DP-203 Practice Test

Contoso has three years of customer, transactional, operation, sourcing, and supplier data
comprised of 10 billion records stored across multiple on-premises Microsoft SQL Server
servers. The SQL server instances contain data from various operational systems. The
data is loaded into the instances by using SQL server integration Services (SSIS)
packages.
You estimate that combining all product sales transactions into a company-wide sales
transactions dataset will result in a single table that contains 5 billion rows, with one row
per transaction.
Most queries targeting the sales transactions data will be used to identify which products
were sold in retail stores and which products were sold online during different time period.
Sales transaction data that is older than three years will be removed monthly.
You plan to create a retail store table that will contain the address of each retail store. The
table will be approximately 2 MB. Queries for retail store sales will include the retail store
addresses.
You plan to create a promotional table that will contain a promotion ID. The promotion ID
will be associated to a specific product. The product will be identified by a product ID. The
table will be approximately 5 GB.
Streaming Twitter Data
The ecommerce department at Contoso develops and Azure logic app that captures
trending Twitter feeds referencing the company’s products and pushes the products to
Azure Event Hubs.
Planned Changes
Contoso plans to implement the following changes:
* Load the sales transaction dataset to Azure Synapse Analytics.
* Integrate on-premises data stores with Azure Synapse Analytics by using SSIS packages.
* Use Azure Synapse Analytics to analyze Twitter feeds to assess customer sentiments
about products.
Sales Transaction Dataset Requirements
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.
Customer Sentiment Analytics Requirement
Contoso identifies the following requirements for customer sentiment analytics:
• Allow Contoso users to use PolyBase in an A/ure Synapse Analytics dedicated SQL pool
to query the content of the data records that host the Twitter feeds. Data must be protected
by using row-level security (RLS). The users must be authenticated by using their own
A/ureAD credentials.
• Maximize the throughput of ingesting Twitter feeds from Event Hubs to Azure Storage
without purchasing additional throughput or capacity units.
• Store Twitter feeds in Azure Storage by using Event Hubs Capture. The feeds will be
converted into Parquet files.
• Ensure that the data store supports Azure AD-based access control down to the object
level.
• Minimize administrative effort to maintain the Twitter feed data records.
• Purge Twitter feed data records;itftaitJ are older than two years.
Data Integration Requirements
Contoso identifies the following requirements for data integration:
Use an Azure service that leverages the existing SSIS packages to ingest on-premises
data into datasets stored in a dedicated SQL pool of Azure Synaps Analytics and transform
the data.
Identify a process to ensure that changes to the ingestion and transformation activities can
be version controlled and developed independently by multiple data engineers.