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| Section | Weight | Objectives |
|---|
| Maintain an Azure Cosmos DB solution | 25-30% | - Monitor and troubleshoot Azure Cosmos DB solutions
- 1. Troubleshoot throughput issues
- 2. Implement backup and recovery strategies
- 3. Monitor performance and availability
- 4. Monitor metrics and logs
|
| Optimize an Azure Cosmos DB solution | 15-20% | - Optimize query performance
- 1. Retrieve request unit cost
- 2. Calculate query cost
- 3. Implement integrated cache
- 4. Adjust indexing policies
- Design and implement change feeds
- 1. Consume change feeds using SDK
- 2. Manage change feed instances
- 3. Implement aggregation persistence
- 4. Develop Azure Functions triggers
- Define and implement indexing strategy
- 1. Optimize index performance
- 2. Choose appropriate index types
- 3. Choose read-heavy or write-heavy indexing
- 4. Configure custom indexing policies
- 5. Implement composite indexes
|
| Integrate an Azure Cosmos DB solution | 5-10% | - Implement solutions across services
- 1. Aggregate and archive data
- 2. Implement Azure AI Search integration
- 3. Use Change Feed for denormalization
- 4. Integrate Azure Functions and Event Hubs
- 5. Enforce referential integrity
- Enable analytical workloads
- 1. Write data back from Spark
- 2. Query analytical store from Synapse
- 3. Enable analytical store
- 4. Configure Azure Cosmos DB Mirroring
|
| Design and implement data distribution | 5-10% | - 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. Evaluate consistency model impacts
- 2. Define automatic failover policies
- 3. Choose when to distribute data
- 4. Choose a consistency model
- 5. Perform manual failovers
|
| Design and implement data models | 35-40% | - Design a data partitioning strategy
- 1. Evaluate cross-partition query cost
- 2. Choose a partition key
- 3. Choose a partitioning strategy
- 4. Plan for transactions with partition keys
- 5. Implement synthetic partition keys
- 6. Evaluate throughput and data distribution
- Plan and implement sizing and scaling
- 1. Evaluate global distribution costs
- 2. Evaluate throughput and storage requirements
- 3. Design resource governance and scaling
- 4. Choose between serverless, provisioned, and free tier
- 5. Configure throughput
- Implement server-side programming
- 1. Write and deploy stored procedures
- 2. Implement user-defined functions
- 3. Implement triggers
- Implement client connectivity options
- 1. Choose gateway or direct connectivity mode
- 2. Configure client-side threading and logging
- 3. Handle connection errors
- 4. Implement singleton client
- 5. Use Azure Cosmos DB emulator
- 6. Create database connections
- Implement data access
- 1. Use consistency and query request options
- 2. Implement optimistic concurrency control
- 3. Implement patch operations
- 4. Implement SQL queries with arrays and aggregation
- 5. Manage transactional batch operations
- 6. Use SDKs for CRUD operations
- Design and implement a non-relational data model for Azure Cosmos DB for NoSQL
- 1. Reference between documents
- 2. Identify data and associated access patterns
- 3. Store multiple related entities in the same document
- 4. Store multiple entity types in the same container
- 5. Identify partition key, id, and unique keys
- 6. Denormalize data across documents
|
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Microsoft Designing and Implementing Cloud-Native Applications Using Microsoft Azure Cosmos DB Sample Questions (Q194-Q199):
NEW QUESTION # 194
Hotspot Question
You have an Azure Cosmos DB for NoSQL container named Contacts that is configured as shown in the following exhibit.

Contacts contains the items shown in the following table.

To Contacts, you plan to Insert the items shown in the following table.

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:

NEW QUESTION # 195
You have an Azure Cosmos DB for NoSQL database named db1 that writes to multiple Azure regions.
You need to test the performance of db1 in the secondary region.
Which command should you run first?
- A. az cosmosdb network-rule remove
- B. az cosmosdb failover-priority-change
- C. az cosmosdb location show
- D. az cosmosdb sql database merge
Answer: B
Explanation:
To perform a manual failover in Azure Cosmos DB for NoSQL, use the 'az cosmosdb failover- priority-change' Azure CLI command with the -failover-policies option, changing the priority of the desired region to 0.
Note:
az cosmosdb failover-priority-change
Changes the failover priority for the Azure Cosmos DB database account.
Incorrect:
[Not A]
The Azure CLI command to show the properties of an Azure Cosmos DB location is az cosmosdb locations show.
[Not B]
az cosmosdb failover-priority-change
Changes the failover priority for the Azure Cosmos DB database account.
[Not D]
az cosmosdb network-rule remove
Remove a virtual network rule from an existing Cosmos DB database account.
Reference:
https://learn.microsoft.com/en-us/cli/azure/cosmosdb?view=azure-cli-latest#az-cosmosdb-failover-priority-change
NEW QUESTION # 196
Drag and Drop Question
You have an Azure subscription that contains an Azure AI Search service named search1 and an Azure Cosmos DB for NoSQL account. The account hosts a container named Container1 in consistent indexing mode.
You need to ensure that the data in Container1 is searchable by using search1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:
Explanation:

NEW QUESTION # 197
You have a container named container1 in an Azure Cosmos DB for NoSQL account named account1 that is set to the session default consistency level. The average size of an item in container1 is 20 KB.
You have an application named App1 that uses the Azure Cosmos DB SDK and performs a point read on the same set of items in container1 every minute.
You need to minimize the consumption of the request units (RUs) associated to the reads by App1. What should you do?
- A. In App1, change the consistency level of read requests to consistent prefix.
- B. In account1, provision a dedicated gateway and integrated cache
- C. In account1, change the default consistency level to bounded staleness.
- D. In App1, modify the connection policy settings.
Answer: A
Explanation:
Explanation
The cost of a point read for a 1 KB item is 1 RU. The cost of other operations depends on factors such as item size, indexing policy, consistency level, and query complexity . To minimize the consumption of RUs, you can optimize these factors according to your application needs.
For your scenario, one possible way to minimize the consumption of RUs associated to the reads by App1 is to change the consistency level of read requests to consistent prefix. Consistent prefix is a lower consistency level than session, which is the default consistency level for Azure Cosmos DB. Lower consistency levels consume fewer RUs than higher consistency levels2. Consistent prefix guarantees that reads never see out-of-order writes and that monotonic reads are preserved1. This may be suitable for your application if you can tolerate some eventual consistency.
NEW QUESTION # 198
You have a container in an Azure Cosmos DB for NoSQL account that stores data about orders.
The following is a sample of an order document.

Documents are up to 2 KB.
You plan to receive one million orders daily.
Customers will frequently view their past order history.
You are the evaluating whether to use orderDate as the partition key.
What are two effects of using orderDate as the partition key? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. You will exceed the maximum storage per partition
- B. There will always be a hot partition
- C. Queries will run cross-partition
- D. You will exceed the maximum number of partition key values
Answer: B,C
Explanation:
Not A: There is no limit to the total number of physical partitions in your container. As your provisioned throughput or data size grows, Azure Cosmos DB will automatically create new physical partitions by splitting existing ones Not B: 1 million a day x 2KB -> 2 GB of data/day while maximum storage across all items per (logical) partition: 20 GB
NEW QUESTION # 199
......
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