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Free DP-203 Exam Braindumps (page: 40)

Page 40 of 94
PDF with 370 Questions

You are designing an Azure Databricks table. The table will ingest an average of 20 million streaming events per day.

You need to persist the events in the table for use in incremental load pipeline jobs in Azure Databricks. The solution must minimize storage costs and incremental load times.

What should you include in the solution?

  1. Partition by DateTime fields.
  2. Sink to Azure Queue storage.
  3. Include a watermark column.
  4. Use a JSON format for physical data storage.

Answer(s): B

Explanation:

The Databricks ABS-AQS connector uses Azure Queue Storage (AQS) to provide an optimized file source that lets you find new files written to an Azure Blob storage (ABS) container without repeatedly listing all of the files. This provides two major advantages:
Lower latency: no need to list nested directory structures on ABS, which is slow and resource intensive. Lower costs: no more costly LIST API requests made to ABS.


Reference:

https://docs.microsoft.com/en-us/azure/databricks/spark/latest/structured-streaming/aqs



HOTSPOT (Drag and Drop is not supported)
You have a self-hosted integration runtime in Azure Data Factory.

The current status of the integration runtime has the following configurations:
-Status: Running
-Type: Self-Hosted
-Version: 4.4.7292.1
-Running / Registered Node(s): 1/1
-High Availability Enabled: False
-Linked Count: 0
-Queue Length: 0
-Average Queue Duration. 0.00s

The integration runtime has the following node details:
-Name: X-M
-Status: Running
-Version: 4.4.7292.1
-Available Memory: 7697MB
-CPU Utilization: 6%
-Network (In/Out): 1.21KBps/0.83KBps
-Concurrent Jobs (Running/Limit): 2/14
-Role: Dispatcher/Worker
-Credential Status: In Sync

Use the drop-down menus to select the answer choice that completes each statement based on the information presented.

NOTE: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Box 1: fail until the node comes back online We see: High Availability Enabled: False

Note: Higher availability of the self-hosted integration runtime so that it's no longer the single point of failure in your big data solution or cloud data integration with Data Factory.

Box 2: lowered
We see:
Concurrent Jobs (Running/Limit): 2/14
CPU Utilization: 6%

Note: When the processor and available RAM aren't well utilized, but the execution of concurrent jobs reaches a node's limits, scale up by increasing the number of concurrent jobs that a node can run


Reference:

https://docs.microsoft.com/en-us/azure/data-factory/create-self-hosted-integration-runtime



You have an Azure Databricks workspace named workspace1 in the Standard pricing tier.
You need to configure workspace1 to support autoscaling all-purpose clusters. The solution must meet the following requirements:

-Automatically scale down workers when the cluster is underutilized for three minutes.
-Minimize the time it takes to scale to the maximum number of workers.
-Minimize costs.

What should you do first?

  1. Enable container services for workspace1.
  2. Upgrade workspace1 to the Premium pricing tier.
  3. Set Cluster Mode to High Concurrency.
  4. Create a cluster policy in workspace1.

Answer(s): B

Explanation:

For clusters running Databricks Runtime 6.4 and above, optimized autoscaling is used by all-purpose clusters in the Premium plan Optimized autoscaling:
Scales up from min to max in 2 steps.
Can scale down even if the cluster is not idle by looking at shuffle file state. Scales down based on a percentage of current nodes.
On job clusters, scales down if the cluster is underutilized over the last 40 seconds.
On all-purpose clusters, scales down if the cluster is underutilized over the last 150 seconds.
The spark.databricks.aggressiveWindowDownS Spark configuration property specifies in seconds how often a cluster makes down-scaling decisions. Increasing the value causes a cluster to scale down more slowly. The maximum value is 600.

Note: Standard autoscaling
Starts with adding 8 nodes. Thereafter, scales up exponentially, but can take many steps to reach the max. You can customize the first step by setting the spark.databricks.autoscaling.standardFirstStepUp Spark configuration property.
Scales down only when the cluster is completely idle and it has been underutilized for the last 10 minutes. Scales down exponentially, starting with 1 node.


Reference:

https://docs.databricks.com/clusters/configure.html



Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You are designing an Azure Stream Analytics solution that will analyze Twitter data.
You need to count the tweets in each 10-second window. The solution must ensure that each tweet is counted only once.

Solution: You use a tumbling window, and you set the window size to 10 seconds.
Does this meet the goal?

  1. Yes
  2. No

Answer(s): A

Explanation:

Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals. The following diagram illustrates a stream with a series of events and how they are mapped into 10-second tumbling windows.


Reference:

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



Page 40 of 94



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