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Practice on 2024 LATEST DP-203 Exam Updated 318 Questions
Microsoft DP-203 certification exam is intended for data engineers who work with data scientists, data analysts, and business stakeholders to implement data solutions on the Microsoft Azure platform. DP-203 exam covers various topics such as Azure data storage, Azure data processing, Azure data integration, and data transformation. It also tests your knowledge of key Azure services such as Azure Data Factory, Azure Synapse Analytics, and Azure Stream Analytics.
NEW QUESTION # 119
that has the activity 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.
Answer:
Explanation:
Explanation
NEW QUESTION # 120
You are designing 2 solution that will use tables in Delta Lake on Azure Databricks.
You need to minimize how long it takes to perform the following:
*Queries against non-partitioned tables
* Joins on non-partitioned columns
Which two options should you include in the solution? Each correct answer presents part of the solution.
(Choose Correct Answer and Give Explanation and Reference to Support the answers based from Data Engineering on Microsoft Azure)
- A. Z-Ordering
- B. the clone command
- C. Apache Spark caching
- D. dynamic file pruning (DFP)
Answer: A,C
Explanation:
1. Z-Ordering: Z-Ordering improves query performance by co-locating data that share the same column values in the same physical partitions. This reduces the need for shuffling data across nodes during query execution. By using Z-Ordering, you can avoid full table scans and reduce the amount of data processed.
2. Apache Spark caching: Caching data in memory can improve query performance by reducing the amount of data read from disk. This helps to speed up subsequent queries that need to access the same data. When you cache a table, the data is read from the data source and stored in memory. Subsequent queries can then read the data from memory, which is much faster than reading it from disk.
Explanation:
According to the information I found on the web, two options that you should include in the solution to minimize how long it takes to perform queries and joins on non-partitioned tables are:
Z-Ordering: This is a technique to colocate related information in the same set of files. This co-locality is automatically used by Delta Lake in data-skipping algorithms. This behavior dramatically reduces the amount of data that Delta Lake on Azure Databricks needs to read123.
Apache Spark caching: This is a feature that allows you to cache data in memory or on disk for faster access. Caching can improve the performance of repeated queries and joins on the same data. You can cache Delta tables using the CACHE TABLE or CACHE LAZY commands.
To minimize the time it takes to perform queries against non-partitioned tables and joins on non-partitioned columns in Delta Lake on Azure Databricks, the following options should be included in the solution:
Reference:
Delta Lake on Databricks: https://docs.databricks.com/delta/index.html
Best Practices for Delta Lake on Databricks: https://databricks.com/blog/2020/05/14/best-practices-for-delta-lake-on-databricks.html
NEW QUESTION # 121
You plan to create a real-time monitoring app that alerts users when a device travels more than 200 meters away from a designated location.
You need to design an Azure Stream Analytics job to process the data for the planned app. The solution must minimize the amount of code developed and the number of technologies used.
What should you include in the Stream Analytics job? 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/stream-analytics/stream-analytics-get-started-with-azure-stream-analytics-to-process-data-from-iot-devices
https://docs.microsoft.com/en-us/azure/stream-analytics/geospatial-scenarios
NEW QUESTION # 122
You have an Azure subscription.
You plan to build a data warehouse in an Azure Synapse Analytics dedicated SQL pool named pool1 that will contain staging tables and a dimensional model Pool1 will contain the following tables.

Answer:
Explanation:
NEW QUESTION # 123
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.
Answer:
Explanation:
NEW QUESTION # 124
From a website analytics system, you receive data extracts about user interactions such as downloads, link clicks, form submissions, and video plays.
The data contains the following columns.
You need to design a star schema to support analytical queries of the data. The star schema will contain four tables including a date dimension.
To which table should you add each column? 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/power-bi/guidance/star-schema
NEW QUESTION # 125
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:
NEW QUESTION # 126
You plan to create an Azure Data Lake Storage Gen2 account
You need to recommend a storage solution that meets the following requirements:
* Provides the highest degree of data resiliency
* Ensures that content remains available for writes if a primary data center fails What should you include in the recommendation? To answer, select the appropriate options in the answer area.
Answer:
Explanation:
Explanation
Graphical user interface, text, application Description automatically generated
Availability : "Microsoft recommends RA-GZRS for maximum availability and durability for your applications." Failover: "The customer initiates the account failover to the secondary endpoint. "
https://docs.microsoft.com/en-us/azure/storage/common/storage-disaster-recovery-guidance?toc=/azure/storage/b
https://docs.microsoft.com/en-us/answers/questions/32583/azure-data-lake-gen2-disaster-recoverystorage-acco.h
NEW QUESTION # 127
You develop a dataset named DBTBL1 by using Azure Databricks.
DBTBL1 contains the following columns:
SensorTypeID
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.
Answer:
Explanation:
NEW QUESTION # 128
You are implementing a star schema in an Azure Synapse Analytics dedicated SQL pool.
You plan to create a table named DimProduct.
DimProduct must be a Type 3 slowly changing dimension (SCO) table that meets the following requirements:
* The values in two columns named ProductKey and ProductSourceID will remain the same.
* The values in three columns named ProductName, ProductDescription, and Color can change.
You need to add additional columns to complete the following table definition.
- A.

- B.

- C.

- D.

- E.

- F.

Answer: C,D,E
NEW QUESTION # 129
You are building an Azure Synapse Analytics dedicated SQL pool that will contain a fact table for transactions from the first half of the year 2020.
You need to ensure that the table meets the following requirements:
Minimizes the processing time to delete data that is older than 10 years Minimizes the I/O for queries that use year-to-date values How should you complete the Transact-SQL statement? 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/sql/t-sql/statements/create-partition-function-transact-sql
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?
- A. schedule
- B. on-demand
- C. tumbling window
- D. event
Answer: D
Explanation:
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 # 131
You have an Azure subscription that contains an Azure Synapse Analytics workspace and a user named Used.
You need to ensure that User1 can review the Azure Synapse Analytics database templates from the gallery.
The solution must follow the principle of least privilege.
Which role should you assign to User1?
- A. Synapse Contributor
- B. Synapse User
- C. Storage blob Data Contributor
- D. Synapse Administrator
Answer: B
NEW QUESTION # 132
You plan to create an Azure Data Factory pipeline that will include a mapping data flow.
You have JSON data containing objects that have nested arrays.
You need to transform the JSON-formatted data into a tabular dataset. The dataset must have one tow for each item in the arrays.
Which transformation method should you use in the mapping data flow?
- A. flatten
- B. unpivot
- C. new branch
- D. alter row
Answer: B
NEW QUESTION # 133
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 # 134
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