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/

Explanation
To keep an interactive cluster configuration even after it has been terminated for more than 30 days, an administrator can pin a cluster to the cluster list.
References:
https://docs.azuredatabricks.net/clusters/clusters-manage.html#automatic-termination

A Hyperscale database is an Azure SQL database in the Hyperscale service tier that is backed by the Hyperscale scale-out storage technology. A Hyperscale database supports up to 100 TB of data and provides high throughput and performance, as well as rapid scaling to adapt to the workload requirements. Scaling is transparent to the application - connectivity, query processing, etc. work like any other Azure SQL database.
Incorrect Answers:
A: SQL Database elastic pools are a simple, cost-effective solution for managing and scaling multiple databases that have varying and unpredictable usage demands. The databases in an elastic pool are on a single Azure SQL Database server and share a set number of resources at a set price. Elastic pools in Azure SQL Database enable SaaS developers to optimize the price performance for a group of databases within a prescribed budget while delivering performance elasticity for each database.
B: Rather than SQL Data Warehouse, consider other options for operational (OLTP) workloads that have large numbers of singleton selects.
Reference:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-service-tier-hyperscale-faq

Data Discovery & Classification introduces a set of advanced capabilities aimed at protecting data and not just the data warehouse itself. Classification/Labeling - Sensitivity classification labels tagged on the columns can be persisted in the data warehouse itself.
Reference:
https://azure.microsoft.com/sv-se/blog/announcing-public-preview-of-data-discovery-classification-for- microsoft-azure-sql-data-warehouse/ Design for data security and compliance Testlet 4 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 questions in 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 section 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 in 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.
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.

Explanation

Box 1: Yes
Box 2: No
autotermination_minutes: Automatically terminates the cluster after it is inactive for this time in minutes. If not set, this cluster will not be automatically terminated. If specified, the threshold must be between 10 and
10000 minutes. You can also set this value to 0 to explicitly disable automatic termination.
Box 3: Yes
References:
https://docs.databricks.com/dev-tools/api/latest/clusters.html