Microsoft Microsoft Certified: Azure Data Engineer Associate DP-203 Questions & Answers

• Question 311:

  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?

  A. replicated

  B. hash-distributed on PurchaseKey

  C. round-robin

  D. hash-distributed on DateKey

  Correct Answer: B

  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

• Question 312:

  You are creating an Azure Data Factory data flow that will ingest data from a CSV file, cast columns to specified types of data, and insert the data into a table in an Azure Synapse Analytic dedicated SQL pool. The CSV file contains three columns named username, comment, and date.

  The data flow already contains the following:

  1.

  A source transformation.

  2.

  A Derived Column transformation to set the appropriate types of data.

  3.

  A sink transformation to land the data in the pool.

  You need to ensure that the data flow meets the following requirements:

  1.

  All valid rows must be written to the destination table.

  2.

  Truncation errors in the comment column must be avoided proactively.

  3.

  Any rows containing comment values that will cause truncation errors upon insert must be written to a file in blob storage.

  Which two actions should you perform? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. To the data flow, add a sink transformation to write the rows to a file in blob storage.

  B. To the data flow, add a Conditional Split transformation to separate the rows that will cause truncation errors.

  C. To the data flow, add a filter transformation to filter out rows that will cause truncation errors.

  D. Add a select transformation to select only the rows that will cause truncation errors.

  Correct Answer: AB

  B: Example:

  1.

  This conditional split transformation defines the maximum length of "title" to be five. Any row that is less than or equal to five will go into the GoodRows stream. Any row that is larger than five will go into the BadRows stream.

  2.

  This conditional split transformation defines the maximum length of "title" to be five. Any row that is less than or equal to five will go into the GoodRows stream. Any row that is larger than five will go into the BadRows stream.

  

  A:

  3.

  Now we need to log the rows that failed. Add a sink transformation to the BadRows stream for logging. Here, we'll "auto-map" all of the fields so that we have logging of the complete transaction record. This is a text-delimited CSV file output to a single file in Blob Storage. We'll call the log file "badrows.csv".

  4.

  The completed data flow is shown below. We are now able to split off error rows to avoid the SQL truncation errors and put those entries into a log file. Meanwhile, successful rows can continue to write to our target database.

  

  

  Reference: https://docs.microsoft.com/en-us/azure/data-factory/how-to-data-flow-error-rows

• Question 313:

  You have an enterprise-wide Azure Data Lake Storage Gen2 account. The data lake is accessible only through an Azure virtual network named VNET1.

  You are building a SQL pool in Azure Synapse that will use data from the data lake.

  Your company has a sales team. All the members of the sales team are in an Azure Active Directory group named Sales. POSIX controls are used to assign the Sales group access to the files in the data lake.

  You plan to load data to the SQL pool every hour.

  You need to ensure that the SQL pool can load the sales data from the data lake.

  Which three actions should you perform? Each correct answer presents part of the solution.

  NOTE: Each area selection is worth one point.

  A. Add the managed identity to the Sales group.

  B. Use the managed identity as the credentials for the data load process.

  C. Create a shared access signature (SAS).

  D. Add your Azure Active Directory (Azure AD) account to the Sales group.

  E. Use the snared access signature (SAS) as the credentials for the data load process.

  F. Create a managed identity.

  Correct Answer: ADF

  The managed identity grants permissions to the dedicated SQL pools in the workspace.

  Note: Managed identity for Azure resources is a feature of Azure Active Directory. The feature provides Azure services with an automatically managed identity in Azure AD

  Reference:

  https://docs.microsoft.com/en-us/azure/synapse-analytics/security/synapse-workspace-managed-identity

• Question 314:

  You need to design an Azure Synapse Analytics dedicated SQL pool that meets the following requirements:

  1.

  Can return an employee record from a given point in time.

  2.

  Maintains the latest employee information.

  3.

  Minimizes query complexity. How should you model the employee data?

  A. as a temporal table

  B. as a SQL graph table

  C. as a degenerate dimension table

  D. as a Type 2 slowly changing dimension (SCD) table

  Correct Answer: D

  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.

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

• Question 315:

  You have an Azure Synapse workspace named MyWorkspace that contains an Apache Spark database named mytestdb.

  You run the following command in an Azure Synapse Analytics Spark pool in MyWorkspace.

  CREATE TABLE mytestdb.myParquetTable(EmployeeID int,EmployeeName string,EmployeeStartDate date)

  USING Parquet

  You then use Spark to insert a row into mytestdb.myParquetTable. The row contains the following data.

  

  One minute later, you execute the following query from a serverless SQL pool in MyWorkspace.

  SELECT EmployeeIDFROM mytestdb.dbo.myParquetTableWHERE EmployeeName = 'Alice';

  What will be returned by the query?

  A. 24

  B. an error

  C. a null value

  Correct Answer: B

  Once a database has been created by a Spark job, you can create tables in it with Spark that use Parquet as the storage format. Table names will be converted to lower case and need to be queried using the lower case name. These tables will immediately become available for querying by any of the Azure Synapse workspace Spark pools. They can also be used from any of the Spark jobs subject to permissions.

  Note: For external tables, since they are synchronized to serverless SQL pool asynchronously, there will be a delay until they appear.

  Reference: https://docs.microsoft.com/en-us/azure/synapse-analytics/metadata/table

• Question 316:

  You need to implement the surrogate key for the retail store table. The solution must meet the sales transaction dataset requirements. What should you create?

  A. a table that has an IDENTITY property

  B. a system-versioned temporal table

  C. a user-defined SEQUENCE object

  D. a table that has a FOREIGN KEY constraint

  Correct Answer: A

  Scenario: Implement a surrogate key to account for changes to the retail store addresses.

  A surrogate key on a table is a column with a unique identifier for each row. The key is not generated from the table data. Data modelers like to create surrogate keys on their tables when they design data warehouse models. You can use the IDENTITY property to achieve this goal simply and effectively without affecting load performance.

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

• Question 317:

  You have an Azure Storage account and a data warehouse in Azure Synapse Analytics in the UK South region.

  You need to copy blob data from the storage account to the data warehouse by using Azure Data Factory. The solution must meet the following requirements:

  Ensure that the data remains in the UK South region at all times.

  Minimize administrative effort.

  Which type of integration runtime should you use?

  A. Azure integration runtime

  B. Azure-SSIS integration runtime

  C. Self-hosted integration runtime

  Correct Answer: A

  

  Incorrect Answers:

  C: Self-hosted integration runtime is to be used On-premises.

  Reference: https://docs.microsoft.com/en-us/azure/data-factory/concepts-integration-runtime

• Question 318:

  You need to design a data retention solution for the Twitter teed data records. The solution must meet the customer sentiment analytics requirements.

  Which Azure Storage functionality should you include in the solution?

  A. time-based retention

  B. change feed

  C. soft delete

  D. Iifecycle management

  Correct Answer: D

  Scenario: Purge Twitter feed data records that are older than two years.

  Data sets have unique lifecycles. Early in the lifecycle, people access some data often. But the need for access often drops drastically as the data ages. Some data remains idle in the cloud and is rarely accessed once stored. Some data sets

  expire days or months after creation, while other data sets are actively read and modified throughout their lifetimes.

  Azure Storage lifecycle management offers a rule-based policy that you can use to transition blob data to the appropriate access tiers or to expire data at the end of the data lifecycle.

  Reference:

  https://docs.microsoft.com/en-us/azure/storage/blobs/lifecycle-management-overview

• Question 319:

  You need to integrate the on-premises data sources and Azure Synapse Analytics. The solution must meet the data integration requirements. Which type of integration runtime should you use?

  A. Azure-SSIS integration runtime

  B. self-hosted integration runtime

  C. Azure integration runtime

  Correct Answer: C