Explanation

Box 1: Interactive Query
Choose Interactive Query cluster type to optimize for ad hoc, interactive queries.
Box 2: Hadoop
Choose Apache Hadoop cluster type to optimize for Hive queries used as a batch process.
Note: In Azure HDInsight, there are several cluster types and technologies that can run Apache Hive queries.
When you create your HDInsight cluster, choose the appropriate cluster type to help optimize performance for your workload needs.
For example, choose Interactive Query cluster type to optimize for ad hoc, interactive queries. Choose Apache Hadoop cluster type to optimize for Hive queries used as a batch process. Spark and HBase cluster types can also run Hive queries.
References:
https://docs.microsoft.com/bs-latn-ba/azure/hdinsight/hdinsight-hadoop-optimize-hive-query?toc=%2Fko-kr%2F

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

Explanation
Azure Stream Analytics (ASA) on IoT Edge empowers developers to deploy near-real-time analytical intelligence closer to IoT devices so that they can unlock the full value of device-generated data.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-edge

Explanation

Box 1: Replicated
Replicated tables are ideal for small star-schema dimension tables, because the fact table is often distributed on a column that is not compatible with the connected dimension tables. If this case applies to your schema, consider changing small dimension tables currently implemented as round-robin to replicated.
Box 2: Replicated
Box 3: Replicated
Box 4: Hash-distributed
For Fact tables use hash-distribution with clustered columnstore index. Performance improves when two hash tables are joined on the same distribution column.
References:
https://azure.microsoft.com/en-us/updates/reduce-data-movement-and-make-your-queries-more-efficient-with-th
https://azure.microsoft.com/en-us/blog/replicated-tables-now-generally-available-in-azure-sql-data-warehouse/

Scenario: ADatum identifies the following requirements for the Health Interface application:
* ..reads must display be the most recent committed version of an item.
Azure Cosmos DB consistency levels include:
Strong: Strong consistency offers a linearizability guarantee. Linearizability refers to serving requests concurrently. The reads are guaranteed to return the most recent committed version of an item. A client never sees an uncommitted or partial write. Users are always guaranteed to read the latest committed write.
Reference:
https://docs.microsoft.com/en-us/azure/cosmos-db/consistency-levels
Design Azure data storage solutions
Testlet 5
Overview
You are a data engineer for Trey Research. The company is close to completing a joint project with the government to build smart highways infrastructure across North America. This involves the placement of sensors and cameras to measure traffic flow, car speed, and vehicle details.
You have been asked to design a cloud solution that will meet the business and technical requirements of the smart highway.
Solution components
Telemetry Capture
The telemetry capture system records each time a vehicle passes in front of a sensor. The sensors run on a custom embedded operating system and record the following telemetry data:
* Time
* Location in latitude and longitude
* Speed in kilometers per hour (kmph)
* Length of vehicle in meters
Visual Monitoring
The visual monitoring system is a network of approximately 1,000 cameras placed near highways that capture images of vehicle traffic every 2 seconds. The cameras record high resolution images. Each image is approximately 3 MB in size.
Requirements: Business
The company identifies the following business requirements:
* External vendors must be able to perform custom analysis of data using machine learning technologies.
* You must display a dashboard on the operations status page that displays the following metrics: telemetry, volume, and processing latency.
* Traffic data must be made available to the Government Planning Department for the purpose of modeling changes to the highway system. The traffic data will be used in conjunction with other data such as information about events such as sporting events, weather conditions, and population statistics. External data used during the modeling is stored in on-premises SQL Server 2016 databases and CSV files stored in an Azure Data Lake Storage Gen2 storage account.
* Information about vehicles that have been detected as going over the speed limit during the last 30 minutes must be available to law enforcement officers. Several law enforcement organizations may respond to speeding vehicles.
* The solution must allow for searches of vehicle images by license plate to support law enforcement investigations. Searches must be able to be performed using a query language and must support fuzzy searches to compensate for license plate detection errors.
Requirements: Security
The solution must meet the following security requirements:
* External vendors must not have direct access to sensor data or images.
* Images produced by the vehicle monitoring solution must be deleted after one month. You must minimize costs associated with deleting images from the data store.
* Unauthorized usage of data must be detected in real time. Unauthorized usage is determined by looking for unusual usage patterns.
* All changes to Azure resources used by the solution must be recorded and stored. Data must be provided to the security team for incident response purposes.
Requirements: Sensor data
You must write all telemetry data to the closest Azure region. The sensors used for the telemetry capture system have a small amount of memory available and so must write data as quickly as possible to avoid losing telemetry data.