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| Section | Objectives |
|---|---|
| Optimize and maintain Cosmos DB solutions | - Performance optimization
|
| Design and implement data models for Azure Cosmos DB | - Model data for NoSQL workloads
|
| Design and implement data distribution | - Partitioning and scaling
|
| Integrate Azure Cosmos DB with Azure services | - Event-driven architectures
|
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NEW QUESTION # 123
You have an Azure Cosmos DB for NoSQL database named DB1 that hosts the containers shown in the following table.
The user permissions are bound to a user and will NOT be queried independently of the user.
The product price is bound to a product and will NOT be queried independently of the product.
You are designing a new data model for DB1.
You need to optimize the data model to improve read performance and maintainability. The solution must meet the following requirements:
- Future enhancements of the user permissions must be supported.
- Future enhancements of the product price do NOT need to be supported.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
To improve read performance and maintainability, you must transition from a normalized relational design to an optimized NoSQL design. NoSQL databases prioritize reading data in a single request rather than joining multiple containers.
Container Denormalization Strategy
[B]
Product Data (Container3 and Container4)
Action: Denormalize Container4 into Container3 by flattening the fields.
Product prices are never queried independently. Combining them allows a single read operation to fetch the complete product details.
Maintainability: Since future price enhancements are not required, you can directly flatten the price fields (e.g., price, currency) into the root of the product document. This keeps the schema simple and highly efficient.
[C]
User Data (Container1 and Container2)
Action: Denormalize Container2 into Container1 using reference (embedding as an array).
Users and permissions are always queried together. Embedding eliminates a second network call, drastically improving read performance.
Maintainability: Because future enhancements to permissions must be supported, embedding them as a strongly-typed JSON array or sub-object within the user document allows you to easily add new fields (e.g., expiration dates, scopes) later without breaking the schema.
Reference:
https://microsoftlearning.github.io/dp-420-cosmos-db-dev/instructions/17-denormalize.html
NEW QUESTION # 124
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:
NEW QUESTION # 125
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.
{
" studentId " : " 631282 " ,
" firstName " : " James " ,
" lastName " : " Smith " ,
" enrollmentYear " : 1990,
" isActivelyEnrolled " : true,
" address " : {
" street " : " " ,
" city " : " " ,
" stateProvince " : " " ,
" postal " : " " ,
}
}
The container1 container has the following indexing policy.
{
" indexingMode " : " consistent " ,
" includePaths " : [
{
" path " : " /* "
},
{
" path " : " /address/city/? "
}
],
" excludePaths " : [
{
" path " : " /address/* "
},
{
" path " : " /firstName/? "
}
]
}
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 # 126
Hotspot Question
You have an Azure subscription that contains an Azure Cosmos DB for NoSQL database named DB1. The shared throughput provisioned for DB1 is 10,000 DTU/s. DB1 contains the containers shown in the following table.
You need to modify the throughput for the containers. The solution must meet the following requirements:
- The maximum throughput for Container1 must be 4,000 DTU/s.
- The throughput for Container2 must be shared across the containers.
- Administrative effort must be minimized.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 127
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 # 128
......
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