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Microsoft DP-420 Exam Syllabus Topics:

SectionWeightObjectives
Maintain an Azure Cosmos DB solution25-30%- Monitor and troubleshoot Azure Cosmos DB solutions
  • 1. Implement backup and recovery strategies
  • 2. Monitor performance and availability
  • 3. Troubleshoot throughput issues
  • 4. Monitor metrics and logs
Design and implement data distribution5-10%- Design and implement replication strategy
  • 1. Define automatic failover policies
  • 2. Choose when to distribute data
  • 3. Evaluate consistency model impacts
  • 4. Perform manual failovers
  • 5. Choose a consistency model
- Design and implement multi-region writes
  • 1. Implement conflict resolution policies
  • 2. Choose when to use multi-region writes
  • 3. Implement multi-region writes
Integrate an Azure Cosmos DB solution5-10%- Implement solutions across services
  • 1. Aggregate and archive data
  • 2. Use Change Feed for denormalization
  • 3. Implement Azure AI Search integration
  • 4. Integrate Azure Functions and Event Hubs
  • 5. Enforce referential integrity
- Enable analytical workloads
  • 1. Configure Azure Cosmos DB Mirroring
  • 2. Query analytical store from Synapse
  • 3. Write data back from Spark
  • 4. Enable analytical store
Design and implement data models35-40%- Implement client connectivity options
  • 1. Use Azure Cosmos DB emulator
  • 2. Implement singleton client
  • 3. Choose gateway or direct connectivity mode
  • 4. Handle connection errors
  • 5. Create database connections
  • 6. Configure client-side threading and logging
- Design and implement a non-relational data model for Azure Cosmos DB for NoSQL
  • 1. Identify data and associated access patterns
  • 2. Store multiple entity types in the same container
  • 3. Store multiple related entities in the same document
  • 4. Denormalize data across documents
  • 5. Reference between documents
  • 6. Identify partition key, id, and unique keys
- Implement server-side programming
  • 1. Implement user-defined functions
  • 2. Write and deploy stored procedures
  • 3. Implement triggers
- Plan and implement sizing and scaling
  • 1. Evaluate throughput and storage requirements
  • 2. Configure throughput
  • 3. Design resource governance and scaling
  • 4. Evaluate global distribution costs
  • 5. Choose between serverless, provisioned, and free tier
- Design a data partitioning strategy
  • 1. Plan for transactions with partition keys
  • 2. Evaluate cross-partition query cost
  • 3. Choose a partitioning strategy
  • 4. Choose a partition key
  • 5. Evaluate throughput and data distribution
  • 6. Implement synthetic partition keys
- Implement data access
  • 1. Implement SQL queries with arrays and aggregation
  • 2. Use SDKs for CRUD operations
  • 3. Implement patch operations
  • 4. Implement optimistic concurrency control
  • 5. Use consistency and query request options
  • 6. Manage transactional batch operations
Optimize an Azure Cosmos DB solution15-20%- Design and implement change feeds
  • 1. Consume change feeds using SDK
  • 2. Develop Azure Functions triggers
  • 3. Manage change feed instances
  • 4. Implement aggregation persistence
- Optimize query performance
  • 1. Calculate query cost
  • 2. Adjust indexing policies
  • 3. Retrieve request unit cost
  • 4. Implement integrated cache
- Define and implement indexing strategy
  • 1. Implement composite indexes
  • 2. Configure custom indexing policies
  • 3. Choose appropriate index types
  • 4. Choose read-heavy or write-heavy indexing
  • 5. Optimize index performance

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Microsoft Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB Sample Questions (Q68-Q73):

NEW QUESTION # 68
You have an Azure Cosmos DB Core (SQL) API account named account1.
You have the Azure virtual networks and subnets shown in the following table.

The vnet1 and vnet2 networks are connected by using a virtual network peer.
The Firewall and virtual network settings for account1 are configured as shown in the exhibit.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Box 1: Yes
VM1 is on vnet1.subnet1 which has the Endpoint Status enabled.
Box 2: No
Only virtual network and their subnets added to Azure Cosmos account have access. Their peered VNets cannot access the account until the subnets within peered virtual networks are added to the account.
Box 3: No
Only virtual network and their subnets added to Azure Cosmos account have access.
Reference: https://docs.microsoft.com/en-us/azure/cosmos-db/how-to-configure-vnet-service-endpoint


NEW QUESTION # 69
You plan to deploy two Azure Cosmos DB Core (SQL) API accounts that will each contain a single database.
The accounts will be configured as shown in the following table.

How should you provision the containers within each account to minimize costs? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Box 1: Serverless capacity mode
Azure Cosmos DB serverless best fits scenarios where you expect intermittent and unpredictable traffic with long idle times. Because provisioning capacity in such situations isn't required and may be cost-prohibitive, Azure Cosmos DB serverless should be considered in the following use-cases:
Getting started with Azure Cosmos DB
Running applications with bursty, intermittent traffic that is hard to forecast, or low (<10%) average-to-peak traffic ratio Developing, testing, prototyping and running in production new applications where the traffic pattern is unknown Integrating with serverless compute services like Azure Functions Box 2: Provisioned throughput capacity mode and autoscale throughput The use cases of autoscale include:
Variable or unpredictable workloads: When your workloads have variable or unpredictable spikes in usage, autoscale helps by automatically scaling up and down based on usage. Examples include retail websites that have different traffic patterns depending on seasonality; IOT workloads that have spikes at various times during the day; line of business applications that see peak usage a few times a month or year, and more. With autoscale, you no longer need to manually provision for peak or average capacity.
Reference:
https://docs.microsoft.com/en-us/azure/cosmos-db/serverless
https://docs.microsoft.com/en-us/azure/cosmos-db/provision-throughput-autoscale#use-cases-of-autoscale


NEW QUESTION # 70
You plan to deploy two Azure Cosmos DB Core (SQL) API accounts that will each contain a single database.
The accounts will be configured as shown in the following table.

How should you provision the containers within each account to minimize costs? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: Serverless capacity mode
Azure Cosmos DB serverless best fits scenarios where you expect intermittent and unpredictable traffic with long idle times. Because provisioning capacity in such situations isn't required and may be cost-prohibitive, Azure Cosmos DB serverless should be considered in the following use-cases:
Getting started with Azure Cosmos DB
Running applications with bursty, intermittent traffic that is hard to forecast, or low (<10%) average-to-peak traffic ratio Developing, testing, prototyping and running in production new applications where the traffic pattern is unknown Integrating with serverless compute services like Azure Functions Box 2: Provisioned throughput capacity mode and autoscale throughput The use cases of autoscale include:
Variable or unpredictable workloads: When your workloads have variable or unpredictable spikes in usage, autoscale helps by automatically scaling up and down based on usage. Examples include retail websites that have different traffic patterns depending on seasonality; IOT workloads that have spikes at various times during the day; line of business applications that see peak usage a few times a month or year, and more. With autoscale, you no longer need to manually provision for peak or average capacity.
Reference:
https://docs.microsoft.com/en-us/azure/cosmos-db/serverless
https://docs.microsoft.com/en-us/azure/cosmos-db/provision-throughput-autoscale#use-cases-of-autoscale


NEW QUESTION # 71
You have an Azure Cosmos DB for NoSQL database that hosts a container named Container1. Container1 stores customer documents that have the following structure.

You need to retrieve the city of residence and the age of each customer who is older than 40.
Which three statements should you run in sequence to build the query? To answer, move the appropriate statements from the list of statements to the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation:


NEW QUESTION # 72
Hotspot Question
You have a container named container1 in an Azure Cosmos DB Core (SQL) API account.
The following is a sample of a document in container1.

The container1 container has the following indexing policy.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: Yes
"path": "/*" is in includePaths.
Include the root path to selectively exclude paths that don't need to be indexed. This is the recommended approach as it lets Azure Cosmos DB proactively index any new property that may be added to your model.
Box 2: No
"path": "/firstName/?" is in excludePaths.
Box 3: Yes
"path": "/address/city/?" is in includePaths
Reference: https://docs.microsoft.com/en-us/azure/cosmos-db/index-policy


NEW QUESTION # 73
......

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