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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Integrate an Azure Cosmos DB solution | 5-10% | - Enable analytical workloads
|
| Topic 2: Optimize an Azure Cosmos DB solution | 15-20% | - Define and implement indexing strategy
|
| Topic 3: Design and implement data models | 35-40% | - Design partitioning strategy
|
| Topic 4: Maintain an Azure Cosmos DB solution | 25-30% | - DevOps and automation
|
| Topic 5: Design and implement data distribution | 5-10% | - Design replication strategy
|
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NEW QUESTION # 42
Drag and Drop Question
You have an Azure Cosmos DB container named owners. Each item in owners represents an owner and their pets.
The following is a sample of the JSON
You are writing a query that will retrieve the average age of each pet owned by each owner, if the pet is older than two years.
How should you complete the query? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 43
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?
Answer: D
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 # 44
You have a database named telemetry in an Azure Cosmos DB Core (SQL) API account that stores IoT dat a. The database contains two containers named readings and devices.
Documents in readings have the following structure.
id
deviceid
timestamp
ownerid
measures (array)
- type
- value
- metricid
Documents in devices have the following structure.
id
deviceid
owner
- ownerid
- emailaddress
- name
brand
model
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 # 45
You have an Azure Cosmos DB for NoSQL container.
All queries to the container use the Bounded Staleness consistency level.
You discover that when many queries are executed against the container, an HTTP 429 error is returned.
What are two ways to reduce the number of HTTP 429 errors? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: B,C
Explanation:
[E]
Diagnose and troubleshoot "Request rate too large" (429) exceptions
A "Request rate too large" exception, also known as error code 429, indicates that your requests against Azure Cosmos DB are being rate limited.
You can increase the RU/s on the database or container using the client SDKs, Azure portal, PowerShell, CLI, or ARM template.
[B]
Strong consistency
Strong consistency offers a linearizability guarantee. Linearizability means serving requests concurrently. The reads are guaranteed to return the most recent committed version of an item. A client never sees an uncommitted or partial write. Users are always guaranteed to read the latest committed write.
Note: Azure Cosmos DB offers five well-defined levels. From strongest to weakest, the levels are:
Strong
Bounded staleness
Session
Consistent prefix
Eventual
Reference:
https://learn.microsoft.com/en-us/azure/cosmos-db/nosql/troubleshoot-request-rate-too-large
https://learn.microsoft.com/en-us/azure/cosmos-db/consistency-levels
NEW QUESTION # 46
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: B
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 # 47
......
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