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[Jan-2024 Newly Released] DP-203 Exam Questions For You To Pass

Microsoft DP-203 Exam: Basic Questions With Answers

NEW QUESTION 130
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?

 on-demand

 tumbling window

 schedule

 event

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 131
You have a data model that you plan to implement in a data warehouse in Azure Synapse Analytics as shown in the following exhibit.

All the dimension tables will be less than 2 GB after compression, and the fact table will be approximately 6 TB.
Which type of table should you use for each table? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

NEW QUESTION 132
You have an Azure Storage account that generates 200.000 new files daily. The file names have a format of (YYY)/(MM)/(DD)/|HH])/(CustornerID).csv.
You need to design an Azure Data Factory solution that will toad new data from the storage account to an Azure Data lake once hourly. The solution must minimize load times and costs.
How should you configure the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

NEW QUESTION 133
You need to create an Azure Data Factory pipeline to process data for the following three departments at your company: Ecommerce, retail, and wholesale. The solution must ensure that data can also be processed for the entire company.
How should you complete the Data Factory data flow script? 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.

Reference:
https://docs.microsoft.com/en-us/azure/data-factory/data-flow-conditional-split

NEW QUESTION 134
You are building an Azure Data Factory solution to process data received from Azure Event Hubs, and then ingested into an Azure Data Lake Storage Gen2 container.
The data will be ingested every five minutes from devices into JSON files. The files have the following naming pattern.
/{deviceType}/in/{YYYY}/{MM}/{DD}/{HH}/{deviceID}_{YYYY}{MM}{DD}HH}{mm}.json You need to prepare the data for batch data processing so that there is one dataset per hour per deviceType. The solution must minimize read times.
How should you configure the sink for the copy activity? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

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

NEW QUESTION 135
You need to implement versioned changes to the integration pipelines. The solution must meet the data integration requirements.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.

Explanation

NEW QUESTION 136
Which Azure Data Factory components should you recommend using together to import the daily inventory data from the SQL server to Azure Data Lake Storage? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Box 1: Self-hosted integration runtime
A self-hosted IR is capable of running copy activity between a cloud data stores and a data store in private network.
Box 2: Schedule trigger
Schedule every 8 hours
Box 3: Copy activity
Scenario:
Customer data, including name, contact information, and loyalty number, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table.
Product data, including product ID, name, and category, comes from Salesforce and can be imported into Azure once every eight hours. Row modified dates are not trusted in the source table.

NEW QUESTION 137
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 area.
NOTE: Each correct selection is worth one point.

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

NEW QUESTION 138
You have the Azure Synapse Analytics pipeline shown in the following exhibit.

You need to add a set variable activity to the pipeline to ensure that after the pipeline’s completion, the status of the pipeline is always successful.
What should you configure for the set variable activity?

 a success dependency on the Business Activity That Fails activity

 a failure dependency on the Upon Failure activity

 a skipped dependency on the Upon Success activity

 a skipped dependency on the Upon Failure activity

A failure dependency means that the activity will run only if the previous activity fails. In this case, setting a failure dependency on the Upon Failure activity will ensure that the set variable activity will run after the pipeline fails and set the status of the pipeline to successful.

NEW QUESTION 139
You build a data warehouse in an Azure Synapse Analytics dedicated SQL pool.
Analysts write a complex SELECT query that contains multiple JOIN and CASE statements to transform data for use in inventory reports. The inventory reports will use the data and additional WHERE parameters depending on the report. The reports will be produced once daily.
You need to implement a solution to make the dataset available for the reports. The solution must minimize query times.
What should you implement?

 a materialized view

 a replicated table

 in ordered clustered columnstore index

 result set chaching

Explanation
Materialized views for dedicated SQL pools in Azure Synapse provide a low maintenance method for complex analytical queries to get fast performance without any query change.
Note: When result set caching is enabled, dedicated SQL pool automatically caches query results in the user database for repetitive use. This allows subsequent query executions to get results directly from the persisted cache so recomputation is not needed. Result set caching improves query performance and reduces compute resource usage. In addition, queries using cached results set do not use any concurrency slots and thus do not count against existing concurrency limits.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/performance-tuning-materialized-v
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/performance-tuning-result-set-cach

NEW QUESTION 140
You have an Azure Synapse Analytics dedicated SQL pool that contains a table named Table1. Table1 contains the following:
One billion rows
A clustered columnstore index
A hash-distributed column named Product Key
A column named Sales Date that is of the date data type and cannot be null Thirty million rows will be added to Table1 each month.
You need to partition Table1 based on the Sales Date column. The solution must optimize query performance and data loading.
How often should you create a partition?

 once per month

 once per year

 once per day

 once per week

Need a minimum 1 million rows per distribution. Each table is 60 distributions. 30 millions rows is added each month. Need 2 months to get a minimum of 1 million rows per distribution in a new partition.
Note: When creating partitions on clustered columnstore tables, it is important to consider how many rows belong to each partition. For optimal compression and performance of clustered columnstore tables, a minimum of 1 million rows per distribution and partition is needed. Before partitions are created, dedicated SQL pool already divides each table into 60 distributions.
Any partitioning added to a table is in addition to the distributions created behind the scenes. Using this example, if the sales fact table contained 36 monthly partitions, and given that a dedicated SQL pool has 60 distributions, then the sales fact table should contain 60 million rows per month, or 2.1 billion rows when all months are populated. If a table contains fewer than the recommended minimum number of rows per partition, consider using fewer partitions in order to increase the number of rows per partition.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-partition

NEW QUESTION 141
You are planning the deployment of Azure Data Lake Storage Gen2.
You have the following two reports that will access the data lake:
* Report1: Reads three columns from a file that contains 50 columns.
* Report2: Queries a single record based on a timestamp.
You need to recommend in which format to store the data in the data lake to support the reports. The solution must minimize read times.
What should you recommend for each report? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Explanation

Report1: CSV
CSV: The destination writes records as delimited data.
Report2: AVRO
AVRO supports timestamps.
Not Parquet, TSV: Not options for Azure Data Lake Storage Gen2.
Reference:
https://streamsets.com/documentation/datacollector/latest/help/datacollector/UserGuide/Destinations/ADLS-G2-

NEW QUESTION 142
You are responsible for providing access to an Azure Data Lake Storage Gen2 account.
Your user account has contributor access to the storage account, and you have the application ID and access key.
You plan to use PolyBase to load data into an enterprise data warehouse in Azure Synapse Analytics.
You need to configure PolyBase to connect the data warehouse to storage account.
Which three components should you create in sequence? To answer, move the appropriate components from the list of components to the answer area and arrange them in the correct order.

Explanation

NEW QUESTION 143
You have an Azure Data Lake Storage Gen2 account named account1 that stores logs as shown in the following table.

You do not expect that the logs will be accessed during the retention periods.
You need to recommend a solution for account1 that meets the following requirements:
* Automatically deletes the logs at the end of each retention period
* Minimizes storage costs
What should you include in the recommendation? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Explanation
Table Description automatically generated

Box 1: Store the infrastructure logs in the Cool access tier and the application logs in the Archive access tier For infrastructure logs: Cool tier – An online tier optimized for storing data that is infrequently accessed or modified. Data in the cool tier should be stored for a minimum of 30 days. The cool tier has lower storage costs and higher access costs compared to the hot tier.
For application logs: Archive tier – An offline tier optimized for storing data that is rarely accessed, and that has flexible latency requirements, on the order of hours. Data in the archive tier should be stored for a minimum of 180 days.
Box 2: Azure Blob storage lifecycle management rules
Blob storage lifecycle management offers a rule-based policy that you can use to transition your data to the desired access tier when your specified conditions are met. You can also use lifecycle management to expire data at the end of its life.
Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/access-tiers-overview

NEW QUESTION 144
You have two Azure Data Factory instances named ADFdev and ADFprod. ADFdev connects to an Azure DevOps Git repository.
You publish changes from the main branch of the Git repository to ADFdev.
You need to deploy the artifacts from ADFdev to ADFprod.
What should you do first?

 From ADFdev, modify the Git configuration.

 From ADFdev, create a linked service.

 From Azure DevOps, create a release pipeline.

 From Azure DevOps, update the main branch.

In Azure Data Factory, continuous integration and delivery (CI/CD) means moving Data Factory pipelines from one environment (development, test, production) to another.
Note:
The following is a guide for setting up an Azure Pipelines release that automates the deployment of a data factory to multiple environments.
In Azure DevOps, open the project that’s configured with your data factory.
On the left side of the page, select Pipelines, and then select Releases.
Select New pipeline, or, if you have existing pipelines, select New and then New release pipeline.
In the Stage name box, enter the name of your environment.
Select Add artifact, and then select the git repository configured with your development data factory. Select the publish branch of the repository for the Default branch. By default, this publish branch is adf_publish.
Select the Empty job template.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/continuous-integration-deployment

NEW QUESTION 145
You have an Azure Data Lake Storage Gen2 account that contains a container named container1. You have an Azure Synapse Analytics serverless SQL pool that contains a native external table named dbo.Table1. The source data for dbo.Table1 is stored in container1. The folder structure of container1 is shown in the following exhibit.

The external data source is defined by using the following statement.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Explanation
Box 1: Yes
In the serverless SQL pool you can also use recursive wildcards /logs/** to reference Parquet or CSV files in any sub-folder beneath the referenced folder.
Box 2: Yes
Box 3: No
Reference: https://learn.microsoft.com/en-us/azure/synapse-analytics/sql/develop-tables-external-tables

NEW QUESTION 146
You are designing a real-time dashboard solution that will visualize streaming data from remote sensors that connect to the internet. The streaming data must be aggregated to show the average value of each 10-second interval. The data will be discarded after being displayed in the dashboard.
The solution will use Azure Stream Analytics and must meet the following requirements:
Minimize latency from an Azure Event hub to the dashboard.
Minimize the required storage.
Minimize development effort.
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

Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-power-bi-dashboard

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