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Microsoft DP-420 Exam Syllabus Topics:

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

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Microsoft Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB DP-420 Prüfungsfragen mit Lösungen (Q194-Q199):

194. Frage
You are implementing an Azure Data Factory data flow that will use an Azure Cosmos DB (SQL API) sink to write a dataset. The data flow will use 2,000 Apache Spark partitions.
You need to ensure that the ingestion from each Spark partition is balanced to optimize throughput.
Which sink setting should you configure?

Antwort: B

Begründung:
Batch size: An integer that represents how many objects are being written to Cosmos DB collection in each batch. Usually, starting with the default batch size is sufficient. To further tune this value, note:
Cosmos DB limits single request's size to 2MB. The formula is "Request Size = Single Document Size * Batch Size". If you hit error saying "Request size is too large", reduce the batch size value.
The larger the batch size, the better throughput the service can achieve, while make sure you allocate enough RUs to empower your workload.
Reference: https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-cosmos-db


195. Frage
You have an Azure subscription that contains an Azure Cosmos DB for NoSQL database named DB1.
You develop a software as a service (SaaS) app named App1 that uses DB1. App1 will be used by multiple vendors to manage their customers.
You need to create a container in DB1 that will store customer and order details for each vendor. The solution must meet the following requirements:
* Each vendor must have a unique tenant ID.
* The performance of the container must be optimized for reads.
* Customer IDs can be duplicated across the container but must be unique for each vendor.
* The container must be optimized for concurrent queries from multiple customers. Each query will return data for a single vendor.
How should you configure the container?

Antwort: D


196. Frage
You have an Azure Cosmos DB for NoSQL account named account that has the disablekeyBasedletadatwriteAccess property enabled.
You are developing an app named App1 that will be used by a use1 named DevUser1 to create containers in account1. DevUser1 has a non-privileged user account in the Azure AD tenant.
You need to ensure that DevUser1 can use App1 to create containers in account1.
What should you do? To answer, select the appropriate options in the answer area.
NOTE Each correct selection is worth one point.

Antwort:

Begründung:


197. Frage
You have an Apache Spark pool in Azure Synapse Analytics that runs the following Python code in a notebook.

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

Antwort:

Begründung:

Explanation
New and updated orders will be added to contoso-erp.orders: Yes
The code performs bulk data ingestion from contoso-app: No
Both contoso-app and contoso-erp have Analytics store enabled: Yes
The code uses the spark.readStream method to read data from a container named orders in a database named contoso-app. The data is then filtered by a condition and written to another container named orders in a database named contoso-erp using the spark.writeStream method. The write mode is set to "append", which means that new and updated orders will be added to the destination container The code does not perform bulk data ingestion from contoso-app, but rather stream processing. Bulk data ingestion is a process of loading large amounts of data into a data store in batches. Stream processing is a process of continuously processing data as it arrives in real-time Both contoso-app and contoso-erp have Analytics store enabled, because they are both accessed by Spark pools using the spark.cosmos.oltp method. This method requires that the containers have Analytics store enabled, which is a feature that allows Spark pools to query data stored in Azure Cosmos DB containers using SQL APIs3.


198. Frage
You have an Azure Cosmos DB for NoSQL database that hosts a container named Contained. Contained stores product details. You have a query named Query1 that contains the following statements.

You need to optimize the performance of Query1 by using custom indexing. Which set of index components will optimize Query1?

Antwort: B


199. Frage
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