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  3. DP-300

Microsoft DP-300 Exam (page: 12)
Microsoft Administering Azure SQL Solutions
Updated on: 05-Aug-2025

Viewing Page 12 of 61
DP-300 PDF 406 Questions

You have an Azure Data Factory pipeline that performs an incremental load of source data to an Azure Data Lake Storage Gen2 account.

Data to be loaded is identified by a column named LastUpdatedDate in the source table. You plan to execute the pipeline every four hours.
You need to ensure that the pipeline execution meets the following requirements:

Automatically retries the execution when the pipeline run fails due to concurrency or throttling limits. Supports backfilling existing data in the table.

Which type of trigger should you use?

  1. tumbling window
  2. on-demand
  3. event
  4. schedule

Answer(s): A

Explanation:

The Tumbling window trigger supports backfill scenarios. Pipeline runs can be scheduled for windows in the past.

Incorrect Answers:
D: Schedule trigger does not support backfill scenarios. Pipeline runs can be executed only on time periods from the current time and the future.


Reference:

https://docs.microsoft.com/en-us/azure/data-factory/concepts-pipeline-execution-triggers



You have an Azure Data Factory that contains 10 pipelines.

You need to label each pipeline with its main purpose of either ingest, transform, or load. The labels must be available for grouping and filtering when using the monitoring experience in Data Factory.

What should you add to each pipeline?

  1. an annotation
  2. a resource tag
  3. a run group ID
  4. a user property
  5. a correlation ID

Answer(s): A

Explanation:

Azure Data Factory annotations help you easily filter different Azure Data Factory objects based on a tag. You can define tags so you can see their performance or find errors faster.


Reference:

https://www.techtalkcorner.com/monitor-azure-data-factory-annotations/



HOTSPOT (Drag and Drop is not supported)
You have an Azure data factory that has two pipelines named PipelineA and PipelineB. PipelineA has four activities as shown in the following exhibit.


PipelineB has two activities as shown in the following exhibit.


You create an alert for the data factory that uses Failed pipeline runs metrics for both pipelines and all failure types. The metric has the following settings:

-Operator: Greater than
-Aggregation type: Total
-Threshold value: 2
-Aggregation granularity (Period): 5 minutes
-Frequency of evaluation: Every 5 minutes

Data Factory monitoring records the failures shown in the following table.


For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Box 1: No
Just one failure within the 5-minute interval.

Box 2: No
Just two failures within the 5-minute interval.

Box 3: No
Just two failures within the 5-minute interval.


Reference:

https://docs.microsoft.com/en-us/azure/azure-monitor/alerts/alerts-metric-overview



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 have an Azure Data Lake Storage account that contains a staging zone.

You need to design a daily process to ingest incremental data from the staging zone, transform the data by executing an R script, and then insert the transformed data into a data warehouse in Azure Synapse Analytics.

Solution: You use an Azure Data Factory schedule trigger to execute a pipeline that executes mapping data flow, and then inserts the data into the data warehouse.

Does this meet the goal?

  1. Yes
  2. No

Answer(s): B

Explanation:

If you need to transform data in a way that is not supported by Data Factory, you can create a custom activity, not a mapping flow,5 with your own data processing logic and use the activity in the pipeline. You can create a custom activity to run R scripts on your HDInsight cluster with R installed.


Reference:

https://docs.microsoft.com/en-US/azure/data-factory/transform-data



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 have an Azure Data Lake Storage account that contains a staging zone.

You need to design a daily process to ingest incremental data from the staging zone, transform the data by executing an R script, and then insert the transformed data into a data warehouse in Azure Synapse Analytics.

Solution: You schedule an Azure Databricks job that executes an R notebook, and then inserts the data into the data warehouse.

Does this meet the goal?

  1. Yes
  2. No

Answer(s): B

Explanation:

Must use an Azure Data Factory, not an Azure Databricks job.


Reference:

https://docs.microsoft.com/en-US/azure/data-factory/transform-data



Viewing Page 12 of 61



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