DP-200 – Implementing an Azure Data Solution (beta)

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Showing 7–9 of 15 questions

Question 7 (Mixed Questions)

You have JSON files stored in an Azure Data Lake Storage Gen2 account. The JSON file contains the FirstName and LastName of customers. You need to use Azure Data bricks to copy the data in the JSON files to an Azure data warehouse. A new column must be created which concatenates the FirstName and LastName values.

You have the following components in place in Azure:

- A destination table in the SQL Data Warehouse

- An Azure Blob storage container

- A service principal

Which of the following are actions you would perform to transfer the data onto the Azure SQL Data warehouse table? (Choose five.)

Select all that apply, then click Submit answer.

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Write the results onto Azure Data Lake Storage
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Drop the data frame
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Perform transformations on the data frame
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Mount the Data Lake Storage onto DBFS
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Perform transformations on the file
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Read the file into a data frame
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Specify a temporary folder to stage the data
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Write the results to a table in SQL Data Warehouse

Reference / correct answer:

Perform transformations on the data frame

Mount the Data Lake Storage onto DBFS

Read the file into a data frame

Specify a temporary folder to stage the data

Write the results to a table in SQL Data Warehouse

To Connect to Azure Data Lake Gen2 storage, we first need to mount the file system. This is also given in the Azure data bricks documentation:

Azure Data Lake Storage Gen2

Azure Data Lake Storage Gen2 (also known as ADLS Gen2) is a next-generation data lake solution for big data analytics. Azure Data Lake Storage Gen2 builds Azure Data Lake Storage Gen1 capabilities – file system semantics, file-level security, and scale – into Azure Blob storage, with its low-cost tiered storage, high availability, and disaster recovery features.

Note:

The Azure Data Lake Storage Gen2 connector is supported in Databricks Runtime 5.2 and above with full support for Delta Lake in Databricks Runtime 5.5 and above.

There are three ways of accessing Azure Data Lake Storage Gen2:

1. Mount an Azure Data Lake Storage Gen2 filesystem to DBFS using a service principal and OAuth 2.0.

2. Use a service principal directly.

3. Use the Azure Data Lake Storage Gen2 storage account access key directly.

Next, we need to load the data from the Azure Data Lake Storage Gen2 account. An example is also given in the Microsoft documentation:

Extract data from the Azure Data Lake Storage Gen2 account

1. You can now load the sample json file as a data frame in Azure Databricks. Paste the following code in a new cell. Replace the placeholders shown in brackets with your values.

Next, we need to transform the data in the data frame to combine the FirstName and LastName values.

You need to mention an Azure storage account as a temporary staging area.

Load data into Azure SQL Data Warehouse

In this section, you upload the transformed data into Azure SQL Data Warehouse. You use the Azure SQL Data Warehouse connector for Azure Databricks to directly upload a dataframe as a table in a SQL data warehouse.

As mentioned earlier, the SQL Data Warehouse connector uses Azure Blob storage as temporary storage to upload data between Azure Databricks and Azure SQL Data Warehouse. So, you start by providing the configuration to connect to the storage account. You must already have already created the account as part of the prerequisites for this article.

And then finally you copy the data onto the Azure SQL data warehouse.

Option “Write the results onto Azure Data Lake Storage” is incorrect since you don’t need to write the results onto Azure Data Lake storage. The results are written to the Azure SQL Data warehouse table. Option “Drop the data frame” is incorrect since we don’t need to drop the data frames.

Option “Perform transformations on the file” is incorrect since transformations need to be carried out on the data frames. Reference:

https://docs.microsoft.com/en-us/azure/azure-databricks/databricks-extract-load-sql-data-warehouse#load-data-into-azure-sql-data-warehouse

Question 8 (Mixed Questions)

You need to enable Transparent Data Encryption for an Azure SQL database.

Which of the following steps would you perform for this requirement? (Choose five.)

Select all that apply, then click Submit answer.

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Create a database encryption key using a certificate
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Create a certificate protected by the master key
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Set the context to the master database
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Create a master key using a password
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Set the context to the company database
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Enable Encryption

Reference / correct answer:

Create a database encryption key using a certificate

Create a certificate protected by the master key

Create a master key using a password

Set the context to the company database

Enable Encryption

The list of steps is given in the Microsoft documentation:

Using Transparent Data Encryption To use TDE, follow these steps. Applies to: SQL Server.

• Create a master key

• Create or obtain a certificate protected by the master key

• Create a database encryption key and protect it by the certificate

• Set the database to use encryption

The following example illustrates encrypting and decrypting the AdventureWorks2012 database using a certificate installed on the server named MyServerCert.

Since this is clearly mentioned in the Microsoft documentation, all other options are incorrect. Reference:

https://docs.microsoft.com/en-us/sql/relational-databases/security/encryption/transparent-data-encryption?view=sql-server-ver15

Question 9 (Mixed Questions)

You are the data engineer for your company. An application uses a NoSQL database to store data. The database uses the key-value and wide-column NoSQL database type.

Developers need to access data in the database using an API.

You need to determine which API to use for the database model and type.

Which two APIs should you use? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

Select all that apply, then click Submit answer.

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Table API
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MongoDB API
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Gremlin API
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SQL API
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Cassandra API

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