When reading from the transactional store using Spark, what is a key consideration regarding performance?

Reads require a separate analytical store endpoint

Reads are free and do not impact the transactional store

Reads consume request units (RUs) from the container, which can affect transactional workloads

Reads are limited to a maximum of 1 MB per request

At which level can you enable the analytical store for an Azure Cosmos DB container?

At the database account level, which applies to all containers in the account

At the database level, which applies to all containers in the database

At the container level, during container creation or by updating an existing container

At the partition key level, allowing different partitions to have different analytical store settings

What is the purpose of time travel in Warehouse within Microsoft Fabric?

To query historical data as it existed at a specific point in time

To enable real-time analytics on the warehouse

To automatically optimize query performance

To replicate data to a secondary region for disaster recovery

A company needs to run complex analytical queries on Cosmos DB data that require joining with data from other sources in a Fabric warehouse. They also need to support time travel for historical analysis. They have a strict requirement to minimize data movement and operational overhead. Which approach should they take?

Use the Spark connector to read from the analytical store and write results to a Fabric lakehouse.

Use the change feed to continuously write changes to a Fabric lakehouse.

Enable the analytical store and query it directly from Synapse SQL serverless.

Use Azure Cosmos DB Mirroring to replicate data into Fabric OneLake and query it from the warehouse.

Which workload is best suited for Azure Cosmos DB Mirroring rather than the Spark connector?

Running a daily batch job that aggregates data from the container into a Fabric warehouse

Performing a point lookup of a single item by ID from a Spark notebook

Updating item metadata in the container based on a Spark DataFrame

Streaming real-time sensor data into the container from an IoT hub

Microsoft Certified:Azure Cosmos DB Developer Specialty

Domain 3Objective 1

Enable Azure Cosmos DB Analytical Workloads DP-420 Practice Questions (Page 1)

Part of the Integrate an Azure Cosmos DB solution domain, which makes up ~9% of our current practice bank. Microsoft does not publish an official question count, but from its 100-minute exam (~40–65 total, ~4–6 in this domain), expect 2–3 from this objective — we provide 26 practice questions to prepare you well beyond it. (estimate)

26questions here

6free pages

8concepts

Questions 1–5

1foundation · easy
When reading from the transactional store using Spark, what is a key consideration regarding performance?
Select an answer first
2foundation · easy
At which level can you enable the analytical store for an Azure Cosmos DB container?
Select an answer first
3foundation · easy
What is the purpose of time travel in Warehouse within Microsoft Fabric?
Select an answer first
4expert · hard
A company needs to run complex analytical queries on Cosmos DB data that require joining with data from other sources in a Fabric warehouse. They also need to support time travel for historical analysis. They have a strict requirement to minimize data movement and operational overhead. Which approach should they take?
Select an answer first
5foundation · easy
Which workload is best suited for Azure Cosmos DB Mirroring rather than the Spark connector?
Select an answer first

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