Get Microsoft DP-203 Dumps Questions [2025] To Gain Brilliant Result [Q197-Q214]

Get Microsoft DP-203 Dumps Questions [2025] To Gain Brilliant Result

DP-203 dumps - TroytecDumps - 100% Passing Guarantee

Microsoft DP-203 Exam Syllabus Topics:

Topic	Details
Topic 1	Design and implement data storage: In this exam section, the candidates are tested for implementing a parity strategy, designing and implementing the data exploration layer, and understanding new or updated data lineage to Microsoft Purview.
Topic 2	Develop data processing: This section of the exam covers topics such as ingesting and transforming data, developing a batch processing solution, developing a stream processing solution, and managing batches and pipelines.
Topic 3	Secure, monitor, and optimize data storage and data processing: This section of the exam covers how to implement data security, monitor data storage and data processing, and enhance and troubleshoot data storage and data processing such as failed spark jobs.

NEW QUESTION # 197
You have an Azure subscription that contains an Azure Databricks workspace. The workspace contains a notebook named Notebook1. In Notebook1, you create an Apache Spark DataFrame named df_sales that contains the following columns:
* Customer
* Salesperson
* Region
* Amount
You need to identify the three top performing salespersons by amount for a region named HQ.
How should you complete the query? 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.

Answer:

Explanation:

Explanation:

NEW QUESTION # 198
You configure version control for an Azure Data Factory instance as 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.

Answer:

Explanation:

Explanation:
Letter Description automatically generated

Box 1: adf_publish
The Publish branch is the branch in your repository where publishing related ARM templates are stored and updated. By default, it's adf_publish.
Box 2: / dwh_batchetl/adf_publish/contososales
Note: RepositoryName (here dwh_batchetl): Your Azure Repos code repository name. Azure Repos projects contain Git repositories to manage your source code as your project grows. You can create a new repository or use an existing repository that's already in your project.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/source-control

NEW QUESTION # 199
You build an Azure Data Factory pipeline to move data from an Azure Data Lake Storage Gen2 container to a database in an Azure Synapse Analytics dedicated SQL pool.
Data in the container is stored in the following folder structure.
/in/{YYYY}/{MM}/{DD}/{HH}/{mm}
The earliest folder is /in/2021/01/01/00/00. The latest folder is /in/2021/01/15/01/45.
You need to configure a pipeline trigger to meet the following requirements:
* Existing data must be loaded.
* Data must be loaded every 30 minutes.
* Late-arriving data of up to two minutes must he included in the load for the time at which the data should have arrived.
How should you configure the pipeline trigger? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: Tumbling window
To be able to use the Delay parameter we select Tumbling window.
Box 2:
Recurrence: 30 minutes, not 32 minutes
Delay: 2 minutes.
The amount of time to delay the start of data processing for the window. The pipeline run is started after the expected execution time plus the amount of delay. The delay defines how long the trigger waits past the due time before triggering a new run. The delay doesn't alter the window startTime.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/how-to-create-tumbling-window-trigger

NEW QUESTION # 200
You have the following table named Employees.

You need to calculate the employee_type value based on the hire_date value.
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.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/sql/t-sql/language-elements/case-transact-sql

NEW QUESTION # 201
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 are designing an Azure Stream Analytics solution that will analyze Twitter data.
You need to count the tweets in each 10-second window. The solution must ensure that each tweet is counted only once.
Solution: You use a tumbling window, and you set the window size to 10 seconds.
Does this meet the goal?

A. Yes
B. No

Answer: A

Explanation:
Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals. The following diagram illustrates a stream with a series of events and how they are mapped into 10-second tumbling windows.

Reference:
https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics

NEW QUESTION # 202
You are building an Azure Analytics query that will receive input data from Azure IoT Hub and write the results to Azure Blob storage.
You need to calculate the difference in readings per sensor per hour.
How should you complete the query? To answer, select the appropriate options in the answer are a.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/stream-analytics-query/lag-azure-stream-analytics

NEW QUESTION # 203
You have an on-premises data warehouse that includes the following fact tables. Both tables have the following columns: DateKey, ProductKey, RegionKey. There are 120 unique product keys and 65 unique region keys.

Queries that use the data warehouse take a long time to complete.
You plan to migrate the solution to use Azure Synapse Analytics. You need to ensure that the Azure-based solution optimizes query performance and minimizes processing skew.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point

Answer:

Explanation:

Explanation:

Box 1: Hash-distributed
Box 2: ProductKey
ProductKey is used extensively in joins.
Hash-distributed tables improve query performance on large fact tables.
Box 3: Round-robin
Box 4: RegionKey
Round-robin tables are useful for improving loading speed.
Consider using the round-robin distribution for your table in the following scenarios:
When getting started as a simple starting point since it is the default If there is no obvious joining key If there is not good candidate column for hash distributing the table If the table does not share a common join key with other tables If the join is less significant than other joins in the query When the table is a temporary staging table Note: A distributed table appears as a single table, but the rows are actually stored across 60 distributions. The rows are distributed with a hash or round-robin algorithm.
Reference:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-distribute

NEW QUESTION # 204
You need to design an analytical storage solution for the transactional data. The solution must meet the sales transaction dataset requirements.
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.

Answer:

Explanation:

Explanation
Graphical user interface, text, application, table Description automatically generated

Box 1: Round-robin
Round-robin tables are useful for improving loading speed.
Scenario: Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month.
Box 2: Hash
Hash-distributed tables improve query performance on large fact tables.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-distribu

NEW QUESTION # 205
You have the following Azure Data Factory pipelines
* ingest Data from System 1
* Ingest Data from System2
* Populate Dimensions
* Populate facts
ingest Data from System1 and Ingest Data from System1 have no dependencies. Populate Dimensions must execute after Ingest Data from System1 and Ingest Data from System* Populate Facts must execute after the Populate Dimensions pipeline. All the pipelines must execute every eight hours.
What should you do to schedule the pipelines for execution?

A. Add an event trigger to all four pipelines.
B. Create a parent pipeline that contains the four pipelines and use a schedule trigger.
C. Create a parent pipeline that contains the four pipelines and use an event trigger.
D. Add a schedule trigger to all four pipelines.

Answer: B

NEW QUESTION # 206
You are designing an Azure Stream Analytics solution that receives instant messaging data from an Azure Event Hub.
You need to ensure that the output from the Stream Analytics job counts the number of messages per time zone every 15 seconds.
How should you complete the Stream Analytics query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Table Description automatically generated

Box 1: timestamp by
Box 2: TUMBLINGWINDOW
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.
Timeline Description automatically generated

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

NEW QUESTION # 207
You have an Azure Data Factory pipeline that has the activities 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.

Answer:

Explanation:

Explanation:
Box 1: succeed
Box 2: failed
Example:
Now let's say we have a pipeline with 3 activities, where Activity1 has a success path to Activity2 and a failure path to Activity3. If Activity1 fails and Activity3 succeeds, the pipeline will fail. The presence of the success path alongside the failure path changes the outcome reported by the pipeline, even though the activity executions from the pipeline are the same as the previous scenario.

Activity1 fails, Activity2 is skipped, and Activity3 succeeds. The pipeline reports failure.
Reference:
https://datasavvy.me/2021/02/18/azure-data-factory-activity-failures-and-pipeline-outcomes/

NEW QUESTION # 208
You need to implement an Azure Synapse Analytics database object for storing the sales transactions dat a. The solution must meet the sales transaction dataset requirements.
What solution must meet the sales transaction dataset requirements.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 209
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 enterprise data warehouse in Azure Synapse Analytics.
You need to prepare the files to ensure that the data copies quickly.
Solution: You convert the files to compressed delimited text files.
Does this meet the goal?

A. Yes
B. No

Answer: A

Explanation:
All file formats have different performance characteristics. For the fastest load, use compressed delimited text files.
Reference:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/guidance-for-loading-data

NEW QUESTION # 210
You are designing an Azure Data Lake Storage Gen2 structure for telemetry data from 25 million devices distributed across seven key geographical regions. Each minute, the devices will send a JSON payload of metrics to Azure Event Hubs.
You need to recommend a folder structure for the dat
a. The solution must meet the following requirements:
Data engineers from each region must be able to build their own pipelines for the data of their respective region only.
The data must be processed at least once every 15 minutes for inclusion in Azure Synapse Analytics serverless SQL pools.
How should you recommend completing the structure? 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.

Answer:

Explanation:

Reference:
https://github.com/paolosalvatori/StreamAnalyticsAzureDataLakeStore/blob/master/README.md

NEW QUESTION # 211
You have an on-premises data warehouse that includes the following fact tables. Both tables have the following columns: DateKey, ProductKey, RegionKey. There are 120 unique product keys and 65 unique region keys.

Queries that use the data warehouse take a long time to complete.
You plan to migrate the solution to use Azure Synapse Analytics. You need to ensure that the Azure-based solution optimizes query performance and minimizes processing skew.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-distribute

NEW QUESTION # 212
You have a table named SalesFact in an enterprise data warehouse in Azure Synapse Analytics. SalesFact contains sales data from the past 36 months and has the following characteristics:
Is partitioned by month
Contains one billion rows
Has clustered columnstore indexes
At the beginning of each month, you need to remove data from SalesFact that is older than 36 months as quickly as possible.
Which three actions should you perform in sequence in a stored procedure? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-partition

NEW QUESTION # 213
You are processing streaming data from vehicles that pass through a toll booth.
You need to use Azure Stream Analytics to return the license plate, vehicle make, and hour the last vehicle passed during each 10-minute window.
How should you complete the query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Graphical user interface, text, application Description automatically generated

Box 1: MAX
The first step on the query finds the maximum time stamp in 10-minute windows, that is the time stamp of the last event for that window. The second step joins the results of the first query with the original stream to find the event that match the last time stamps in each window.
Query:
WITH LastInWindow AS
(
SELECT
MAX(Time) AS LastEventTime
FROM
Input TIMESTAMP BY Time
GROUP BY
TumblingWindow(minute, 10)
)
SELECT
Input.License_plate,
Input.Make,
Input.Time
FROM
Input TIMESTAMP BY Time
INNER JOIN LastInWindow
ON DATEDIFF(minute, Input, LastInWindow) BETWEEN 0 AND 10
AND Input.Time = LastInWindow.LastEventTime
Box 2: TumblingWindow
Tumbling windows are a series of fixed-sized, non-overlapping and contiguous time intervals.
Box 3: DATEDIFF
DATEDIFF is a date-specific function that compares and returns the time difference between two DateTime fields, for more information, refer to date functions.
Reference:
https://docs.microsoft.com/en-us/stream-analytics-query/tumbling-window-azure-stream-analytics