Question #31
You are developing a solution that will stream to Azure Stream Analytics. The solution will have both streaming data and reference data.
Which input type should you use for the reference data?
- A. Azure Cosmos DB
- B. Azure Blob storage
- C. Azure IoT Hub
- D. Azure Event Hubs
Correct Answer: B
Stream Analytics supports Azure Blob storage and Azure SQL Database as the storage layer for Reference Data.
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-use-reference-data
B (100%)
Question #32
You are designing an Azure Stream Analytics job to process incoming events from sensors in retail environments.
You need to process the events to produce a running average of shopper counts during the previous 15 minutes, calculated at five-minute intervals.
Which type of window should you use?
- A. snapshot
- B. tumbling
- C. hopping Most Voted
- D. sliding
Correct Answer: C
Unlike tumbling windows, hopping windows model scheduled overlapping windows. A hopping window specification consist of three parameters: the timeunit, the windowsize (how long each window lasts) and the hopsize (by how much each window moves forward relative to the previous one).
Reference:
https://docs.microsoft.com/en-us/stream-analytics-query/hopping-window-azure-stream-analytics
C (100%)
Question #33
HOTSPOT –
You are designing a monitoring solution for a fleet of 500 vehicles. Each vehicle has a GPS tracking device that sends data to an Azure event hub once per minute.
You have a CSV file in an Azure Data Lake Storage Gen2 container. The file maintains the expected geographical area in which each vehicle should be.
You need to ensure that when a GPS position is outside the expected area, a message is added to another event hub for processing within 30 seconds. The solution must minimize cost.
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.
Hot Area:
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
Question #34
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?
- A. Partition by DateTime fields.
- B. Sink to Azure Queue storage. Most Voted
- C. Include a watermark column.
- D. Use a JSON format for physical data storage.
Correct Answer: B
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
B (100%)
Question #35
HOTSPOT –
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:
Correct Answer: 
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
Question #36
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?
- A. Enable container services for workspace1.
- B. Upgrade workspace1 to the Premium pricing tier. Most Voted
- C. Set Cluster Mode to High Concurrency.
- D. Create a cluster policy in workspace1.
Correct Answer: B
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
B (100%)
Question #37
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?
- A. Yes
- B. No
Correct Answer: A
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
A (100%)
Question #38
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 session window that uses a timeout size of 10 seconds.
Does this meet the goal?
- A. Yes
- B. No
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
B (100%)
Question #39
You use Azure Stream Analytics to receive data from Azure Event Hubs and to output the data to an Azure Blob Storage account.
You need to output the count of records received from the last five minutes every minute.
Which windowing function should you use?
- A. Session
- B. Tumbling
- C. Sliding
- D. Hopping Most Voted
Correct Answer:D
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.
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions
D (100%)
Question #40
HOTSPOT –
You configure version control for an Azure Data Factory instance as shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Hot Area:
Correct Answer:
Box 1: adf_publish –
The Publish branch is the branch in your repository where publishing related ARM templates are stored and updated. By default, it’s adf_publish.
Box 2: / dwh_batchetl/adf_publish/contososales
Note: RepositoryName (here dwh_batchetl): Your Azure Repos code repository name. Azure Repos projects contain Git repositories to manage your source code as your project grows. You can create a new repository or use an existing repository that’s already in your project.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/source-control
