Explanation/Reference:
Explanation:
For large graphs with lots of entities and relationships, you can perform very complex analyses very quickly. Many graph databases provide a query language that you can use to traverse a network of relationships efficiently.
Relevant Azure service: Cosmos DB
References:
https://docs.microsoft.com/en-us/azure/architecture/guide/technology-choices/data-store-overview

Explanation

Box 1: Self-hosted integration runtime
A self-hosted IR is capable of nunning copy activity between a cloud data stores and a data store in private network.
Scenario: Daily inventory data comes from a Microsoft SQL server located on a private network.
Box 2: Schedule trigger
Daily schedule
Box 3: Copy activity
Scenario:
Stage inventory data in Azure Data Lake Storage Gen2 before loading the data into the analytical data store.
Litware wants to remove transient data from Data Lake Storage once the data is no longer in use. Files that have a modified date that is older than 14 days must be removed.

Explanation

Box 1: Hash-distributed
Box 2: ProductKey
ProductKey is used extensively in joins.
Hash-distributed tables improve query performance on large fact tables.
Box 3: Round-robin
Box 4: RegionKey
Round-robin tables are useful for improving loading speed.
Consider using the round-robin distribution for your table in the following scenarios:
* When getting started as a simple starting point since it is the default
* If there is no obvious joining key
* If there is not good candidate column for hash distributing the table
* If the table does not share a common join key with other tables
* If the join is less significant than other joins in the query
* When the table is a temporary staging table
Note: A distributed table appears as a single table, but the rows are actually stored across 60 distributions. The rows are distributed with a hash or round-robin algorithm.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables-distribute

Explanation

Tagging data must be uploaded to the cloud from the New York office location.
Tagging data must be replicated to regions that are geographically close to company office locations.

Auditing for Azure SQL Database and SQL Data Warehouse tracks database events and writes them to an audit log in your Azure storage account, Log Analytics workspace or Event Hubs.
Alerts in Azure Monitor can identify important information in your Log Analytics repository. They are created by alert rules that automatically run log searches at regular intervals, and if results of the log search match particular criteria, then an alert record is created and it can be configured to perform an automated response.
Reference:
https://docs.microsoft.com/en-us/azure/sql-database/sql-database-auditing
https://docs.microsoft.com/en-us/azure/azure-monitor/learn/tutorial-response