Microsoft DP-900 Practice Test - Questions Answers, Page 20

Data Manipulation Language (DML) affect the information stored in the database. Use these statements to insert, update, and change the rows in the database.

BULK INSERT

DELETE

INSERT

SELECT

UPDATE

MERGE

Reference:

https://docs.microsoft.com/en-us/sql/t-sql/statements/statements

Data Definition Language (DDL) statements defines data structures. Use these statements to create, alter, or drop data structures in a database. These statements include:

ALTER

Collations

CREATE

DROP

DISABLE TRIGGER

ENABLE TRIGGER

RENAME

UPDATE STATISTICS

TRUNCATE TABLE

Reference:

https://docs.microsoft.com/en-us/sql/t-sql/statements/statements

Azure Data Studio is a cross-platform database tool for data professionals who use on-premises and cloud data platforms on Windows, macOS, and Linux.

Azure Data Studio offers a modern editor experience with IntelliSense, code snippets, source control integration, and an integrated terminal.

Reference:

https://docs.microsoft.com/en-us/sql/azure-data-studio/download-azure-data-studio

First create an Azure storage account, then use Table service in the Azure portal to create a table.

Note: An Azure storage account contains all of your Azure Storage data objects: blobs, files, queues, and tables.

Reference:

https://docs.microsoft.com/en-us/azure/storage/tables/table-storage-quickstart-portal

https://docs.microsoft.com/en-us/azure/storage/common/storage-account-create

Azure Cosmos DB comes with native Core (SQL) API support.

In Azure Cosmos DB, data is indexed following indexing policies that are defined for each container. The default indexing policy for newly created containers enforces range indexes for any string or number. This policy can be overridden with your own custom indexing policy.

Reference:

https://docs.microsoft.com/en-us/azure/cosmos-db/sql/how-to-manage-indexing-policy

Data as it appears in the real world is naturally connected. Traditional data modeling focuses on defining entities separately and computing their relationships at runtime. While this model has its advantages, highly connected data can be challenging to manage under its constraints.

A graph database approach relies on persisting relationships in the storage layer instead, which leads to highly efficient graph retrieval operations. Azure Cosmos DB's Gremlin API supports the property graph model.

Reference:

https://docs.microsoft.com/en-us/azure/cosmos-db/graph-introduction#introduction-to-graph-databases

B: Transactional data tends to be heavy writes, moderate reads.

D: Typical traits of transactional data include: schema on write, strongly enforced. The schema is defined in a database.

F: Transactional data tends to be highly normalized.

Reference:

https://docs.microsoft.com/en-us/azure/architecture/data-guide/relational-data/online-transaction-processing

Consider using a data warehouse when you need to keep historical data separate from the source transaction systems for performance reasons. Data warehouses make it easy to access historical data from multiple locations, by providing a centralized location using common formats, keys, and data models.

Query both relational and nonrelational data.

Incorrect Answers:

D: Data warehouses don't need to follow the same terse data structure you may be using in your OLTP databases.

Reference:

https://docs.microsoft.com/en-us/azure/architecture/data-guide/relational-data/data-warehousing

Reference:

https://docs.microsoft.com/en-us/cli/azure/storage/container?view=azure-cli-latest