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Microsoft DP-420 Exam Syllabus Topics:

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

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Microsoft Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB Sample Questions (Q70-Q75):

NEW QUESTION # 70
You have an Azure Cosmos DB for NoSQL container named Container1 that stores 500 GB of data.
You plan to run cross-partition queries that target Container1. The queries will include the partition key values specified in the db filters.
You need to minimize the cost of the queries.
What should you do?

Answer: D

Explanation:
To keep the price (RU cost) of your queries low in an Azure Cosmos DB for NoSQL container with hundreds of GB of data, the correct action is: Add the IN keyword to the queries.
Why it works: Using the IN keyword allows you to provide a specific list of partition key values in the query's WHERE clause.
Cost reduction: By specifying partition keys, the query engine can route the request only to the specific physical partitions that hold those values.
Efficiency: This avoids a full "fan-out" query (visiting every physical partition in a container with hundreds of GBs), which is the most expensive way to query Cosmos DB.
Incorrect:
[Not A]
Decrease the value of the MaxBufferedItemCount parameter:
This parameter controls how many items the client-side SDK buffers during a cross-partition query to manage memory and latency. It does not reduce the Request Unit (RU) cost charged by the database.
[Not B]
Add the DISTINCT keyword to the queries:
The DISTINCT keyword often increases the RU cost because the query engine must perform extra work to identify and remove duplicate values across multiple partitions.
[Not C]
Decrease the value of the MaxConcurrency parameter:
MaxConcurrency limits the number of simultaneous network requests the SDK makes to different partitions. While it can help manage client-side resources and avoid rate limiting, it does not reduce the total RU cost of the query execution on the server side.
Reference:
https://learn.microsoft.com/en-us/azure/cosmos-db/performance-tips-query-sdk


NEW QUESTION # 71
You plan to create an Azure Cosmos DB Core (SQL) API account that will use customer-managed keys stored in Azure Key Vault.
You need to configure an access policy in Key Vault to allow Azure Cosmos DB access to the keys.
Which three permissions should you enable in the access policy? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

Answer: A,D,E


NEW QUESTION # 72
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.

Answer:

Explanation:

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 container1.
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-time2.
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.


NEW QUESTION # 73
Hotspot Question
You plan to use a multi-region Azure Cosmos DB for NoSQL account to store data for a new application suite. The suite contains the applications shown in the following table.

Each application should use the weakest consistency level possible.
Which consistency level should you configure for each application? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
https://willvelida.medium.com/understanding-consistency-levels-in-azure-cosmos-db-77b90dbf3daa


NEW QUESTION # 74
You have an Azure subscription. The subscription contains an Azure Cosmos DB for NoSQL account named account1 that hosts a container named Devices. Devices has a partition key named type.

You are evaluating the resource utilization of the query. How will the query be executed?

Answer: C


NEW QUESTION # 75
......

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