Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB Practice Test
Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB के लिए अपना आत्मविश्वास बढ़ाएँ। अवधारणाओं का अभ्यास करें, उत्तरों को समझें और हर सवाल के साथ अपना ज्ञान मज़बूत करें।
एक नमूना सवाल आज़माएँपरीक्षा का परिचय और विवरण
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.
नमूना प्रश्न
अभ्यास कैसे काम करता है, यह जानने के लिए एक उत्तर चुनें और व्याख्या देखें।
During an Azure Cosmos DB architecture review, the topic is worldwide read/write availability. Which recommendation should be made?
एक Azure Cosmos DB वास्तुकला समीक्षा के दौरान, विश्वभर में पढ़ने/लिखने की उपलब्धता का विषय है। जिस सिफारिश की जानी चाहिए, वह क्या है?
A सही है क्योंकि संदर्भ उपयोग 99.999% विश्वभर में पढ़ने और लिखने की उपलब्धता का समर्थन करता है। B गलत है क्योंकि संदर्भ 99.999% विश्वभर में कहता है। C गलत है क्योंकि संदर्भ पढ़ने और लिखने की उपलब्धता शामिल करता है। D गलत है क्योंकि स्रोत एक मान देता है।
During an Azure Cosmos DB architecture review, the topic is throughput divided across physical partitions. Which recommendation should be made?
A सही है क्योंकि उद्धरण 18,000 RU/s को तीन भौतिक विभाजनों में समान रूप से विभाजित करने का समर्थन करता है।
B गलत है क्योंकि उद्धरण कहता है कि प्रत्येक विभाजन एक-तिहाई का उपयोग करता है।
C गलत है क्योंकि उद्धरण कहता है कि कुल थ्रूपुट का एक-तिहाई।
D गलत है क्योंकि उद्धरण भौतिक विभाजनों द्वारा विभाजन का वर्णन करता है।
During an Azure Cosmos DB architecture review, the topic is vector and hybrid search. Which recommendation should be made?
एक Azure Cosmos DB आर्किटेक्चर समीक्षा के दौरान, वेक्टर और हाइब्रिड सर्च का विषय आता है। जो सिफारिश दी जानी चाहिए?
A सही है क्योंकि संदर्भ संग्रहीत वेक्टर और हाइब्रिड समानता सर्च के लिए ऑपरेशनल डेटा के साथ एम्बेडिंग का समर्थन करता है। B गलत है क्योंकि संदर्भ कहता है कि ऑपरेशनल डेटा के साथ एम्बेडिंग। C गलत है क्योंकि संदर्भ वेक्टर और हाइब्रिड समानता सर्च नाम देता है। D गलत है क्योंकि संदर्भ उन स्थितियों का नाम देता है।
During an Azure Cosmos DB architecture review, the topic is partition key for throughput distribution. Which recommendation should be made?
A गलत है क्योंकि citation कहता है कि यह महत्वपूर्ण है।
B गलत है क्योंकि source चेतावनी देता है कि low-cardinality fields लोड के तहत uneven distribution या hot partitions बना सकते हैं।
C सही है क्योंकि citation समर्थन करता है Choose a partition key that evenly distributes throughput consumption।
D गलत है क्योंकि source कहता है कि even distribution महत्वपूर्ण है।
During an Azure Cosmos DB architecture review, the topic is regular partition keys. Which recommendation should be made?
A गलत है क्योंकि स्रोत कुंजी चयन को सामान्य क्वेरी प्रेडिकेट्स या फ़िल्टर पैटर्न से जोड़ता है।
B सही है क्योंकि उद्धरण यह समर्थन करता है कि Use a regular partition key when it has high cardinality and aligns with query patterns।
C गलत है क्योंकि स्रोत चेतावनी देता है कि कम कार्डिनैलिटी वाले फ़ील्ड्स लोड के तहत असमान वितरण या हॉट पार्टिशन बना सकते हैं।
D गलत है क्योंकि स्रोत कहता है कि पहले GA विकल्पों जैसे synthetic या hierarchical keys का मूल्यांकन करें।
करियर के अवसर और वेतन
जब तक लोकल रेंज न दिखे, ये US मार्केट के आंकड़े हैं।
इस परीक्षा में क्या शामिल है
पढ़ाई की योजना बनाने के लिए प्रकाशित डोमेन भार का उपयोग करें। अभ्यास के परिणाम आपके सर्टिफिकेशन परीक्षा के स्कोर का अनुमान नहीं हैं।
01Design and implement data models
विषय
- 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
सीखने के उद्देश्य
- 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
विषय
- 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
सीखने के उद्देश्य
- 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
विषय
- 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
सीखने के उद्देश्य
- 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
विषय
- Design and implement a replication strategy for Azure Cosmos DB
- Implement a custom conflict resolution policy for Azure Cosmos DB for NoSQL
सीखने के उद्देश्य
- 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
विषय
- Enable Azure Cosmos DB analytical workloads
- Implement solutions across services
सीखने के उद्देश्य
- 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