DP-203 Online Practice Questions and Answers

Correct Answer:

Box 1: Azure Stream Analytics

Box 2: Hopping Hopping window functions hop forward in time by a fixed period. It may be easy to think of them as Tumbling windows that can overlap and be emitted more often than the window size. Events can belong to more than one Hopping window result set. To make a Hopping window the same as a Tumbling window, specify the hop size to be the same as the window size.

Box 3: Point within polygon

Reference: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions

Correct Answer:

Box 1: Yes

You can now use a new extension of Azure Stream Analytics SQL to specify the number of partitions of a stream when reshuffling the data.

The outcome is a stream that has the same partition scheme. Please see below for an example:

WITH step1 AS (SELECT * FROM [input1] PARTITION BY DeviceID INTO 10), step2 AS (SELECT * FROM [input2] PARTITION BY DeviceID INTO 10)

SELECT * INTO [output] FROM step1 PARTITION BY DeviceID UNION step2 PARTITION BY DeviceID

Note: The new extension of Azure Stream Analytics SQL includes a keyword INTO that allows you to specify the number of partitions for a stream when performing reshuffling using a PARTITION BY statement.

Box 2: Yes

When joining two streams of data explicitly repartitioned, these streams must have the same partition key and partition count.

Box 3: Yes

10 partitions x six SUs = 60 SUs is fine.

Note: Remember, Streaming Unit (SU) count, which is the unit of scale for Azure Stream Analytics, must be adjusted so the number of physical resources available to the job can fit the partitioned flow. In general, six SUs is a good number to

assign to each partition. In case there are insufficient resources assigned to the job, the system will only apply the repartition if it benefits the job.

Correct Answer:

Correct Answer: B

Instead use a tumbling window. Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals.

Reference: https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics

Correct Answer: C

General symptoms of the job hitting system resource limits include:

If the backlog event metric keeps increasing, it's an indicator that the system resource is constrained (either because of output sink throttling, or high CPU).

Note: Backlogged Input Events: Number of input events that are backlogged. A non-zero value for this metric implies that your job isn't able to keep up with the number of incoming events. If this value is slowly increasing or consistently nonzero, you should scale out your job: adjust Streaming Units.

Reference:

https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-scale-jobs

https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-monitoring

Correct Answer: C

The performance capacity of a query is determined by the user's resource class. Resource classes are pre-determined resource limits in Synapse SQL pool that govern compute resources and concurrency for query execution. Resource classes can help you configure resources for your queries by setting limits on the number of queries that run concurrently and on the compute-resources assigned to each query. There's a trade-off between memory and concurrency.

Smaller resource classes reduce the maximum memory per query, but increase concurrency. Larger resource classes increase the maximum memory per query, but reduce concurrency.

Reference: https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/resource-classes-for-workload-management

Correct Answer: B

Instead convert the files to compressed delimited text files.

Reference: https://docs.microsoft.com/en-us/azure/sql-data-warehouse/guidance-for-loading-data

Correct Answer: D

https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/stream-processing

Correct Answer: B

A shared access signature (SAS) provides secure delegated access to resources in your storage account. With a SAS, you have granular control over how a client can access your data. For example:

What resources the client may access.

What permissions they have to those resources.

How long the SAS is valid.

Reference:

https://docs.microsoft.com/en-us/azure/storage/common/storage-sas-overview

Correct Answer: D

Tumbling window triggers are a type of trigger that fires at a periodic time interval from a specified start time, while retaining state. Tumbling windows are a series of fixed-sized, non-overlapping, and contiguous time intervals. A tumbling window trigger has a one-to-one relationship with a pipeline and can only reference a singular pipeline. Tumbling window trigger is a more heavy weight alternative for schedule trigger offering a suite of features for complex scenarios.

The following table provides a comparison of the tumbling window trigger and schedule trigger:

*

Backfill scenarios Tumbling: Supported Scheduled: Not supported

*

Retry capability

Tumbling: Supported Failed pipeline runs have a default retry policy of 0, or a policy that's specified by the user in the trigger definition.

Scheduled: Not supported

* Etc.

Reference: https://learn.microsoft.com/en-us/azure/data-factory/how-to-create-tumbling-window-trigger https://learn.microsoft.com/en-us/azure/data-factory/concepts-pipeline-execution-triggers