Free DP-203 Exam Braindumps (page: 53)

Page 53 of 94

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You have an Azure Synapse Analytics pipeline named Pipeline1 that contains a data flow activity named Dataflow1.

Pipeline1 retrieves files from an Azure Data Lake Storage Gen 2 account named storage1.
Dataflow1 uses the AutoResolveIntegrationRuntime integration runtime configured with a core count of 128.

You need to optimize the number of cores used by Dataflow1 to accommodate the size of the files in storage1.

What should you configure? To answer, select the appropriate options in the answer area.

  1. See Explanation section for answer.

Answer(s): A

Explanation:



Box 1: A Get Metadata activity
Dynamically size data flow compute at runtime
The Core Count and Compute Type properties can be set dynamically to adjust to the size of your incoming source data at runtime. Use pipeline activities like Lookup or Get Metadata in order to find the size of the source dataset data. Then, use Add Dynamic Content in the Data Flow activity properties.

Box 2: Dynamic content


Reference:

https://docs.microsoft.com/en-us/azure/data-factory/control-flow-execute-data-flow-activity



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

  1. Yes
  2. No

Answer(s): A



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 High Concurrency 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



You are designing a folder structure for the files in an Azure Data Lake Storage Gen2 account. The account has one container that contains three years of data.

You need to recommend a folder structure that meets the following requirements:

-Supports partition elimination for queries by Azure Synapse Analytics serverless SQL pools
-Supports fast data retrieval for data from the current month
-Simplifies data security management by department

Which folder structure should you recommend?

  1. \Department\DataSource\YYYY\MM\DataFile_YYYYMMDD.parquet
  2. \DataSource\Department\YYYYMM\DataFile_YYYYMMDD.parquet
  3. \DD\MM\YYYY\Department\DataSource\DataFile_DDMMYY.parquet
  4. \YYYY\MM\DD\Department\DataSource\DataFile_YYYYMMDparquet

Answer(s): A

Explanation:

Department top level in the hierarchy to simplify security management.
Month (MM) at the leaf/bottom level to support fast data retrieval for data from the current month.



Page 53 of 94



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