ExamGecko
Home Home / Microsoft / DP-300

Microsoft DP-300 Practice Test - Questions Answers, Page 18

Question list
Search
Search

List of questions

Search

Related questions











You have a SQL pool in Azure Synapse that contains a table named dbo.Customers. The table contains a column name Email. You need to prevent nonadministrative users from seeing the full email addresses in the Email column. The users must see values in a format of [email protected] instead. What should you do?

A.
From the Azure portal, set a mask on the Email column.
A.
From the Azure portal, set a mask on the Email column.
Answers
B.
From the Azure portal, set a sensitivity classification of Confidential for the Email column.
B.
From the Azure portal, set a sensitivity classification of Confidential for the Email column.
Answers
C.
From Microsoft SQL Server Management Studio, set an email mask on the Email column.
C.
From Microsoft SQL Server Management Studio, set an email mask on the Email column.
Answers
D.
From Microsoft SQL Server Management Studio, grant the SELECT permission to the users for all the columns in the dbo.Customers table except Email.
D.
From Microsoft SQL Server Management Studio, grant the SELECT permission to the users for all the columns in the dbo.Customers table except Email.
Answers
Suggested answer: B

Explanation:

The Email masking method, which exposes the first letter and replaces the domain with XXX.com using a constant string prefix in the form of an email address. Example: [email protected]

You have an Azure Databricks workspace named workspace1 in the Standard pricing tier. Workspace1 contains an all-purpose cluster named cluster1. You need to reduce the time it takes for cluster1 to start and scale up. The solution must minimize costs. What should you do first?

A.
Upgrade workspace1 to the Premium pricing tier.
A.
Upgrade workspace1 to the Premium pricing tier.
Answers
B.
Configure a global init script for workspace1.
B.
Configure a global init script for workspace1.
Answers
C.
Create a pool in workspace1.
C.
Create a pool in workspace1.
Answers
D.
Create a cluster policy in workspace1.
D.
Create a cluster policy in workspace1.
Answers
Suggested answer: C

Explanation:

You can use Databricks Pools to Speed up your Data Pipelines and Scale Clusters Quickly.

Databricks Pools, a managed cache of virtual machine instances that enables clusters to start and scale 4 times faster.

Reference:

https://databricks.com/blog/2019/11/11/databricks-pools-speed-up-data-pipelines.html

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 have an Azure Synapse Analytics dedicated SQL pool that contains a table named Table1.

You have files that are ingested and loaded into an Azure Data Lake Storage Gen2 container named container1. You plan to insert data from the files into Table1 and transform the data. Each row of data in the files will produce one row in the serving layer of Table1. You need to ensure that when the source data files are loaded to container1, the DateTime is stored as an additional column in Table1. Solution: In an Azure Synapse Analytics pipeline, you use a Get Metadata activity that retrieves the DateTime of the files. Does this meet the goal?

A.
Yes
A.
Yes
Answers
B.
No
B.
No
Answers
Suggested answer: B

Explanation:

Instead use a serverless SQL pool to create an external table with the extra column.

Reference:

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/create-use-external-tables

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 have an Azure Synapse Analytics dedicated SQL pool that contains a table named Table1.

You have files that are ingested and loaded into an Azure Data Lake Storage Gen2 container named container1. You plan to insert data from the files into Table1 and transform the data. Each row of data in the files will produce one row in the serving layer of Table1. You need to ensure that when the source data files are loaded to container1, the DateTime is stored as an additional column in Table1. Solution: You use an Azure Synapse Analytics serverless SQL pool to create an external table that has an additional DateTime column. Does this meet the goal?

A.
Yes
A.
Yes
Answers
B.
No
B.
No
Answers
Suggested answer: A

Explanation:

In dedicated SQL pools you can only use Parquet native external tables. Native external tables are generally available in serverless SQL pools.

Reference:

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/create-use-external-tables

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 have an Azure Synapse Analytics dedicated SQL pool that contains a table named Table1.

You have files that are ingested and loaded into an Azure Data Lake Storage Gen2 container named container1.

You plan to insert data from the files into Table1 and transform the data. Each row of data in the files will produce one row in the serving layer of Table1.

You need to ensure that when the source data files are loaded to container1, the DateTime is stored as an additional column in Table1.

Solution: You use a dedicated SQL pool to create an external table that has an additional DateTime column.

Does this meet the goal?

A.
Yes
A.
Yes
Answers
B.
No
B.
No
Answers
Suggested answer: B

Explanation:

Instead use a serverless SQL pool to create an external table with the extra column.

Note: In dedicated SQL pools you can only use Parquet native external tables. Native external tables are generally available in serverless SQL pools.

Reference:

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/create-use-external-tables

HOTSPOT

You have an Azure Synapse Analytics dedicated SQL pool named Pool1 and an Azure Data Lake Storage Gen2 account named Account1.

You plan to access the files in Account1 by using an external table.

You need to create a data source in Pool1 that you can reference when you create the external table.

How should you complete the Transact-SQL statement? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.


Question 176
Correct answer: Question 176

Explanation:

Box 1: blob

The following example creates an external data source for Azure Data Lake Gen2

CREATE EXTERNAL DATA SOURCE YellowTaxi

WITH ( LOCATION = 'https://azureopendatastorage.blob.core.windows.net/nyctlc/yellow/',

TYPE = HADOOP)

Box 2: HADOOP

Reference:

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/develop-tables-external-tables

HOTSPOT

You plan to develop a dataset named Purchases by using Azure Databricks. Purchases will contain the following columns:

ProductID

ItemPrice

LineTotal

Quantity

StoreID

Minute

Month

Hour

Year

Day

You need to store the data to support hourly incremental load pipelines that will vary for each StoreID. The solution must minimize storage costs.

How should you complete the code? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.


Question 177
Correct answer: Question 177

Explanation:

Box 1: .partitionBy

Example:

df.write.partitionBy("y","m","d")

.mode(SaveMode.Append)

.parquet("/data/hive/warehouse/db_name.db/" + tableName)

Box 2: ("Year","Month","Day","Hour","StoreID")

Box 3: .parquet("/Purchases")

Reference:

https://intellipaat.com/community/11744/how-to-partition-and-write-dataframe-in-spark-without-deleting-partitions-with-no-new-data

Question 178
Correct answer: Question 178

Explanation:

Box 1: CREATE EXTERNAL TABLE

An external table points to data located in Hadoop, Azure Storage blob, or Azure Data Lake Storage. External tables are used to read data from files or write data to files in Azure Storage. With Synapse SQL, you can use external tables to read external data using dedicated SQL pool or serverless SQL pool.

Syntax:

CREATE EXTERNAL TABLE { database_name.schema_name.table_name | schema_name.table_name | table_name } ( <column_definition> [ ,...n ] )

WITH (

LOCATION = 'folder_or_filepath',

DATA_SOURCE = external_data_source_name,

FILE_FORMAT = external_file_format_name

Box 2. OPENROWSET

When using serverless SQL pool, CETAS is used to create an external table and export query results to Azure Storage Blob or Azure Data Lake Storage Gen2.

Example:

AS

SELECT decennialTime, stateName, SUM(population) AS population

FROM

OPENROWSET(BULK 'https://azureopendatastorage.blob.core.windows.net/censusdatacontainer/release/us_population_county/year=*/*.parquet', FORMAT='PARQUET') AS [r]

GROUP BY decennialTime, stateName

GO

Reference:

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/develop-tables-external-tables

Question 179
Correct answer: Question 179

Explanation:

Box 1: FactEvents

Fact tables store observations or events, and can be sales orders, stock balances, exchange rates, temperatures, etc.

Box 2: DimChannel

Dimension tables describe business entities – the things you model. Entities can include products, people, places, and concepts including time itself. The most consistent table you'll find in a star schema is a date dimension table. A dimension table contains a key column (or columns) that acts as a unique identifier, and descriptive columns.

Box 3: DimEvent

Reference:

https://docs.microsoft.com/en-us/power-bi/guidance/star-schema

DRAG DROP

You plan to create a table in an Azure Synapse Analytics dedicated SQL pool.

Data in the table will be retained for five years. Once a year, data that is older than five years will be deleted.

You need to ensure that the data is distributed evenly across partitions. The solutions must minimize the amount of time required to delete old data.

How should you complete the Transact-SQL statement? 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.


Question 180
Correct answer: Question 180

Explanation:

Box 1: HASH

Box 2: OrderDateKey

In most cases, table partitions are created on a date column.

A way to eliminate rollbacks is to use Metadata Only operations like partition switching for data management. For example, rather than execute a DELETE statement to delete all rows in a table where the order_date was in October of 2001, you could partition your data early. Then you can switch out the partition with data for an empty partition from another table.

Reference:

https://docs.microsoft.com/en-us/sql/t-sql/statements/create-table-azure-sql-data-warehouse

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/best-practices-dedicated-sql-pool

Total 338 questions
Go to page: of 34