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Download DemoVendor: Microsoft
Certifications: Microsoft Certified: Azure Data Engineer Associate
Exam Code: DP-203
Exam Name: Data Engineering on Microsoft Azure
Updated: Mar 12, 2024
Q&As: 380
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DRAG DROP
You are responsible for providing access to an Azure Data Lake Storage Gen2 account.
Your user account has contributor access to the storage account, and you have the application ID and access key.
You plan to use PolyBase to load data into an enterprise data warehouse in Azure Synapse Analytics.
You need to configure PolyBase to connect the data warehouse to storage account.
Which three components should you create in sequence? To answer, move the appropriate components from the list of components to the answer area and arrange them in the correct order.
Select and Place:
Correct Answer:
Step 1: a database scoped credential
To access your Data Lake Storage account, you will need to create a Database Master Key to encrypt your credential secret used in the next step. You then create a database scoped credential.
Step 2: an external data source Create the external data source. Use the CREATE EXTERNAL DATA SOURCE command to store the location of the data. Provide the credential created in the previous step.
Step 3: an external file format Configure data format: To import the data from Data Lake Storage, you need to specify the External File Format. This object defines how the files are written in
Data Lake Storage.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-load-from-azure-data-lake-store
You have two Azure Blob Storage accounts named account1 and account2.
You plan to create an Azure Data Factory pipeline that will use scheduled intervals to replicate newly created or modified blobs from account1 to account2.
You need to recommend a solution to implement the pipeline. The solution must meet the following requirements:
1.
Ensure that the pipeline only copies blobs that were created or modified since the most recent replication event.
2.
Minimize the effort to create the pipeline. What should you recommend?
A. Run the Copy Data tool and select Metadata-driven copy task.
B. Create a pipeline that contains a Data Flow activity.
C. Create a pipeline that contains a flowlet.
D. Run the Copy Data tool and select Built-in copy task.
Correct Answer: A
Build large-scale data copy pipelines with metadata-driven approach in copy data tool
When you want to copy huge amounts of objects (for example, thousands of tables) or load data from large variety of sources, the appropriate approach is to input the name list of the objects with required copy behaviors in a control table,
and then use parameterized pipelines to read the same from the control table and apply them to the jobs accordingly. By doing so, you can maintain (for example, add/remove) the objects list to be copied easily by just updating the object
names in control table instead of redeploying the pipelines. What's more, you will have single place to easily check which objects copied by which pipelines/triggers with defined copy behaviors.
Copy data tool in ADF eases the journey of building such metadata driven data copy pipelines. After you go through an intuitive flow from a wizard-based experience, the tool can generate parameterized pipelines and SQL scripts for you to
create external control tables accordingly. After you run the generated scripts to create the control table in your SQL database, your pipelines will read the metadata from the control table and apply them on the copy jobs automatically.
Incorrect:
Not C: A flowlet is a reusable container of activities that can be created from an existing mapping data flow or started from scratch. By reusing patterns you can prevent logic duplication and apply the same logic across many mapping data
flows.
With flowlets you can create logic to do things such as address cleaning or string trimming. You can then map the input and outputs to columns in the calling data flow for a dynamic code reuse experience.
Reference:
https://learn.microsoft.com/en-us/azure/data-factory/copy-data-tool-metadata-driven
You have an Azure Synapse Analytics workspace that contains an Apache Spark pool named SparkPool1. SparkPool1 contains a Delta Lake table named SparkTable1.
You need to recommend a solution that supports Transact-SQL queries against the data referenced by SparkTable1. The solution must ensure that the queries can use partition elimination.
What should you include in the recommendation?
A. a partitioned table in a dedicated SQL pool
B. a partitioned view in a dedicated SQL pool
C. a partitioned index in a dedicated SQL pool
D. a partitioned view in a serverless SQL pool
Correct Answer:
Explanation:
Delta Lake
There are some limitations that you might see in Delta Lake support in serverless SQL pools:
*
External tables don't support partitioning. Use partitioned views on the Delta Lake folder to use the partition elimination.
*
Etc.
Note: Partitioned views
If you have a set of files that is partitioned in the hierarchical folder structure, you can describe the partition pattern using the wildcards in the file path.
Partitioned views can improve the performance of your queries by performing partition elimination when you query them with filters on the partitioning columns. However, not all queries support partition elimination, so it's important to follow
some best practices.
Delta Lake partitioned views
If you are creating the partitioned views on top of Delta Lake storage, you can specify just a root Delta Lake folder and don't need to explicitly expose the partitioning columns using the FILEPATH function.
Reference: https://learn.microsoft.com/en-us/azure/synapse-analytics/sql/resources-self-help-sql-on-demand https://learn.microsoft.com/en-us/azure/synapse-analytics/sql/create-use-views
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