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Microsoft DP-203 Exam Questions
Data Engineering on Microsoft Azure (Page 11 )

Updated On: 17-Feb-2026
Page 11 of 75
PDF with 397 Questions

You are designing a fact table named FactPurchase in an Azure Synapse Analytics dedicated SQL pool. The table contains purchases from suppliers for a retail store. FactPurchase will contain the following columns.


FactPurchase will have 1 million rows of data added daily and will contain three years of data.
Transact-SQL queries similar to the following query will be executed daily.

SELECT
SupplierKey, StockItemKey, COUNT(*)
FROM FactPurchase
WHERE DateKey >= 20210101
AND DateKey <= 20210131
GROUP By SupplierKey, StockItemKey

Which table distribution will minimize query times?

  1. replicated
  2. hash-distributed on PurchaseKey
  3. round-robin
  4. hash-distributed on DateKey

Answer(s): B

Explanation:

Hash-distributed tables improve query performance on large fact tables, and are the focus of this article. Round-robin tables are useful for improving loading speed.

Incorrect:
Not D: Do not use a date column. . All data for the same date lands in the same distribution. If several users are all filtering on the same date, then only 1 of the 60 distributions do all the processing work.


Reference:

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



You are implementing a batch dataset in the Parquet format.
Data files will be produced be using Azure Data Factory and stored in Azure Data Lake Storage Gen2. The files will be consumed by an Azure Synapse Analytics serverless SQL pool.
You need to minimize storage costs for the solution. What should you do?

  1. Use Snappy compression for files.
  2. Use OPENROWSET to query the Parquet files.
  3. Create an external table that contains a subset of columns from the Parquet files.
  4. Store all data as string in the Parquet files.

Answer(s): A



DRAG DROP (Drag and Drop is not supported)
You need to build a solution to ensure that users can query specific files in an Azure Data Lake Storage Gen2 account from an Azure Synapse Analytics serverless SQL pool.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.
Select and Place:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




Step 1: Create an external data source
You can create external tables in Synapse SQL pools via the following steps:
1. CREATE EXTERNAL DATA SOURCE to reference an external Azure storage and specify the credential that should be used to access the storage.
2. CREATE EXTERNAL FILE FORMAT to describe format of CSV or Parquet files.
3. CREATE EXTERNAL TABLE on top of the files placed on the data source with the same file format.
Step 2: Create an external file format object
Creating an external file format is a prerequisite for creating an external table.
Step 3: Create an external table


Reference:

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/develop-tables-external-tables



You are designing a data mart for the human resources (HR) department at your company. The data mart will contain employee information and employee transactions.
From a source system, you have a flat extract that has the following fields:
-EmployeeID
-FirstName
-LastName
-Recipient
-GrossAmount
-TransactionID
-GovernmentID
-NetAmountPaid
-TransactionDate

You need to design a star schema data model in an Azure Synapse Analytics dedicated SQL pool for the data mart.

Which two tables should you create? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

  1. a dimension table for Transaction
  2. a dimension table for EmployeeTransaction
  3. a dimension table for Employee
  4. a fact table for Employee
  5. a fact table for Transaction

Answer(s): C,E

Explanation:

C: Dimension tables contain attribute data that might change but usually changes infrequently. For example, a customer's name and address are stored in a dimension table and updated only when the customer's profile changes. To minimize the size of a large fact table, the customer's name and address don't need to be in every row of a fact table. Instead, the fact table and the dimension table can share a customer ID. A query can join the two tables to associate a customer's profile and transactions.
E: Fact tables contain quantitative data that are commonly generated in a transactional system, and then loaded into the dedicated SQL pool. For example, a retail business generates sales transactions every day, and then loads the data into a dedicated SQL pool fact table for analysis.


Reference:

https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tablesoverview



You are designing a dimension table for a data warehouse. The table will track the value of the dimension attributes over time and preserve the history of the data by adding new rows as the data changes.
Which type of slowly changing dimension (SCD) should you use?

  1. Type 0
  2. Type 1
  3. Type 2
  4. Type 3

Answer(s): C

Explanation:

A Type 2 SCD supports versioning of dimension members. Often the source system doesn't store versions, so the data warehouse load process detects and manages changes in a dimension table. In this case, the dimension table must use a surrogate key to provide a unique reference to a version of the dimension member. It also includes columns that define the date range validity of the version (for example, StartDate and EndDate) and possibly a flag column (for example, IsCurrent) to easily filter by current dimension members.

Incorrect Answers:
B: A Type 1 SCD always reflects the latest values, and when changes in source data are detected, the dimension table data is overwritten.
D: A Type 3 SCD supports storing two versions of a dimension member as separate columns. The table includes a column for the current value of a member plus either the original or previous value of the member. So Type 3 uses additional columns to track one key instance of history, rather than storing additional rows to track each change like in a Type 2 SCD.


Reference:

https://docs.microsoft.com/en-us/learn/modules/populate-slowly-changing-dimensions-azure-synapseanalytics-pipelines/3-choose-between-dimension-types






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