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Free DP-203 Exam Braindumps (page: 42)

Page 42 of 94
PDF with 370 Questions

HOTSPOT (Drag and Drop is not supported)
You have an Azure Data Factory instance named ADF1 and two Azure Synapse Analytics workspaces named WS1 and WS2.

ADF1 contains the following pipelines:
-P1: Uses a copy activity to copy data from a nonpartitioned table in a dedicated SQL pool of WS1 to an Azure Data Lake Storage Gen2 account
-P2: Uses a copy activity to copy data from text-delimited files in an Azure Data Lake Storage Gen2 account to a nonpartitioned table in a dedicated SQL pool of WS2

You need to configure P1 and P2 to maximize parallelism and performance.
Which dataset settings should you configure for the copy activity if each pipeline? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:


Box 1: Set the Copy method to PolyBase
While SQL pool supports many loading methods including non-Polybase options such as BCP and SQL BulkCopy API, the fastest and most scalable way to load data is through PolyBase. PolyBase is a technology that accesses external data stored in Azure Blob storage or Azure Data Lake Store via the T-SQL language.
Box 2: Set the Copy method to Bulk insert
Polybase not possible for text files. Have to use Bulk insert.


Reference:

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/load-data-overview



HOTSPOT (Drag and Drop is not supported)
You have an Azure Storage account that generates 200,000 new files daily. The file names have a format of {YYYY}/{MM}/{DD}/{HH}/{CustomerID}.csv.
You need to design an Azure Data Factory solution that will load new data from the storage account to an Azure Data Lake once hourly. The solution must minimize load times and costs.

How should you configure the solution? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Hot Area:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Box 1: Incremental load
Box 2: Tumbling window
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



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 plan to create an Azure Databricks workspace that has a tiered structure. The workspace will contain the following three workloads:
-A workload for data engineers who will use Python and SQL.
-A workload for jobs that will run notebooks that use Python, Scala, and SQL.
-A workload that data scientists will use to perform ad hoc analysis in Scala and R.

The enterprise architecture team at your company identifies the following standards for Databricks environments:
-The data engineers must share a cluster.
-The job cluster will be managed by using a request process whereby data scientists and data engineers provide packaged notebooks for deployment to the cluster.
-All the data scientists must be assigned their own cluster that terminates automatically after 120 minutes of inactivity. Currently, there are three data scientists.
-You need to create the Databricks clusters for the workloads.

Solution: You create a Standard cluster for each data scientist, a Standard cluster for the data engineers, and a High Concurrency cluster for the jobs.
Does this meet the goal?

  1. Yes
  2. No

Answer(s): B

Explanation:

We need a High Concurrency cluster for the data engineers and the jobs.
Note: Standard clusters are recommended for a single user. Standard can run workloads developed in any language: Python, R, Scala, and SQL.
A high concurrency cluster is a managed cloud resource. The key benefits of high concurrency clusters are that they provide Apache Spark-native fine-grained sharing for maximum resource utilization and minimum query latencies.


Reference:

https://docs.azuredatabricks.net/clusters/configure.html



You have the following Azure Data Factory pipelines:
-Ingest Data from System1
-Ingest Data from System2
-Populate Dimensions
-Populate Facts
-Ingest Data from System1 and Ingest Data from System2 have no dependencies.
-Populate Dimensions must execute after Ingest Data from System1 and Ingest Data from System2.
-Populate Facts must execute after Populate Dimensions pipeline.
-All the pipelines must execute every eight hours.

What should you do to schedule the pipelines for execution?

  1. Add an event trigger to all four pipelines.
  2. Add a schedule trigger to all four pipelines.
  3. Create a patient pipeline that contains the four pipelines and use a schedule trigger.
  4. Create a patient pipeline that contains the four pipelines and use an event trigger.

Answer(s): C

Explanation:

Schedule trigger: A trigger that invokes a pipeline on a wall-clock schedule.


Reference:

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



Page 42 of 94



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