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| Section | Weight | Objectives |
|---|---|---|
| Design and implement data distribution | 5-10% | - Manage data distribution and failover
|
| Optimize an Azure Cosmos DB solution | 15-20% | - Optimize query performance
|
| Design and implement data models | 35-40% | - Design partitioning strategy
|
| Integrate an Azure Cosmos DB solution | 5-10% | - Integrate with Azure services
|
| Maintain an Azure Cosmos DB solution | 25-30% | - Implement backup and restore
|
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NEW QUESTION # 180
Hotspot Question
You plan to create an Azure Cosmos DB database named db1 that will contain two containers.
One of the containers will contain blog posts, and the other will contain users. Each item in the blog post container will include:
- A single blog post
- All the comments associated to the blog post
- The names of the users who created the blog post and added the
comments
You need to design a solution to update usernames in the user container without causing data integrity issues. The solution must minimize administrative and development effort.
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 181
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 # 182
You have an Azure subscription that contains an Azure Cosmos DB for NoSQL account. You define and implement an encryption policy by using the following C# code.
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 QUESTION # 183
You have a database in an Azure Cosmos DB Core (SQL) API account.
You need to create an Azure function that will access the database to retrieve records based on a variable named accountnumber. The solution must protect against SQL injection attacks.
How should you define the command statement in the function?
Answer: B
Explanation:
Explanation
Azure Cosmos DB supports queries with parameters expressed by the familiar @ notation. Parameterized SQL provides robust handling and escaping of user input, and prevents accidental exposure of data through SQL injection.
For example, you can write a query that takes lastName and address.state as parameters, and execute it for various values of lastName and address.state based on user input.
SELECT *
FROM Families f
WHERE f.lastName = @lastName AND f.address.state = @addressState
Reference: https://docs.microsoft.com/en-us/azure/cosmos-db/sql/sql-query-parameterized-queries
NEW QUESTION # 184
You have an Azure Cosmos DB Core (SQL) account that has a single write region in West Europe.
You run the following Azure CLI script.
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
The Automatic failover option allows Azure Cosmos DB to failover to the region with the highest failover priority with no user action should a region become unavailable.
Box 2: No
West Europe is used for failover. Only North Europe is writable.
To Configure multi-region set UseMultipleWriteLocations to true.
Box 3: Yes
Provisioned throughput with single write region costs $0.008/hour per 100 RU/s and provisioned throughput with multiple writable regions costs $0.016/per hour per 100 RU/s.
Reference:
https://docs.microsoft.com/en-us/azure/cosmos-db/sql/how-to-multi-master
https://docs.microsoft.com/en-us/azure/cosmos-db/optimize-cost-regions
NEW QUESTION # 185
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