Microsoft DP-203 Exam Questions Instant Access

Question 1

You are designing the folder structure for an Azure Data Lake Storage Gen2 account.

You identify the following usage patterns:

* Users will query data by using Azure Synapse Analytics serverless SQL pools and Azure Synapse Analytics serverless Apache Spark pods.

* Most queries will include a filter on the current year or week.

* Data will be secured by data source.

You need to recommend a folder structure that meets the following requirements:

* Supports the usage patterns

* Simplifies folder security

* Minimizes query times

Which folder structure should you recommend?

AOption A

BOption B

COption C

DOption D

EOption E

Answer : C

Data will be secured by data source. -> Use DataSource as top folder.

Most queries will include a filter on the current year or week -> Use \YYYY\WW\ as subfolders.

Common Use Cases

A common use case is to filter data stored in a date (and possibly time) folder structure such as /YYYY/MM/DD/ or /YYYY/MM/YYYY-MM-DD/. As new data is generated/sent/copied/moved to the storage account, a new folder is created for each specific time period. This strategy organises data into a maintainable folder structure.

Question 2

You have a Microsoft Purview account. The Lineage view of a CSV file is shown in the following exhibit.

How is the data for the lineage populated?

Amanually

Bby scanning data stores

Cby executing a Data Factory pipeline

Answer : B

According to Microsoft Purview Data Catalog lineage user guide1, data lineage in Microsoft Purview is a core platform capability that populates the Microsoft Purview Data Map with data movement and transformations across systems2.Lineage is captured as it flows in the enterprise and stitched without gaps irrespective of its source2.

Question 3

You are monitoring an Azure Stream Analytics job.

You discover that the Backlogged Input Events metric is increasing slowly and is consistently non-zero.

You need to ensure that the job can handle all the events.

What should you do?

AChange the compatibility level of the Stream Analytics job.

BIncrease the number of streaming units (SUs).

CRemove any named consumer groups from the connection and use $default.

DCreate an additional output stream for the existing input stream.

Answer : B

Backlogged Input Events: Number of input events that are backlogged. A non-zero value for this metric implies that your job isn't able to keep up with the number of incoming events. If this value is slowly increasing or consistently non-zero, you should scale out your job. You should increase the Streaming Units.

Note: Streaming Units (SUs) represents the computing resources that are allocated to execute a Stream Analytics job. The higher the number of SUs, the more CPU and memory resources are allocated for your job.

https://docs.microsoft.com/bs-cyrl-ba/azure/stream-analytics/stream-analytics-monitoring

Question 4

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 azure Data Lake Storage Gen2 container named container1.

You plan to insert data from the files into Table1 and transform the dat

a. 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 a additional DateTime column.

Does this meet the goal?

AYes

BNo

Answer : A

Question 5

You have an Azure Synapse Analytics Apache Spark pool named Pool1.

You plan to load JSON files from an Azure Data Lake Storage Gen2 container into the tables in Pool1. The structure and data types vary by file.

You need to load the files into the tables. The solution must maintain the source data types.

What should you do?

AUse a Get Metadata activity in Azure Data Factory.

BUse a Conditional Split transformation in an Azure Synapse data flow.

CLoad the data by using the OPEHROwset Transact-SQL command in an Azure Synapse Anarytics serverless SQL pool.

DLoad the data by using PySpark.

Answer : A

Serverless SQL pool can automatically synchronize metadata from Apache Spark. A serverless SQL pool database will be created for each database existing in serverless Apache Spark pools.

Serverless SQL pool enables you to query data in your data lake. It offers a T-SQL query surface area that accommodates semi-structured and unstructured data queries.

To support a smooth experience for in place querying of data that's located in Azure Storage files, serverless SQL pool uses the OPENROWSET function with additional capabilities.

The easiest way to see to the content of your JSON file is to provide the file URL to the OPENROWSET function, specify csv FORMAT.

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/query-json-files

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/query-data-storage

Question 6

You have a data warehouse in Azure Synapse Analytics.

You need to ensure that the data in the data warehouse is encrypted at rest.

What should you enable?

AAdvanced Data Security for this database

BTransparent Data Encryption (TDE)

CSecure transfer required

DDynamic Data Masking

Answer : B

Azure SQL Database currently supports encryption at rest for Microsoft-managed service side and client-side encryption scenarios.

Support for server encryption is currently provided through the SQL feature called Transparent Data Encryption.

Client-side encryption of Azure SQL Database data is supported through the Always Encrypted feature.

https://docs.microsoft.com/en-us/azure/security/fundamentals/encryption-atrest

Question 7

You have an Azure Stream Analytics query. The query returns a result set that contains 10,000 distinct values for a column named clusterID.

You monitor the Stream Analytics job and discover high latency.

You need to reduce the latency.

Which two actions should you perform? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

AAdd a pass-through query.

BAdd a temporal analytic function.

CScale out the query by using PARTITION BY.

DConvert the query to a reference query.

EIncrease the number of streaming units.

Answer : C, E

C: Scaling a Stream Analytics job takes advantage of partitions in the input or output. Partitioning lets you

divide data into subsets based on a partition key. A process that consumes the data (such as a Streaming

Analytics job) can consume and write different partitions in parallel, which increases throughput.

E: Streaming Units (SUs) represents the computing resources that are allocated to execute a Stream Analytics

job. The higher the number of SUs, the more CPU and memory resources are allocated for your job. This

capacity lets you focus on the query logic and abstracts the need to manage the hardware to run your Stream

Analytics job in a timely manner.

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

https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-streaming-unit-consumption

Microsoft Data Engineering on Microsoft Azure DP-203 Exam Questions