Microsoft Building AI Applications with Azure Cosmos DB Exam Syllabus

Building AI Applications with Azure Cosmos DB PDF, DP-420 Dumps, DP-420 PDF, Building AI Applications with Azure Cosmos DB VCE, DP-420 Questions PDF, Microsoft DP-420 VCE, Microsoft MCA Azure Cosmos DB AI Developer Dumps, Microsoft MCA Azure Cosmos DB AI Developer PDFUse this quick start guide to collect all the information about Microsoft Building AI Applications with Azure Cosmos DB (DP-420) Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the DP-420 Microsoft Building AI Applications with Azure Cosmos DB exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual Microsoft MCA Azure Cosmos DB AI Developer certification exam.

The Microsoft Building AI Applications with Azure Cosmos DB certification is mainly targeted to those candidates who want to build their career in Microsoft Azure domain. The Microsoft Certified - Azure Cosmos DB AI Developer Associate exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of Microsoft MCA Azure Cosmos DB AI Developer.

Microsoft Building AI Applications with Azure Cosmos DB Exam Summary:

Exam Name Microsoft Certified - Azure Cosmos DB AI Developer Associate
Exam Code DP-420
Exam Price $165 (USD)
Duration 100 mins
Number of Questions 40-60
Passing Score 700 / 1000
Books / Training DP-420T00-A: Build AI applications with Azure Cosmos DB
Schedule Exam Pearson VUE
Sample Questions Microsoft Building AI Applications with Azure Cosmos DB Sample Questions
Practice Exam Microsoft DP-420 Certification Practice Exam

Microsoft DP-420 Exam Syllabus Topics:

Topic Details

Design and develop database solutions (40-45%)
Evaluate resource models and SDK solutions - Evaluate consistency levels
- Evaluate Request Unit (RU) consumption requirements
- Evaluating and configuring appropriate throughput
- Configure account-level and container-level settings
- Create and manage database and containers
- Initialize SDK clients and connections
Implement database operations - Implement queries, parameterized queries, paging, and continuation tokens
- Implement point read operations
- Implement create, replace, update, patch and delete operations
- Implement bulk, batch, and transactional operations
- Configure Time to Live (TTL)
- Implement concurrency control mechanisms
Design and implement data models and partitioning strategies - Design data models for access patterns and operational requirements
- Design and select partition keys based on cardinality, access patterns, and write distribution requirements
- Determine when to embed or reference related data
- Evaluate the impact of partition key selection on scalability and performance
- Implement hierarchical partition keys (HPKs) and multi-tenant partitioning strategies
- Implement synthetic partition keys
- Identify partitioning anti-patterns and partitioning issues during solution reviews
- Estimate workload cost requirements
Implement change feed - Implement change feed operations
- Differentiate between change feed processor and pull mode models
- Configure lease containers
- Implement change feed processing using latest mode or all versions and delete
- Implement copy container jobs
Design and implement Azure Cosmos DB solutions by using AI-assisted tools - Implement Agent Kit to follow Azure Cosmos DB development recommended solutions
- Implement Agent Kit to accelerate AI application development

Secure, optimize, and deploy database solutions (30-35%)
Implement database security controls - Configure authentication mechanisms
- Configure authorization controls
- Configure network security
- Configure dynamic data masking
Implement availability and recovery solutions - Configure backup and restore solutions
- Implement multi-region read and write configurations
- Configure partition failover solutions
Optimize and remediate database performance - Optimize query performance
- Analyze query performance metrics
- Analyze index performance metrics
- Monitor end-to-end latency against service objectives
- Configure Azure Monitor alerts
- Analyze diagnostic logs in Log Analytics
Implement Fleet management and monitoring across multiple accounts and subscriptions - Configure Fleet level throughput management
- Configure Fleet monitoring with fleet analytics

Implement AI and analytics capabilities in database solutions (20-25%)
Design and implement AI retrieval - Design and implement full-text search
- Design for vector data, including vector data type, vector indexes, and size
- Design and implement vector partitioning and sharded DiskANN implementations
- Implement vector search
- Design and implement hybrid search
- Optimize full-text and vector performance
- Design and implement Retrieval-Augmented Generation (RAG)
Design and implement agent memory stores - Design durable memory stores
- Implement conversation state
- Implement semantic memory
Design and implement operational analytics on Azure Cosmos DB - Choose between Azure Cosmos DB and Cosmos DB in Microsoft Fabric
- Configure and monitor data mirroring into Microsoft Fabric
- Query Azure Cosmos DB mirrored JSON data in Microsoft Fabric with T-SQL and Spark
- Read and write data into Azure Cosmos DB from Microsoft Fabric with Cosmos DB Spark connector

To ensure success in Microsoft MCA Azure Cosmos DB AI Developer certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for Microsoft Building AI Applications with Azure Cosmos DB (DP-420) exam.

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