2025 Reliable Microsoft DP-203: Data Engineering on Microsoft Azure Reliable Learning Materials

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The DP-203 Exam covers a broad range of topics, including data storage, data processing, data integration, and data security. Successful candidates will be able to design and implement data solutions using Azure services such as Azure SQL Database, Azure Cosmos DB, Azure Data Factory, and Azure Stream Analytics. They will also be able to implement advanced analytics solutions using Azure Databricks and Azure Synapse Analytics.

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Microsoft Data Engineering on Microsoft Azure Sample Questions (Q52-Q57):

NEW QUESTION # 52
You have an Azure Data Factory instance named ADF1 and two Azure Synapse Analytics workspaces named WS1 and WS2.
ADF1 contains the following pipelines:
P1: Uses a copy activity to copy data from a nonpartitioned table in a dedicated SQL pool of WS1 to an Azure Data Lake Storage Gen2 account P2: Uses a copy activity to copy data from text-delimited files in an Azure Data Lake Storage Gen2 account to a nonpartitioned table in a dedicated SQL pool of WS2 You need to configure P1 and P2 to maximize parallelism and performance.
Which dataset settings should you configure for the copy activity if each pipeline? 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/azure/synapse-analytics/sql/load-data-overview

NEW QUESTION # 53
You are designing an Azure Databricks table. The table will ingest an average of 20 million streaming events per day.
You need to persist the events in the table for use in incremental load pipeline jobs in Azure Databricks. The solution must minimize storage costs and incremental load times.
What should you include in the solution?

A. Sink to Azure Queue storage.
B. Partition by DateTime fields.
C. Use a JSON format for physical data storage.
D. Include a watermark column.

Answer: B

Explanation:
Explanation
The Databricks ABS-AQS connector uses Azure Queue Storage (AQS) to provide an optimized file source that lets you find new files written to an Azure Blob storage (ABS) container without repeatedly listing all of the files.
This provides two major advantages:
* Lower latency: no need to list nested directory structures on ABS, which is slow and resource intensive.
* Lower costs: no more costly LIST API requests made to ABS.
Reference:
https://docs.microsoft.com/en-us/azure/databricks/spark/latest/structured-streaming/aqs

NEW QUESTION # 54
You have an Azure Synapse Analytics dedicated SQL pool named Pool1 that contains an external table named Sales. Sales contains sales data. Each row in Sales contains data on a single sale, including the name of the salesperson.
You need to implement row-level security (RLS). The solution must ensure that the salespeople can access only their respective sales.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: A security policy for sale
Here are the steps to create a security policy for Sales:
* Create a user-defined function that returns the name of the current user:
* CREATE FUNCTION dbo.GetCurrentUser()
* RETURNS NVARCHAR(128)
* AS
* BEGIN
* RETURN SUSER_SNAME();
* END;
* Create a security predicate function that filters the Sales table based on the current user:
* CREATE FUNCTION dbo.SalesPredicate(@salesperson NVARCHAR(128))
* RETURNS TABLE
* WITH SCHEMABINDING
* AS
* RETURN SELECT 1 AS access_result
* WHERE @salesperson = SalespersonName;
* Create a security policy on the Sales table that uses the SalesPredicate function to filter the data:
* CREATE SECURITY POLICY SalesFilter
* ADD FILTER PREDICATE dbo.SalesPredicate(dbo.GetCurrentUser()) ON dbo.Sales
* WITH (STATE = ON);
By creating a security policy for the Sales table, you ensure that each salesperson can only access their own sales data. The security policy uses a user-defined function to get the name of the current user and a security predicate function to filter the Sales table based on the current user.
Box 2: table-value function
to restrict row access by using row-level security, you need to create a table-valued function that returns a table of values that represent the rows that a user can access. You then use this function in a security policy that applies a predicate on the table.

NEW QUESTION # 55
You use Azure Data Factory to prepare data to be queried by Azure Synapse Analytics serverless SQL pools.
Files are initially ingested into an Azure Data Lake Storage Gen2 account as 10 small JSON files. Each file contains the same data attributes and data from a subsidiary of your company.
You need to move the files to a different folder and transform the data to meet the following requirements:
* Provide the fastest possible query times.
* Automatically infer the schema from the underlying files.
How should you configure the Data Factory copy activity? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Box 1: Preserver herarchy
Compared to the flat namespace on Blob storage, the hierarchical namespace greatly improves the performance of directory management operations, which improves overall job performance.
Box 2: Parquet
Azure Data Factory parquet format is supported for Azure Data Lake Storage Gen2.
Parquet supports the schema property.
Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-introduction
https://docs.microsoft.com/en-us/azure/data-factory/format-parquet

NEW QUESTION # 56
You are designing an Azure Stream Analytics job to process incoming events from sensors in retail environments.
You need to process the events to produce a running average of shopper counts during the previous 15 minutes, calculated at five-minute intervals.
Which type of window should you use?

A. tumbling
B. snapshot
C. hopping
D. sliding

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 # 57
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