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Microsoft DP-203 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Monitor and optimize data solutions | 30-35% | - Optimize performance
|
| Secure data solutions | - Ensure compliance
| |
| Design and implement data storage | 25-30% | - Design data storage structures
|
| Develop data processing | 40-45% | - Develop data transformation logic
|
DP-203 Buyers' Frequently Asked Questions
The test engine simulates the real exam environment, records your performance after each session, and points out the questions you got wrong so you know where to concentrate. Once you answer a question correctly consistently, you can mark it as mastered and clear it from your rotation, keeping every practice round focused on what still needs work.
The Microsoft Data Engineering on Microsoft Azure exam contains 40-60 (approx.) questions, and you will have 120 minutes minutes to complete them. Simulating that combination in advance turns exam-day pacing into a habit.
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This package includes 365 practice questions for the Microsoft Data Engineering on Microsoft Azure exam. Every answer is verified by an experienced team of experts, so you can trust what you study.
The Microsoft Data Engineering on Microsoft Azure exam requires a score of 700 (out of 1000) to pass, and the official registration fee is $165 USD (varies by country/region). Knowing both figures before you begin helps you plan your preparation realistically.
You can register for the Microsoft Data Engineering on Microsoft Azure exam through the official channels:
No mandatory prerequisites. Recommended: familiarity with Azure data services, SQL, and basic data engineering concepts.
You can confirm the current eligibility requirements on the official DP-203 exam page before you register.
The Microsoft Data Engineering on Microsoft Azure practice questions are organized around the official exam objectives. Key topics include:
- Secure data solutions ()
- Monitor and optimize data solutions (30-35%)
- Design and implement data storage (25-30%)
Your practice results against these topics show you exactly where to spend your next session.
Microsoft Data Engineering on Microsoft Azure Sample Questions:
You need to implement an Azure Synapse Analytics database object for storing the sales transactions data.
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.
Correct Answer:
Explanation:
Box 1: Create table
Scenario: Load the sales transaction dataset to Azure Synapse Analytics Box 2: RANGE RIGHT FOR VALUES Scenario: Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong to the partition on the right.
RANGE RIGHT: Specifies the boundary value belongs to the partition on the right (higher values).
FOR VALUES ( boundary_value [,...n] ): Specifies the boundary values for the partition.
Scenario: Load the sales transaction dataset to Azure Synapse Analytics.
Contoso identifies the following requirements for the sales transaction dataset:
Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong to the partition on the right.
Ensure that queries joining and filtering sales transaction records based on product ID complete as quickly as possible.
Implement a surrogate key to account for changes to the retail store addresses.
Ensure that data storage costs and performance are predictable.
Minimize how long it takes to remove old records.
Reference:
https://docs.microsoft.com/en-us/sql/t-sql/statements/create-table-azure-sql-data-warehouse
You have an Azure Data Lake Storage Gen2 container that contains 100 TB of data.
You need to ensure that the data in the container is available for read workloads in a secondary region if an outage occurs in the primary region. The solution must minimize costs.
Which type of data redundancy should you use?
- A. zone-redundant storage (ZRS)
- B. locally-redundant storage (LRS)
- C. read-access geo-redundant storage (RA-GRS)
- D. geo-redundant storage (GRS)
Correct Answer: C 🗳️
Explanation: Only visible for ExamsTorrent members. You can sign-up / login (it's free).
You implement an enterprise data warehouse in Azure Synapse Analytics.
You have a large fact table that is 10 terabytes (TB) in size.
Incoming queries use the primary key SaleKey column to retrieve data as displayed in the following table:
You need to distribute the large fact table across multiple nodes to optimize performance of the table.
Which technology should you use?
- A. hash distributed table with clustered index
- B. round robin distributed table with clustered Columnstore index
- C. round robin distributed table with clustered index
- D. heap table with distribution replicate
- E. hash distributed table with clustered Columnstore index
Correct Answer: E 🗳️
Explanation: Only visible for ExamsTorrent members. You can sign-up / login (it's free).
You use PySpark in Azure Databricks to parse the following JSON input.
You need to output the data in the following tabular format.
How should you complete the PySpark code? To answer, drag the appropriate values to he 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.
Correct Answer:
Explanation:
Box 1: select
Box 2: explode
Bop 3: alias
pyspark.sql.Column.alias returns this column aliased with a new name or names (in the case of expressions that return more than one column, such as explode).
Reference:
https://spark.apache.org/docs/latest/api/python/reference/api/pyspark.sql.Column.alias.html
https://docs.microsoft.com/en-us/azure/databricks/sql/language-manual/functions/explode
You have an Azure Data Lake Storage account named account1.
You use an Azure Synapse Analytics serverless SQL pool to access sales data stored in account1.
You need to create a bar chart that displays sales by product. The solution must minimize development effort.
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
Correct Answer:
Explanation:
To create a bar chart that displays sales by product while minimizing development effort, follow these steps in the correct order:
Correct Order
* Create a SQL script by using Synapse Studio.
* Start by writing a SQL query in Synapse Studio that retrieves the sales by product data from the data stored in Azure Data Lake.
* Add a SELECT statement that will return the sales by product data.
* Write the query in the SQL script, such as:
sql
Copy
SELECT ProductName, SUM(SalesAmount) AS TotalSales
FROM OPENROWSET(
BULK 'https://account1.dfs.core.windows.net/salesdata/',
FORMAT='CSV',
DATA_SOURCE='MyDataSource'
) AS SalesData
GROUP BY ProductName
ORDER BY TotalSales DESC;
* Execute the script.
* Run the SQL script to retrieve the data and ensure it returns the correct results.
* Switch to the Chart view.
* In Synapse Studio, switch to the Chart view to visualize the query results.
* Modify the Chart settings.
* Customize the chart settings to display a bar chart with "ProductName" on the x-axis and
"TotalSales" on the y-axis.
Explanation:
* The sequence ensures that you first retrieve the data correctly, verify it by executing the query, and then switch to the Chart view to visualize the data with minimal development effort.
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