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Question 1:

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 2:

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 3:

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 4:

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

Question 5:

What should you do to improve the high availability of the real-time data processing solution?

A. Deploy a High Concurrency Databricks cluster.

B. Deploy an Azure Stream Analytics job and use an Azure Automation runbook to check the status of the job and to start the job if it stops.

C. Set Data Lake Storage to use geo-redundant storage (GRS).

D. Deploy identical Azure Stream Analytics jobs to paired regions in Azure.

Correct Answer: D

Guarantee Stream Analytics job reliability during service updates Part of being a fully managed service is the capability to introduce new service functionality and improvements at a rapid pace. As a result, Stream Analytics can have a service update deployed on a weekly (or more frequent) basis. No matter how much testing is done there is still a risk that an existing, running job may break due to the introduction of a bug. If you are running mission-critical jobs, these risks need to be avoided. You can reduce this risk by following Azure\’s paired region model.

Scenario: The application development team will create an Azure event hub to receive real-time sales data, including store number, date, time, product ID, customer loyalty number, price, and the discount amount, from the point of sale (POS) system and output the data to data storage in Azure

Reference: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-job-reliability

Question 6:

What should you recommend to prevent users outside the Litware on-premises network from accessing the analytical data store?

A. a server-level virtual network rule

B. a database-level virtual network rule

C. a server-level firewall IP rule

D. a database-level firewall IP rule

Correct Answer: C

Scenario:

1.

Ensure that the analytical data store is accessible only to the company\’s on-premises network and Azure services.

2.

Litware does not plan to implement Azure ExpressRoute or a VPN between the on-premises network and Azure.

Since Litware does not plan to implement Azure ExpressRoute or a VPN between the on-premises network and Azure, it will have to create firewall IP rules to allow connection from the IP ranges of the on-premise network. They can also use the firewall rule 0.0.0.0 to allow access from Azure services.

Reference: https://docs.microsoft.com/en-us/azure/sql-database/sql-database-vnet-service-endpoint-rule-overview

Question 7:

What should you recommend using to secure sensitive customer contact information?

A. Transparent Data Encryption (TDE)

B. row-level security

C. column-level security

D. data sensitivity labels

Correct Answer: C

Scenario: All cloud data must be encrypted at rest and in transit.

Always Encrypted is a feature designed to protect sensitive data stored in specific database columns from access (for example, credit card numbers, national identification numbers, or data on a need-to-know basis). This includes database administrators or other privileged users who are authorized to access the database to perform management tasks, but have no business need to access the particular data in the encrypted columns. The data is always encrypted, which means the encrypted data is decrypted only for processing by client applications with access to the encryption key.

References: https://docs.microsoft.com/en-us/azure/sql-database/sql-database-security-overview

Question 8:

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 9:

You have a table in an Azure Synapse Analytics dedicated SQL pool. The table was created by using the following Transact-SQL statement.

You need to alter the table to meet the following requirements:

Ensure that users can identify the current manager of employees.

Support creating an employee reporting hierarchy for your entire company.

Provide fast lookup of the managers\’ attributes such as name and job title.

Which column should you add to the table?

A. [ManagerEmployeeID] [int] NULL

B. [ManagerEmployeeID] [smallint] NULL

C. [ManagerEmployeeKey] [int] NULL

D. [ManagerName] [varchar](200) NULL

Correct Answer: C

We need an extra column to identify the Manager. Use the data type as the EmployeeKey column, an int column.

Reference: https://docs.microsoft.com/en-us/analysis-services/tabular-models/hierarchies-ssas-tabular

Question 10:

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 11:

You have files and folders in Azure Data Lake Storage Gen2 for an Azure Synapse workspace as shown in the following exhibit.

You create an external table named ExtTable that has LOCATION=\’/topfolder/\’.

When you query ExtTable by using an Azure Synapse Analytics serverless SQL pool, which files are returned?

A. File2.csv and File3.csv only

B. File1.csv and File4.csv only

C. File1.csv, File2.csv, File3.csv, and File4.csv

D. File1.csv only

Correct Answer: B

To run a T-SQL query over a set of files within a folder or set of folders while treating them as a single entity or rowset, provide a path to a folder or a pattern (using wildcards) over a set of files or folders.

Reference: https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/query-data-storage#query-multiple-files-or-folders

Question 12:

You are designing the folder structure for an Azure Data Lake Storage Gen2 container.

Users will query data by using a variety of services including Azure Databricks and Azure Synapse Analytics serverless SQL pools. The data will be secured by the subject area. Most queries will include data from the current year or current

month.

Which folder structure should you recommend to support fast queries and simplified folder security?

A. /{SubjectArea}/{DataSource}/{DD}/{MM}/{YYYY}/{FileData}_{YYYY}_{MM}_{DD}.csv

B. /{DD}/{MM}/{YYYY}/{SubjectArea}/{DataSource}/{FileData}_{YYYY}_{MM}_{DD}.csv

C. /{YYYY}/{MM}/{DD}/{SubjectArea}/{DataSource}/{FileData}_{YYYY}_{MM}_{DD}.csv

D. /{SubjectArea}/{DataSource}/{YYYY}/{MM}/{DD}/{FileData}_{YYYY}_{MM}_{DD}.csv

Correct Answer: D

There\’s an important reason to put the date at the end of the directory structure. If you want to lock down certain regions or subject matters to users/groups, then you can easily do so with the POSIX permissions. Otherwise, if there was a need to restrict a certain security group to viewing just the UK data or certain planes, with the date structure in front a separate permission would be required for numerous directories under every hour directory. Additionally, having the date structure in front would exponentially increase the number of directories as time went on.

Note: In IoT workloads, there can be a great deal of data being landed in the data store that spans across numerous products, devices, organizations, and customers. It\’s important to pre-plan the directory layout for organization, security, and efficient processing of the data for down-stream consumers. A general template to consider might be the following layout:

{Region}/{SubjectMatter(s)}/{yyyy}/{mm}/{dd}/{hh}/

Question 13:

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 14:

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 15:

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

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