Scenario: SalesSQLDb1 uses database firewall rules and contained database users.
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
Topic 3, ADatum Corporation
Existing Environment
ADatum has one database server that has Microsoft SQL Server 2016 installed. The server hosts three mission-critical databases named SALESDB, DOCDB, and REPORTINGDB.
SALESDB collects data from the stores and the website.
DOCDB stores documents that connect to the sales data in SALESDB. The documents are stored in two different JSON formats based on the sales channel.
REPORTINGDB stores reporting data and contains several columnstore indexes. A daily process creates reporting data in REPORTINGDB from the data in SALESDB. The process is implemented as a SQL Server Integration Services (SSIS) package that runs a stored procedure from SALESDB.
Requirements
Planned Changes
ADatum plans to move the current data infrastructure to Azure. The new infrastructure has the following requirements:
Migrate SALESDB and REPORTINGDB to an Azure SQL database.
Migrate DOCDB to Azure Cosmos DB.
The sales data, including the documents in JSON format, must be gathered as it arrives and analyzed online by using Azure Stream Analytics. The analytics process will perform aggregations that must be done continuously, without gaps, and without overlapping.
As they arrive, all the sales documents in JSON format must be transformed into one consistent format.
Azure Data Factory will replace the SSIS process of copying the data from SALESDB to REPORTINGDB.
Technical Requirements
The new Azure data infrastructure must meet the following technical requirements:
Data in SALESDB must encrypted by using Transparent Data Encryption (TDE). The encryption must use your own key.
SALESDB must be restorable to any given minute within the past three weeks.
Real-time processing must be monitored to ensure that workloads are sized properly based on actual usage patterns.
Missing indexes must be created automatically for REPORTINGDB.
Disk IO, CPU, and memory usage must be monitored for SALESDB.

Section: [none]
Explanation:
Box 1: Hash
Consider using a hash-distributed table when:
The table size on disk is more than 2 GB.
The table has frequent insert, update, and delete operations.
Box 2: Clustered columnstore
Clustered columnstore tables offer both the highest level of data compression and the best overall query performance.
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables- distribute
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-index

Section: [none]
Explanation:
In SQL Server, you can also minimize locking contention while protecting transactions from dirty reads of uncommitted data modifications using either:
* The READ COMMITTED isolation level with the READ_COMMITTED_SNAPSHOT database option set to ON.
* The SNAPSHOT isolation level.
If READ_COMMITTED_SNAPSHOT is set to ON (the default on SQL Azure Database), the Database Engine uses row versioning to present each statement with a transactionally consistent snapshot of the data as it existed at the start of the statement. Locks are not used to protect the data from updates by other transactions.
Incorrect Answers:
A: When the PARAMETERIZATION database option is set to SIMPLE, the SQL Server query optimizer may choose to parameterize the queries. This means that any literal values that are contained in a query are substituted with parameters. This process is referred to as simple parameterization. When SIMPLE parameterization is in effect, you cannot control which queries are parameterized and which queries are not.
B: You can specify that all queries in a database be parameterized by setting the PARAMETERIZATION database option to FORCED. This process is referred to as forced parameterization.
C: Delayed transaction durability is accomplished using asynchronous log writes to disk. Transaction log records are kept in a buffer and written to disk when the buffer fills or a buffer flushing event takes place.
Delayed transaction durability reduces both latency and contention within the system.
Some of the cases in which you could benefit from using delayed transaction durability are:
* You can tolerate some data loss.
* You are experiencing a bottleneck on transaction log writes.
* Your workloads have a high contention rate.
Reference:
https://docs.microsoft.com/en-us/sql/t-sql/statements/set-transaction-isolation-level-transact-sql

Section: [none]
Explanation:
Activity log events - An alert can trigger on every event, or, only when a certain number of events occur.
Incorrect Answers:
C: Metric values - The alert triggers when the value of a specified metric crosses a threshold you assign in
either direction. That is, it triggers both when the condition is first met and then afterwards when that condition
is no longer being met.
Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/alerts-insights-configure-portal

Explanation/Reference:
Explanation:
You can override the default language by specifying the language magic command %<language> at the beginning of a cell. The supported magic commands are: %python, %r, %scala, and %sql.
Reference:
https://docs.microsoft.com/en-us/azure/databricks/notebooks/notebooks-use