DP-203 Dumps Questions With Valid Answers

You need to collect application metrics, streaming query events, and application log
messages for an Azure Databrick cluster.
Which type of library and workspace should you implement? To answer, select the
appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

You plan to create an Azure Data Factory pipeline that will include a mapping data flow.
You have JSON data containing objects that have nested arrays.
You need to transform the JSON-formatted data into a tabular dataset. The dataset must
have one tow for each item in the arrays.
Which transformation method should you use in the mapping data flow?

You have a SQL pool in Azure Synapse.
You plan to load data from Azure Blob storage to a staging table. Approximately 1 million
rows of data will be loaded daily. The table will be truncated before each daily load.
You need to create the staging table. The solution must minimize how long it takes to load
the data to the staging table.
How should you configure the table? To answer, select the appropriate options in the
answer area.
NOTE: Each correct selection is worth one point.

The storage account container view is shown in the Refdata exhibit. (Click the Refdata tab.)
You need to configure the Stream Analytics job to pick up the new reference data. What
should you configure? To answer, select the appropriate options in the answer area NOTE:
Each correct selection is worth one point.

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 SOL.
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 High Concurrency cluster
for the data engineers, and a Standard cluster for the jobs.
Does this meet the goal?