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Download Data Engineering on Microsoft Azure Exam Dumps
NEW QUESTION 50
The following code segment is used to create an Azure Databricks cluster.
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:
Reference:
https://adatis.co.uk/databricks-cluster-sizing/
https://docs.microsoft.com/en-us/azure/databricks/jobs
https://docs.databricks.com/administration-guide/capacity-planning/cmbp.html
https://docs.databricks.com/delta/index.html
NEW QUESTION 51
You have two Azure Data Factory instances named ADFdev and ADFprod. ADFdev connects to an Azure DevOps Git repository.
You publish changes from the main branch of the Git repository to ADFdev.
You need to deploy the artifacts from ADFdev to ADFprod.
What should you do first?
- A. From Azure DevOps, update the main branch.
- B. From ADFdev, create a linked service.
- C. From ADFdev, modify the Git configuration.
- D. From Azure DevOps, create a release pipeline.
Answer: D
Explanation:
Explanation
In Azure Data Factory, continuous integration and delivery (CI/CD) means moving Data Factory pipelines from one environment (development, test, production) to another.
Note:
The following is a guide for setting up an Azure Pipelines release that automates the deployment of a data factory to multiple environments.
* In Azure DevOps, open the project that's configured with your data factory.
* On the left side of the page, select Pipelines, and then select Releases.
* Select New pipeline, or, if you have existing pipelines, select New and then New release pipeline.
* In the Stage name box, enter the name of your environment.
* Select Add artifact, and then select the git repository configured with your development data factory.
Select the publish branch of the repository for the Default branch. By default, this publish branch is adf_publish.
* Select the Empty job template.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/continuous-integration-deployment
NEW QUESTION 52
You are designing an inventory updates table in an Azure Synapse Analytics dedicated SQL pool. The table will have a clustered columnstore index and will include the following columns:
* EventDate: 1 million per day
* EventTypelD: 10 million per event type
* WarehouselD: 100 million per warehouse
* ProductCategoryTypeiD: 25 million per product category type
You identify the following usage patterns:
Analyst will most commonly analyze transactions for a warehouse.
Queries will summarize by product category type, date, and/or inventory event type.
You need to recommend a partition strategy for the table to minimize query times.
On which column should you recommend partitioning the table?
- A. EventTypeID
- B. WarehouseID
- C. ProductCategoryTypeID
- D. EventDate
Answer: A
NEW QUESTION 53
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.
Answer:
Explanation:
Explanation
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
NEW QUESTION 54
You are designing a highly available Azure Data Lake Storage solution that will induce geo-zone-redundant storage (GZRS).
You need to monitor for replication delays that can affect the recovery point objective (RPO).
What should you include m the monitoring solution?
- A. availability
- B. Error errors
- C. Average Success Latency
- D. Last Sync Time
Answer: D
Explanation:
Explanation
Because geo-replication is asynchronous, it is possible that data written to the primary region has not yet been written to the secondary region at the time an outage occurs. The Last Sync Time property indicates the last time that data from the primary region was written successfully to the secondary region. All writes made to the primary region before the last sync time are available to be read from the secondary location. Writes made to the primary region after the last sync time property may or may not be available for reads yet.
Reference:
https://docs.microsoft.com/en-us/azure/storage/common/last-sync-time-get
NEW QUESTION 55
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