Explanation/Reference:
Explanation:
Image data must be stored in a single data store at minimum cost.
Note: Azure Blob storage is Microsoft's object storage solution for the cloud. Blob storage is optimized for storing massive amounts of unstructured data. Unstructured data is data that does not adhere to a particular data model or definition, such as text or binary data.
Blob storage is designed for:
Serving images or documents directly to a browser.

Storing files for distributed access.

Streaming video and audio.

Writing to log files.

Storing data for backup and restore, disaster recovery, and archiving.

Storing data for analysis by an on-premises or Azure-hosted service.

References:
https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction

Explanation
Virtual network rules are one firewall security feature that controls whether the database server for your single databases and elastic pool in Azure SQL Database or for your databases in SQL Data Warehouse accepts communications that are sent from particular subnets in virtual networks.
Server-level, not database-level: Each virtual network rule applies to your whole Azure SQL Database server, not just to one particular database on the server. In other words, virtual network rule applies at the serverlevel, not at the database-level.
References:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-vnet-service-endpoint-rule-overview
Topic 2, Trey Research 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 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 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

Layer 1: Azure Data Factory
Layer 2: Azure Databricks
Azure Databricks is fully integrated with Azure Data Factory .

References:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/

Ensure that files stored are larger, not smaller than 250MB.
You can have a separate compaction job that combines these files into larger ones.
Note: The file POSIX permissions and auditing in Data Lake Storage Gen1 comes with an overhead that becomes apparent when working with numerous small files. As a best practice, you must batch your data into larger files versus writing thousands or millions of small files to Data Lake Storage Gen1. Avoiding small file sizes can have multiple benefits, such as:
* Lowering the authentication checks across multiple files
* Reduced open file connections
* Faster copying/replication
* Fewer files to process when updating Data Lake Storage Gen1 POSIX permissions Reference:
https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-best-practices

Explanation
Scenario: ADatum identifies the following requirements for the Health Interface application:
Reduce the amount of development effort to rewrite existing SQL queries.
Upgrade to a data storage solution that will provide flexible schemas and increased throughput for writing data. Data must be regionally located close to each hospital, and reads must display be the most recent committed version of an item.
Reduce the amount of time it takes to add data from new hospitals to Health Interface.
Support a more scalable batch processing solution in Azure.