Microsoft Data Engineering on Microsoft Azure DP-203 Exam Questions

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Total 354 questions
Question 1

You have an Azure Synapse Analytics dedicated SQL pool mat contains a table named dbo.Users.

You need to prevent a group of users from reading user email addresses from dbo.Users. What should you use?



Answer : C


Question 2

You are planning a solution to aggregate streaming data that originates in Apache Kafka and is output to Azure Data Lake Storage Gen2. The developers who will implement the stream processing solution use Java, Which service should you recommend using to process the streaming data?



Answer : C

https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/stream-processing


Question 3

You need to implement the surrogate key for the retail store table. The solution must meet the sales transaction

dataset requirements.

What should you create?



Answer : A

Scenario: Implement a surrogate key to account for changes to the retail store addresses.

A surrogate key on a table is a column with a unique identifier for each row. The key is not generated from the table data. Data modelers like to create surrogate keys on their tables when they design data warehouse models. You can use the IDENTITY property to achieve this goal simply and effectively without affecting load performance.


https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-identity

Question 4

You plan to ingest streaming social media data by using Azure Stream Analytics. The data will be stored in files in Azure Data Lake Storage, and then consumed by using Azure Datiabricks and PolyBase in Azure Synapse Analytics.

You need to recommend a Stream Analytics data output format to ensure that the queries from Databricks and PolyBase against the files encounter the fewest possible errors. The solution must ensure that the tiles can be queried quickly and that the data type information is retained.

What should you recommend?



Answer : A

https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-define-outputs


Question 5

You need to trigger an Azure Data Factory pipeline when a file arrives in an Azure Data Lake Storage Gen2 container.

Which resource provider should you enable?



Answer : C

Event-driven architecture (EDA) is a common data integration pattern that involves production, detection, consumption, and reaction to events. Data integration scenarios often require Data Factory customers to trigger pipelines based on events happening in storage account, such as the arrival or deletion of a file in Azure Blob Storage account. Data Factory natively integrates with Azure Event Grid, which lets you trigger pipelines on such events.


https://docs.microsoft.com/en-us/azure/data-factory/how-to-create-event-trigger

https://docs.microsoft.com/en-us/azure/data-factory/concepts-pipeline-execution-triggers

Question 6

What should you recommend using to secure sensitive customer contact information?



Answer : B

Scenario: All cloud data must be encrypted at rest and in transit.

Always Encrypted is a feature designed to protect sensitive data stored in specific database columns from access (for example, credit card numbers, national identification numbers, or data on a need to know basis). This includes database administrators or other privileged users who are authorized to access the database to perform management tasks, but have no business need to access the particular data in the encrypted columns. The data is always encrypted, which means the encrypted data is decrypted only for processing by client applications with access to the encryption key.


https://docs.microsoft.com/en-us/azure/sql-database/sql-database-security-overview

Question 7

You are designing a dimension table for a data warehouse. The table will track the value of the dimension attributes over time and preserve the history of the data by adding new rows as the data changes.

Which type of slowly changing dimension (SCD) should use?



Answer : C

Type 2- Creating a new additional record. In this methodology all history of dimension changes is kept in the database. You capture attribute change by adding a new row with a new surrogate key to the dimension table. Both the prior and new rows contain as attributes the natural key(or other durable identifier). Also 'effective date' and 'current indicator' columns are used in this method. There could be only one record with current indicator set to 'Y'. For 'effective date' columns, i.e. start_date and end_date, the end_date for current record usually is set to value 9999-12-31. Introducing changes to the dimensional model in type 2 could be very expensive database operation so it is not recommended to use it in dimensions where a new attribute could be added in the future.

https://www.datawarehouse4u.info/SCD-Slowly-Changing-Dimensions.html


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