DP-203 試験問題を無料オンラインアクセス

You have an Azure Synapse Analytics dedicated SQL pool named Pcol1. Pool1 contains a table named tablet You load 5 TB of data into table1.
You need to ensure that column store compression is maximized for table1.
Which statement should you execute?

• A.DBCC DBREINOEX (table)
• B.ALTER INDEX ALL on table REORGANIZE
• C.ALTER INDEX ALL on table REBUILD
• D.DBCC IIDEXDEFRAG (pool1, table1)

You have an Azure Synapse Analytics dedicated SQL Pool1. Pool1 contains a partitioned fact table named dbo.Sales and a staging table named stg.Sales that has the matching table and partition definitions.
You need to overwrite the content of the first partition in dbo.Sales with the content of the same partition in stg.Sales. The solution must minimize load times.
What should you do?

• A.Switch the first partition from stg.Sales to dbo. Sales.
• B.Switch the first partition from dbo.Sales to stg.Sales.
• C.Insert the data from stg.Sales into dbo.Sales.
• D.Update dbo.Sales from stg.Sales.

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.

正解：

Explanation:

Box 1: Yes
A cluster mode of 'High Concurrency' is selected, unlike all the others which are 'Standard'. This results in a worker type of Standard_DS13_v2.
Box 2: No
When you run a job on a new cluster, the job is treated as a data engineering (job) workload subject to the job workload pricing. When you run a job on an existing cluster, the job is treated as a data analytics (all-purpose) workload subject to all-purpose workload pricing.
Box 3: Yes
Delta Lake on Databricks allows you to configure Delta Lake based on your workload patterns.
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

You need to create an Azure Data Factory pipeline to process data for the following three departments at your company: Ecommerce, retail, and wholesale. The solution must ensure that data can also be processed for the entire company.
How should you complete the Data Factory data flow script? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

正解：

Explanation:

The conditional split transformation routes data rows to different streams based on matching conditions. The conditional split transformation is similar to a CASE decision structure in a programming language. The transformation evaluates expressions, and based on the results, directs the data row to the specified stream.
Box 1: dept=='ecommerce', dept=='retail', dept=='wholesale'
First we put the condition. The order must match the stream labeling we define in Box 3.
Syntax:
<incomingStream>
split(
<conditionalExpression1>
<conditionalExpression2>
disjoint: {true | false}
) ~> <splitTx>@(stream1, stream2, ..., <defaultStream>)
Box 2: discount : false
disjoint is false because the data goes to the first matching condition. All remaining rows matching the third condition go to output stream all.
Box 3: ecommerce, retail, wholesale, all
Label the streams
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/data-flow-conditional-split

You have an Azure Stream Analytics job that read data from an Azure event hub.
You need to evaluate whether the job processes data as quickly as the data arrives or cannot keep up.
Which metric should you review?

• A.Backlogged input Events
• B.InputEventLastPunctuationTime
• C.Input Sources Receive
• D.Late input Events