Explanation
Choose a partition key that has a wide range of values and access patterns that are evenly spread across logical partitions. This helps spread the data and the activity in your container across the set of logical partitions, so that resources for data storage and throughput can be distributed across the logical partitions.
Choose a partition key that spreads the workload evenly across all partitions and evenly over time. Your choice of partition key should balance the need for efficient partition queries and transactions against the goal of distributing items across multiple partitions to achieve scalability.
Candidates for partition keys might include properties that appear frequently as a filter in your queries. Queries can be efficiently routed by including the partition key in the filter predicate.
References:
https://docs.microsoft.com/en-us/azure/cosmos-db/partitioning-overview#choose-partitionkey

Explanation
Azure SQL Data Warehouse is a cloud-based enterprise data warehouse that leverages massively parallel processing (MPP) to quickly run complex queries across petabytes of data. Use SQL Data Warehouse as a key component of a big data solution.
You can access Azure SQL Data Warehouse (SQL DW) from Databricks using the SQL Data Warehouse connector (referred to as the SQL DW connector), a data source implementation for Apache Spark that uses Azure Blob Storage, and PolyBase in SQL DW to transfer large volumes of data efficiently between a Databricks cluster and a SQL DW instance.
Scenario: ADatum identifies the following requirements for the Health Insights application:
* The new Health Insights application must be built on a massively parallel processing (MPP) architecture that will support the high performance of joins on large fact tables References:
https://docs.databricks.com/data/data-sources/azure/sql-data-warehouse.html

Explanation/Reference:
Explanation:
Stream Analytics has first-class integration with Azure data streams as inputs from three kinds of
resources:
Azure Event Hubs

Azure IoT Hub

Azure Blob storage

References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-define-inputs

DP-201
Testlet 1
Case study
This is a case study. Case studies are not timed separately. You can use as much exam time as you
would like to complete each case. However, there may be additional case studies and sections on this
exam. You must manage your time to ensure that you are able to complete all questions included on this
exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in
the case study. Case studies might contain exhibits and other resources that provide more information
about the scenario that is described in the case study. Each question is independent of the other question
on this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers
and to make changes before you move to the next sections of the exam. After you begin a new section,
you cannot return to this section.
To start the case study
To display the first question on this case study, click the Next button. Use the buttons in the left pane to
explore the content of the case study before you answer the questions. Clicking these buttons displays
information such as business requirements, existing environment, and problem statements. If the case
study has an All Information tab, note that the information displayed is identical to the information
displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to
return to the question.
Background
Trey Research is a technology innovator. The company partners with regional transportation department
office to build solutions that improve traffic flow and safety.
The company is developing the following solutions:

Regional transportation departments installed traffic sensor systems on major highways across North
America. Sensors record the following information each time a vehicle passes in front of a sensor:
Time

Location in latitude and longitude

Speed in kilometers per second (kmps)

License plate number

Length of vehicle in meters

Sensors provide data by using the following structure:

Traffic sensors will occasionally capture an image of a vehicle for debugging purposes.
You must optimize performance of saving/storing vehicle images.
Traffic sensor data
Sensors must have permission only to add items to the SensorData collection.

Traffic data insertion rate must be maximized.

Once every three months all traffic sensor data must be analyzed to look for data patterns that indicate

sensor malfunctions.
Sensor data must be stored in a Cosmos DB named treydata in a collection named SensorData

The impact of vehicle images on sensor data throughout must be minimized.

Backtrack
This solution reports on all data related to a specific vehicle license plate. The report must use data from
the SensorData collection. Users must be able to filter vehicle data in the following ways:
vehicles on a specific road

vehicles driving above the speed limit

Planning Assistance
Data used for Planning Assistance must be stored in a sharded Azure SQL Database.
Data from the Sensor Data collection will automatically be loaded into the Planning Assistance database
once a week by using Azure Data Factory. You must be able to manually trigger the data load process.
Privacy and security policy
Azure Active Directory must be used for all services where it is available.

For privacy reasons, license plate number information must not be accessible in Planning Assistance.

Unauthorized usage of the Planning Assistance data must be detected as quickly as possible.

Unauthorized usage is determined by looking for an unusual pattern of usage.
Data must only be stored for seven years.

Performance and availability
The report for Backtrack must execute as quickly as possible.

The SLA for Planning Assistance is 70 percent, and multiday outages are permitted.

All data must be replicated to multiple geographic regions to prevent data loss.

You must maximize the performance of the Real Time Response system.

Financial requirements
Azure resource costs must be minimized where possible.

Explanation

Box 1: Standard
The following table describes the types of storage accounts and their capabilities:

Box 2: Geo-redundant storage
If your storage account has GRS enabled, then your data is durable even in the case of a complete regional outage or a disaster in which the primary region isn't recoverable.
Note: If you opt for GRS, you have two related options to choose from:
GRS replicates your data to another data center in a secondary region, but that data is available to be read only if Microsoft initiates a failover from the primary to secondary region.
Read-access geo-redundant storage (RA-GRS) is based on GRS. RA-GRS replicates your data to another data center in a secondary region, and also provides you with the option to read from the secondary region. With RA-GRS, you can read from the secondary region regardless of whether Microsoft initiates a failover from the primary to secondary region.

References:
https://docs.microsoft.com/en-us/azure/storage/common/storage-introduction
https://docs.microsoft.com/en-us/azure/storage/common/storage-redundancy-grs

Explanation

References:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/batch-processing
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-service-tier-hyperscale