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

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Microsoft Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB Sample Questions (Q58-Q63):

NEW QUESTION # 58
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 # 59
You need to create a database in an Azure Cosmos DB for NoSQL account. The database will contain three containers named coll1, coll2, and coll3. The coll1 container will have unpredictable read and write volumes. The coll2 and coll3 containers will have predictable read and write volumes. The expected maximum throughput for coll1 and coll2 is 50,000 request units per second (RU/s) each.
How should you provision the collection while minimizing costs?

Answer: B

Explanation:
Manual is best suited for workloads with steady or predictable traffic.
Autoscale workloads is best suited with variable or unpredictable traffic.
Note: Azure Cosmos DB allows you to set provisioned throughput on your databases and containers. There are two types of provisioned throughput, standard (manual) or autoscale.
Autoscale provisioned throughput in Azure Cosmos DB allows you to scale the throughput (RU/s) of your database or container automatically and instantly. The throughput is scaled based on the usage, without impacting the availability, latency, throughput, or performance of the workload.
The use cases of autoscale include:
* Variable or unpredictable workloads
Reference:
https://docs.microsoft.com/en-us/azure/cosmos-db/how-to-choose-offer


NEW QUESTION # 60
You have an Azure Cosmos DB Core (SQL) API account named account1 that is configured for automatic failover. The account1 account has a single read-write region in West US and a read region in East US.
You run the following PowerShell command.
Update-AzCosmosDBAccountFailoverPriority -ResourceGroupName `rg1` -Name
`account1` -FailoverPolicy @(`East US`, `West US`)
What is the effect of running the command?

Answer: C

Explanation:
Note: The Update-AzCosmosDBAccountFailoverPriority command updates Failover Region Priority of a CosmosDB Account.
Parameter -FailoverPolicy is an array of strings having region names, ordered by failover priority.
E.g eastus, westus
Reference:
https://docs.microsoft.com/en-us/azure/cosmos-db/high-availability


NEW QUESTION # 61
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a container named container1 in an Azure Cosmos DB Core (SQL) API account.
You need to make the contents of container1 available as reference data for an Azure Stream Analytics job.
Solution: You create an Azure Data Factory pipeline that uses Azure Cosmos DB Core (SQL) API as the input and Azure Blob Storage as the output.
Does this meet the goal?

Answer: B

Explanation:
Explanation
Instead create an Azure function that uses Azure Cosmos DB Core (SQL) API change feed as a trigger and Azure event hub as the output.
The Azure Cosmos DB change feed is a mechanism to get a continuous and incremental feed of records from an Azure Cosmos container as those records are being created or modified. Change feed support works by listening to container for any changes. It then outputs the sorted list of documents that were changed in the order in which they were modified.
The following diagram represents the data flow and components involved in the solution:

Reference: https://docs.microsoft.com/en-us/azure/cosmos-db/sql/changefeed-ecommerce-solution


NEW QUESTION # 62
You have the indexing policy shown in the following exhibit.

Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: ORDER BY c.name DESC, c.age DESC
Queries that have an ORDER BY clause with two or more properties require a composite index.
The following considerations are used when using composite indexes for queries with an ORDER BY clause with two or more properties:
If the composite index paths do not match the sequence of the properties in the ORDER BY clause, then the composite index can't support the query.
The order of composite index paths (ascending or descending) should also match the order in the ORDER BY clause.
The composite index also supports an ORDER BY clause with the opposite order on all paths.
Box 2: At the same time as the item creation
Azure Cosmos DB supports two indexing modes:
Consistent: The index is updated synchronously as you create, update or delete items. This means that the consistency of your read queries will be the consistency configured for the account.
None: Indexing is disabled on the container.
Reference: https://docs.microsoft.com/en-us/azure/cosmos-db/index-policy


NEW QUESTION # 63
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