monitor alerts for Azure Cosmos DB; Implement and query Azure Monitor resource logs for Azure Cosmos DB; Monitor throughput across partitions; Monitor distribution of data across partitions; Monitor security by using logging and auditing
- Implement backup and restore for an Azure Cosmos DB solution: Choose between periodic and continuous backup; Configure periodic backup; Configure continuous backup and point-in-time restore; Locate a restore point for a point-in-time restore; Restore a database or container from a restore point
- Implement security for an Azure Cosmos DB solution: Choose between service-managed and customer-managed encryption keys; Configure network-level access control for Azure Cosmos DB; Configure data encryption for Azure Cosmos DB; Manage control plane access to Azure Cosmos DB by using Azure role-based access control (RBAC); Manage control plane access to Azure Cosmos DB Data Explorer by using Azure role-based access control (RBAC); Manage data plane access to Azure Cosmos DB by using Microsoft Entra ID; Configure cross-origin resource sharing (CORS) settings; Manage account keys by using Azure Key Vault; Implement customer-managed keys for encryption; Implement Always Encrypted
- Implement data movement for an Azure Cosmos DB solution: Choose a data movement strategy; Move data by using client SDK bulk operations; Move data by using Azure Data Factory and Azure Synapse pipelines; Move data by using a Kafka connector; Move data by using Azure Stream Analytics; Move data by using the Azure Cosmos DB Spark connector; Configure Azure Cosmos DB as a custom endpoint for an Azure IoT Hub
- Implement a DevOps process for an Azure Cosmos DB solution: Choose when to use declarative versus imperative operations; Provision and manage Azure Cosmos DB resources by using Azure Resource Manager templates; Migrate between standard and autoscale throughput by using PowerShell or Azure CLI; Initiate a regional failover by using PowerShell or Azure CLI; Maintain indexing policies in production by using Azure Resource Manager templates
03Optimize an Azure Cosmos DB solution
Temas
- Optimize query performance when using the API for Azure Cosmos DB for NoSQL
- Design and implement change feeds for Azure Cosmos DB for NoSQL
- Define and implement an indexing strategy for Azure Cosmos DB for NoSQL
Objetivos de aprendizaje
- Optimize query performance when using the API for Azure Cosmos DB for NoSQL: Adjust indexes on the database; Calculate the cost of the query; Retrieve request unit cost of a point operation or query; Implement Azure Cosmos DB integrated cache
- Design and implement change feeds for Azure Cosmos DB for NoSQL: Develop an Azure Functions trigger to process a change feed; Consume a change feed from within an application by using the SDK; Manage the number of change feed instances by using the change feed estimator; Implement denormalization by using a change feed; Implement referential enforcement by using a change feed; Implement aggregation persistence by using a change feed; Implement data archiving by using a change feed
- Define and implement an indexing strategy for Azure Cosmos DB for NoSQL: Choose when to use a read-heavy versus write-heavy index strategy; Choose an appropriate index type; Configure a custom indexing policy by using the Azure portal; Implement a composite index; Optimize index performance
04Design and implement data distribution
Temas
- Design and implement a replication strategy for Azure Cosmos DB
- Implement a custom conflict resolution policy for Azure Cosmos DB for NoSQL
Objetivos de aprendizaje
- Design and implement a replication strategy for Azure Cosmos DB: Choose when to distribute data; Define automatic failover policies for regional failure for Azure Cosmos DB for NoSQL; Perform manual failovers to move single master write regions; Choose a consistency model; Identify use cases for different consistency models; Evaluate the impact of consistency model choices on availability and associated request unit (RU) cost; Evaluate the impact of consistency model choices on performance and latency; Specify application connections to replicated data; Design and implement multi-region writes
- Implement a custom conflict resolution policy for Azure Cosmos DB for NoSQL
05Integrate an Azure Cosmos DB solution
Temas
- Enable Azure Cosmos DB analytical workloads
- Implement solutions across services
Objetivos de aprendizaje
- Enable Azure Cosmos DB analytical workloads: Configure Azure Cosmos DB Mirroring for Microsoft Fabric; Choose between Azure Cosmos DB Mirroring and the Azure Cosmos DB Spark connector; Enable the analytical store on a container; Enable a connection to an analytical store and query from Azure Synapse Spark or Azure Synapse SQL; Perform a query against the transactional store from Spark; Write data back to the transactional store from Spark; Implement Change Data Capture in the Azure Cosmos DB analytical store
- Implement solutions across services: Integrate events with other applications by using Azure Functions and Azure Event Hubs; Denormalize data by using Change Feed and Azure Functions; Enforce referential integrity by using Change Feed and Azure Functions; Aggregate data by using Change Feed and Azure Functions, including reporting; Archive data by using Change Feed and Azure Functions; Implement Azure AI Search for an Azure Cosmos DB solution