Try Free Microsoft DP-203 Updated Questions & Answers

Question 1

You are designing an enterprise data warehouse in Azure Synapse Analytics that will contain a table named Customers. Customers will contain credit card information.

You need to recommend a solution to provide salespeople with the ability to view all the entries in Customers.

The solution must prevent all the salespeople from viewing or inferring the credit card information.

What should you include in the recommendation?

Adata masking

BAlways Encrypted

Ccolumn-level security

Drow-level security

Correct answer: A

SQL Database dynamic data masking limits sensitive data exposure by masking it to non-privileged users.

The Credit card masking method exposes the last four digits of the designated fields and adds a constant string as a prefix in the form of a credit card.

Example: XXXX-XXXX-XXXX-1234

https://docs.microsoft.com/en-us/azure/sql-database/sql-database-dynamic-data-masking-get-started

Question 2

You have an Azure Data Lake Storage Gen2 account that contains two folders named Folder and Folder2.

You use Azure Data Factory to copy multiple files from Folder1 to Folder2.

You receive the following error.

What should you do to resolve the error.

AAdd an explicit mapping.

BEnable fault tolerance to skip incompatible rows.

CLower the degree of copy parallelism

DChange the Copy activity setting to Binary Copy

Correct answer: A

Question 3

You have two fact tables named Flight and Weather. Queries targeting the tables will be based on the join between the following columns.

You need to recommend a solution that maximizes query performance.

What should you include in the recommendation?

AIn the tables use a hash distribution of ArrivalDateTime and ReportDateTime.

BIn the tables use a hash distribution of ArrivaIAirportID and AirportlD.

CIn each table, create an identity column.

DIn each table, create a column as a composite of the other two columns in the table.

Correct answer: B

Hash-distribution improves query performance on large fact tables.

Incorrect Answers:

A: Do not use a date column for hash distribution. All data for the same date lands in the same distribution. If several users are all filtering on the same date, then only 1 of the 60 distributions do all the processing work.

Question 4

You have an Azure data factory named ADM.

You currently publish all pipeline authoring changes directly to ADF1.

You need to implement version control for the changes made to pipeline artifacts The solution must ensure that you can apply version control to the resources currently defined in the Azure Data Factory Studio for AOFl

Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

ACreate an Azure Data Factory trigger.

BFrom the Azure Data Factory Studio, select Publish.

CFrom the Azure Data Factory Studio, run Publish All

DCreate a Git repository.

ECreate a GitHub action.

FFrom the Azure Data Factory Studio, select up code respository

Correct answer: D, F

Question 5

You have an Azure Stream Analytics job that receives clickstream data from an Azure event hub.

You need to define a query in the Stream Analytics job. The query must meet the following requirements:

Count the number of clicks within each 10-second window based on the country of a visitor.

Ensure that each click is NOT counted more than once.

How should you define the Query?

ASELECT Country, Avg(*) AS Average
FROM ClickStream TIMESTAMP BY CreatedAt
GROUP BY Country, SlidingWindow(second, 10)

BSELECT Country, Count(*) AS Count
FROM ClickStream TIMESTAMP BY CreatedAt
GROUP BY Country, TumblingWindow(second, 10)

CSELECT Country, Avg(*) AS Average
FROM ClickStream TIMESTAMP BY CreatedAt
GROUP BY Country, HoppingWindow(second, 10, 2)

DSELECT Country, Count(*) AS Count
FROM ClickStream TIMESTAMP BY CreatedAt
GROUP BY Country, SessionWindow(second, 5, 10)

Correct answer: B

Tumbling window functions are used to segment a data stream into distinct time segments and perform a function against them, such as the example below. The key differentiators of a Tumbling window are that they repeat, do not overlap, and an event cannot belong to more than one tumbling window.

Example:

https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions

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