Vivid Microsoft DP-203 Exams Online

NEW QUESTION 1

You have an enterprise data warehouse in Azure Synapse Analytics named DW1 on a server named Server1. You need to verify whether the size of the transaction log file for each distribution of DW1 is smaller than 160 GB.
What should you do?

• A. On the master database, execute a query against the sys.dm_pdw_nodes_os_performance_counters dynamic management view.
• B. From Azure Monitor in the Azure portal, execute a query against the logs of DW1.
• C. On DW1, execute a query against the sys.database_files dynamic management view.
• D. Execute a query against the logs of DW1 by using theGet-AzOperationalInsightSearchResult PowerShell cmdlet.

Answer: A

Explanation:
The following query returns the transaction log size on each distribution. If one of the log files is reaching 160 GB, you should consider scaling up your instance or limiting your transaction size.
-- Transaction log size SELECT
instance_name as distribution_db, cntr_value*1.0/1048576 as log_file_size_used_GB, pdw_node_id
FROM sys.dm_pdw_nodes_os_performance_counters WHERE
instance_name like 'Distribution_%'
AND counter_name = 'Log File(s) Used Size (KB)' References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-manage-monitor

NEW QUESTION 2

You have a self-hosted integration runtime in Azure Data Factory.
The current status of the integration runtime has the following configurations:
Status: Running
Type: Self-Hosted
Version: 4.4.7292.1
Running / Registered Node(s): 1/1
High Availability Enabled: False
Linked Count: 0
Queue Length: 0
Average Queue Duration. 0.00s
The integration runtime has the following node details:
Name: X-M
Status: Running
Version: 4.4.7292.1
Available Memory: 7697MB
CPU Utilization: 6%
Network (In/Out): 1.21KBps/0.83KBps
Concurrent Jobs (Running/Limit): 2/14
Role: Dispatcher/Worker
Credential Status: In Sync
Use the drop-down menus to select the answer choice that completes each statement based on the information presented.
NOTE: Each correct selection is worth one point.

• A. Mastered
• B. Not Mastered

Answer: A

Explanation:
Box 1: fail until the node comes back online We see: High Availability Enabled: False
Note: Higher availability of the self-hosted integration runtime so that it's no longer the single point of failure in your big data solution or cloud data integration with Data Factory.
Box 2: lowered We see:
Concurrent Jobs (Running/Limit): 2/14 CPU Utilization: 6%
Note: When the processor and available RAM aren't well utilized, but the execution of concurrent jobs reaches a node's limits, scale up by increasing the number of concurrent jobs that a node can run
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/create-self-hosted-integration-runtime

NEW QUESTION 3

You have an Azure Synapse Analytics job that uses Scala. You need to view the status of the job.
What should you do?

• A. From Azure Monitor, run a Kusto query against the AzureDiagnostics table.
• B. From Azure Monitor, run a Kusto query against the SparkLogying1 Event.CL table.
• C. From Synapse Studio, select the workspac
• D. From Monitor, select Apache Sparks applications.
• E. From Synapse Studio, select the workspac
• F. From Monitor, select SQL requests.

Answer: C

NEW QUESTION 4

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?

• A. SELECT Country, Avg(*) AS AverageFROM ClickStream TIMESTAMP BY CreatedAt GROUP BY Country, SlidingWindow(second, 10)
• B. SELECT Country, Count(*) AS CountFROM ClickStream TIMESTAMP BY CreatedAt GROUP BY Country, TumblingWindow(second, 10)
• C. SELECT Country, Avg(*) AS AverageFROM ClickStream TIMESTAMP BY CreatedAt GROUP BY Country, HoppingWindow(second, 10, 2)
• D. SELECT Country, Count(*) AS CountFROM ClickStream TIMESTAMP BY CreatedAt GROUP BY Country, SessionWindow(second, 5, 10)

Answer: B

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

NEW QUESTION 5

You are designing a slowly changing dimension (SCD) for supplier data in an Azure Synapse Analytics dedicated SQL pool.
You plan to keep a record of changes to the available fields. The supplier data contains the following columns.

Which three additional columns should you add to the data to create a Type 2 SCD? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

• A. surrogate primary key
• B. foreign key
• C. effective start date
• D. effective end date
• E. last modified date
• F. business key

Answer: BCF

NEW QUESTION 6

You have a SQL pool in Azure Synapse.
A user reports that queries against the pool take longer than expected to complete. You need to add monitoring to the underlying storage to help diagnose the issue.
Which two metrics should you monitor? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

• A. Cache used percentage
• B. DWU Limit
• C. Snapshot Storage Size
• D. Active queries
• E. Cache hit percentage

Answer: AE

Explanation:
A: Cache used is the sum of all bytes in the local SSD cache across all nodes and cache capacity is the sum of the storage capacity of the local SSD cache across all nodes.
E: Cache hits is the sum of all columnstore segments hits in the local SSD cache and cache miss is the columnstore segments misses in the local SSD cache summed across all nodes
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-concept-resou

NEW QUESTION 7

You store files in an Azure Data Lake Storage Gen2 container. The container has the storage policy shown in the following exhibit.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection Is worth one point.

• A. Mastered
• B. Not Mastered

Answer: A

Explanation:

NEW QUESTION 8

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 scenario, 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 Storage account that contains 100 GB of files. The files contain text and numerical values. 75% of the rows contain description data that has an average length of 1.1 MB.
You plan to copy the data from the storage account to an Azure SQL data warehouse. You need to prepare the files to ensure that the data copies quickly.
Solution: You modify the files to ensure that each row is more than 1 MB. Does this meet the goal?

• A. Yes
• B. No

Answer: B

Explanation:
Instead modify the files to ensure that each row is less than 1 MB. References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/guidance-for-loading-data

NEW QUESTION 9

You are designing a monitoring solution for a fleet of 500 vehicles. Each vehicle has a GPS tracking device that sends data to an Azure event hub once per minute.
You have a CSV file in an Azure Data Lake Storage Gen2 container. The file maintains the expected geographical area in which each vehicle should be.
You need to ensure that when a GPS position is outside the expected area, a message is added to another event hub for processing within 30 seconds. The solution must minimize cost.
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

• A. Mastered
• B. Not Mastered

Answer: A

Explanation:
Box 1: Azure Stream Analytics Box 2: Hopping
Hopping window functions hop forward in time by a fixed period. It may be easy to think of them as Tumbling windows that can overlap and be emitted more often than the window size. Events can belong to more than one Hopping window result set. To make a Hopping window the same as a Tumbling window, specify the hop size to be the same as the window size.
Box 3: Point within polygon Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions

NEW QUESTION 10

You plan to create an Azure Synapse Analytics dedicated SQL pool.
You need to minimize the time it takes to identify queries that return confidential information as defined by the company's data privacy regulations and the users who executed the queues.
Which two components should you include in the solution? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

• A. sensitivity-classification labels applied to columns that contain confidential information
• B. resource tags for databases that contain confidential information
• C. audit logs sent to a Log Analytics workspace
• D. dynamic data masking for columns that contain confidential information

Answer: AC

Explanation:
A: You can classify columns manually, as an alternative or in addition to the recommendation-based classification:

Select Add classification in the top menu of the pane.
In the context window that opens, select the schema, table, and column that you want to classify, and the information type and sensitivity label.
Select Add classification at the bottom of the context window.
C: An important aspect of the information-protection paradigm is the ability to monitor access to sensitive data. Azure SQL Auditing has been enhanced to include a new field in the audit log called data_sensitivity_information. This field logs the sensitivity classifications (labels) of the data that was returned by a query. Here's an example:

Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/data-discovery-and-classification-overview

NEW QUESTION 11

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 scenario, 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 Storage account that contains 100 GB of files. The files contain text and numerical values. 75% of the rows contain description data that has an average length of 1.1 MB.
You plan to copy the data from the storage account to an Azure SQL data warehouse. You need to prepare the files to ensure that the data copies quickly.
Solution: You modify the files to ensure that each row is less than 1 MB. Does this meet the goal?

• A. Yes
• B. No

Answer: A

Explanation:
When exporting data into an ORC File Format, you might get Java out-of-memory errors when there are large text columns. To work around this limitation, export only a subset of the columns.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/guidance-for-loading-data

NEW QUESTION 12

You have an Azure Factory instance named DF1 that contains a pipeline named PL1.PL1 includes a tumbling window trigger.
You create five clones of PL1. You configure each clone pipeline to use a different data source.
You need to ensure that the execution schedules of the clone pipeline match the execution schedule of PL1. What should you do?

• A. Add a new trigger to each cloned pipeline
• B. Associate each cloned pipeline to an existing trigger.
• C. Create a tumbling window trigger dependency for the trigger of PL1.
• D. Modify the Concurrency setting of each pipeline.

Answer: B

NEW QUESTION 13

You need to schedule an Azure Data Factory pipeline to execute when a new file arrives in an Azure Data Lake Storage Gen2 container.
Which type of trigger should you use?

• A. on-demand
• B. tumbling window
• C. schedule
• D. event

Answer: D

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

NEW QUESTION 14

You have an Azure Synapse Analytics serverless SQL pool named Pool1 and an Azure Data Lake Storage Gen2 account named storage1. The AllowedBlobpublicAccess porperty is disabled for storage1.
You need to create an external data source that can be used by Azure Active Directory (Azure AD) users to access storage1 from Pool1.
What should you create first?

• A. an external resource pool
• B. a remote service binding
• C. database scoped credentials
• D. an external library

Answer: C

NEW QUESTION 15

You develop a dataset named DBTBL1 by using Azure Databricks. DBTBL1 contains the following columns:
• SensorTypelD
• GeographyRegionID
• Year
• Month
• Day
• Hour
• Minute
• Temperature
• WindSpeed
• Other
You need to store the data to support daily incremental load pipelines that vary for each GeographyRegionID. 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.

• A. Mastered
• B. Not Mastered

Answer: A

Explanation:

NEW QUESTION 16

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?

• A. data masking
• B. Always Encrypted
• C. column-level security
• D. row-level security

Answer: A

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

NEW QUESTION 17

You have an Azure Data Lake Storage Gen2 account that contains a JSON file for customers. The file contains two attributes named FirstName and LastName.
You need to copy the data from the JSON file to an Azure Synapse Analytics table by using Azure Databricks. A new column must be created that concatenates the FirstName and LastName values.
You create the following components:
A destination table in Azure Synapse
An Azure Blob storage container
A service principal
Which five actions should you perform in sequence next in is Databricks notebook? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

• A. Mastered
• B. Not Mastered

Answer: A

Explanation:
Step 1: Read the file into a data frame.
You can load the json files as a data frame in Azure Databricks. Step 2: Perform transformations on the data frame.
Step 3:Specify a temporary folder to stage the data
Specify a temporary folder to use while moving data between Azure Databricks and Azure Synapse. Step 4: Write the results to a table in Azure Synapse.
You upload the transformed data frame into Azure Synapse. You use the Azure Synapse connector for Azure Databricks to directly upload a dataframe as a table in a Azure Synapse.
Step 5: Drop the data frame
Clean up resources. You can terminate the cluster. From the Azure Databricks workspace, select Clusters on the left. For the cluster to terminate, under Actions, point to the ellipsis (...) and select the Terminate icon.
Reference:
https://docs.microsoft.com/en-us/azure/azure-databricks/databricks-extract-load-sql-data-warehouse

NEW QUESTION 18

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?

• A. a table that has an IDENTITY property
• B. a system-versioned temporal table
• C. a user-defined SEQUENCE object
• D. a table that has a FOREIGN KEY constraint

Answer: A

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

NEW QUESTION 19

You have a C# application that process data from an Azure IoT hub and performs complex transformations. You need to replace the application with a real-time solution. The solution must reuse as much code as possible from the existing application.

• A. Azure Databricks
• B. Azure Event Grid
• C. Azure Stream Analytics
• D. Azure Data Factory

Answer: C

Explanation:
Azure Stream Analytics on IoT Edge empowers developers to deploy near-real-time analytical intelligence closer to IoT devices so that they can unlock the full value of device-generated data. UDF are available in C# for IoT Edge jobs
Azure Stream Analytics on IoT Edge runs within the Azure IoT Edge framework. Once the job is created in Stream Analytics, you can deploy and manage it using IoT Hub.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-edge

NEW QUESTION 20
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