The exhibit displays a central Sales table containing foreign keys that reference surrounding tables. This hub-and-spoke configuration, where a central fact table is directly connected to several dimension tables without further normalization (i.e., the dimensions do not link to other sub-dimensions), is the definition of a star schema. The Customer table is a dimension table because it contains descriptive attributes about the business entity and provides the context for the quantitative measures stored in the Sales fact table. It contains a primary key (CustomerID) but no foreign keys, indicating it is at the end of a relationship branch.

Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd ed.). Wiley. Chapter 1: "Data Warehousing, Business Intelligence, and Dimensional Modeling Primer," pages 10–15.

Microsoft Learn Documentation. "Understand star schema and the importance for Power BI." Section: Star schema overview. [Official Vendor Documentation]

Oracle Help Center. "Data Warehousing Concepts." Section 19: "Dimensions and Keys in a Star Schema." [Official Vendor Documentation]

IEEE Xplore. "Comparison between Star and Snowflake Schemas in Data Warehousing." https://doi.org/10.1109/ICATC.2016.7725916

Azure Cosmos DB is a multi-model database service that supports various data models through different APIs. For graph databases, which represent data as a network of vertices (nodes) and edges (relationships), the correct API is Azure Cosmos DB for Apache Gremlin. Gremlin is a powerful graph traversal language from Apache TinkerPop that allows you to query and manipulate graph data. This API is specifically designed and optimized for storing and querying large-scale graphs with low latency.

B. Azure Cosmos DB for NoSQL: This is the native API for Azure Cosmos DB and is designed for document databases, storing data in JSON format.

C. Azure Cosmos DB for Apache Cassandra: This API is used for wide-column store databases and is compatible with the Apache Cassandra wire protocol.

D. Azure Cosmos DB for Table: This API is designed for key-value data models and provides a premium offering for applications written for Azure Table Storage.

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1. Microsoft Learn. "Choose an API in Azure Cosmos DB." Azure Cosmos DB Documentation. Under the section "Azure Cosmos DB for Apache Gremlin," it states, "A graph database service that uses the Gremlin query language."

Reference: learn.microsoft.com/en-us/azure/cosmos-db/choose-api

2. Microsoft Learn. "Introduction to Azure Cosmos DB for Apache Gremlin." Azure Cosmos DB Documentation. The first paragraph states, "Azure Cosmos DB for Apache Gremlin is a fully managed graph database service that implements the popular Apache TinkerPop property graph model."

Reference: learn.microsoft.com/en-us/azure/cosmos-db/gremlin/introduction

3. Microsoft Learn. "Work with data using Azure Cosmos DB service APIs." DP-900: Microsoft Azure Data Fundamentals Courseware. In the module "Explore non-relational data in Azure," the unit "Explore Azure Cosmos DB" explicitly maps the graph data model to the Gremlin API.

Reference: learn.microsoft.com/en-us/training/modules/explore-non-relational-data/4-explore-cosmos-db

Azure Cosmos DB is a multi-model database service that uses different APIs to project data into specific structures:

• Gremlin API: Used for graph-based data, utilizing vertices (entities) and edges (relationships).
• MongoDB API: Implements the MongoDB wire protocol to store data in BSON (Binary JSON) format, which is functionally equivalent to JSON documents. (Note: The NoSQL/Core API also uses JSON, but MongoDB is the specific match among the provided options).
• Table API: Designed for premium Key/Value storage, providing a schema-less structure similar to Azure Table Storage but with higher performance and global distribution.
• Cassandra API: Not used in this specific match, as it is primarily a wide-column store.

Microsoft Learn: Choose an API in Azure Cosmos DB. Section: "Gremlin API (graph)". Document ID: cosmos-db/choose-api.

Microsoft Learn: Azure Cosmos DB for MongoDB. Section: "What is the API for MongoDB?". Document ID: cosmos-db/mongodb/introduction.

Microsoft Learn: Introduction to Azure Cosmos DB: Table API. Section: "Key/value storage". Document ID: cosmos-db/table/introduction.

Microsoft Azure Documentation: Data modeling in Azure Cosmos DB. Section: "JSON as the data format".

Azure Data Lake Storage Gen2 implements a hierarchical namespace on top of Blob storage. At the highest level is the Azure Storage account, which serves as the top-level container for all data. Within the account, you create a File system (also referred to as a "container" in standard Blob storage). This file system acts as the root directory. Unlike standard flat namespaces, ADLS Gen2 allows for actual directories and subdirectories within the file system to organize data, which is essential for big data analytics performance and security (ACLs). "File share" is typically associated with Azure Files, but in some legacy or hybrid contexts, it represents a specific mountable entry point.

Microsoft Documentation: Introduction to Azure Data Lake Storage Gen2. Section: "Hierarchical Namespace". Official Microsoft Docs

Microsoft Documentation: Structure of Azure Data Lake Storage Gen2. Section: "Account, File systems, and Paths". Official Microsoft Docs

Azure Architecture Center: Data Lake Storage Gen2 Hierarchy and ACLs. Official Microsoft Architecture Guide

In a relational database, a primary key is a column (or a set of columns) that uniquely identifies every record in a table. It enforces entity integrity by requiring that all values in the primary key column are unique and do not contain NULL values. While other elements like unique indexes can also enforce uniqueness, the primary key serves as the official, fundamental identifier used for indexing and establishing relationships (via foreign keys) with other tables in the database schema.

Codd, E. F. (1970). A Relational Model of Data for Large Shared Data Banks. Communications of the ACM, 13(6), 377–387. https://doi.org/10.1145/362384.362685 (Section 1.3: Data Inventory and Redundancy).

Microsoft Official Documentation. Primary and Foreign Key Constraints. Microsoft Learn. https://learn.microsoft.com/en-us/sql/relational-databases/tables/primary-and-foreign-key-constraints

Oracle Database Documentation. Data Integrity: Primary Key Constraints. Oracle Help Center. https://docs.oracle.com/en/database/oracle/oracle-database/21/cncpt/data-integrity.html

IBM Documentation. Primary Keys. IBM Documentation for Db2. https://www.ibm.com/docs/en/db2/11.5?topic=constraints-primary-keys

Garcia-Molina, H., Ullman, J. D., & Widom, J. (2008). Database Systems: The Complete Book (2nd Edition). Pearson/Prentice Hall. (Section 2.3.4: Keys of Relations).

A Database Administrator (DBA) is responsible for the operational aspects of data management. This includes the implementation, maintenance, and security of database systems. A core requirement of the DBA role is ensuring data integrity and availability through the management of storage, performance tuning, and the execution of backup and recovery strategies. While Data Engineers design and build data pipelines, and Data Analysts/Scientists consume data for insights, the DBA is specifically tasked with the "keeping the lights on" activities that protect data against loss or corruption via restoration procedures.

Microsoft Learn: DP-900: Microsoft Azure Data Fundamentals — "Explore roles and responsibilities in the world of data," Section: "Database Administrator."

Oracle Help Center: Database Administrator's Guide, 19c, Chapter 1: "The Roles of a Database Administrator," Section: "Backing Up and Recovering the Database."

IBM Documentation: DB2 Fundamentals, Section: "The role of the database administrator (DBA)," Subsection: "Data recovery and maintenance."

ACM/IEEE-CS: Information Technology Curricula 2017 (IT2017), Page 61, Table 5.3: "Data Management (DM) - Competencies," Item DM-05 (Backup and Recovery).

Stanford University (CS145): Introduction to Databases, Course Materials on "Database System Architecture," Section: "Recovery and Durability."

Azure Data Factory (ADF): ADF is an ETL service that uses "Copy" activities and "Mapping Data Flows" to transform data. It supports Parquet as a sink (destination) format, allowing it to output processed data into Parquet files.

Azure Data Lake Storage (ADLS): This is a highly scalable, secure data lake for high-performance analytics. It serves as the physical storage layer for various file formats, including Parquet.

Azure Synapse Analytics: Through its "Serverless SQL pools" or "Dedicated SQL pools," Synapse can create External Tables. These persist a tabular schema/representation over raw Parquet files stored in a data lake, allowing users to query them using standard T-SQL.

Microsoft Learn: Connector overview - Parquet format in Azure Data Factory and Azure Synapse Analytics. (Specifies ADF can use Parquet as a source and sink for data movement).

Microsoft Learn: Introduction to Azure Data Lake Storage Gen2. (Defines ADLS as the foundation for building enterprise data lakes on Azure for storing large datasets).

Microsoft Learn: Use external tables with Synapse SQL. (Explains how Synapse persists metadata and tabular structures over Parquet files in ADLS).

Microsoft Learn: Data integration in Azure Synapse Analytics vs Azure Data Factory. (Distinguishes the roles of orchestration/output versus storage and relational representation).

Azure SQL Edge is a small-footprint, containerized relational database engine specifically designed for IoT (Internet of Things) and edge computing deployments. Unlike standard cloud-based offerings, it is optimized for low-latency processing and works in disconnected or intermittently connected environments. It supports both ARM and x64 architectures and includes built-in capabilities for data streaming and time-series analysis, which are essential for processing telemetry data from sensors. While Azure SQL Database and Managed Instance are robust cloud PaaS solutions, they are not designed to run locally on edge devices. SQL Server on VMs is an IaaS solution meant for full-scale server migrations, not lightweight IoT deployments.

Microsoft Learn Documentation: "What is Azure SQL Edge?" Section: Overview and Key Capabilities. Document ID: azure-sql-edge-overview.

Microsoft Learn Documentation: "Compare Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure VMs." Section: Service Comparison.

Microsoft Technical Documentation: "Data streaming in Azure SQL Edge." Section: Edge IoT Scenarios.

IEEE Xplore: "A Survey on Edge Computing: Architecture, Challenges and Opportunities." (For context on edge-optimized database requirements). https://doi.org/10.1109/COMST.2017.2717385

To use Azure Table storage, you must first create an Azure Storage Account. This account acts as a unique namespace in Azure for your data and is the top-level resource for all Azure Storage services, including Blobs, Queues, Files, and Tables. The az storage account create command is the correct Azure CLI command to provision this fundamental resource. Once the storage account is created, you can then proceed to create individual tables within it to store your data.

A. az storage share create is used to create a file share for the Azure Files service, not for Table storage.

C. az cosmosdb create provisions an Azure Cosmos DB account. While Cosmos DB offers a Table API, the question refers to the standard Azure Table storage service.

D. az storage container create is used to create a container for the Azure Blob storage service, which stores unstructured data like images or documents.

1. Microsoft Azure Documentation, Azure CLI, az storage account create: "Create a storage account." This is the primary command for creating the necessary parent resource.

Source: Microsoft Learn, az storage account, Section: az-storage-account-create.

2. Microsoft Azure Documentation, Introduction to Azure Table Storage: "Azure Table storage is a service that stores non-relational structured data (also known as structured NoSQL data) in the cloud... All access to Table storage is through a storage account."

Source: Microsoft Learn, Azure Storage documentation > Tables > Table storage overview.

3. Microsoft Azure Documentation, Azure CLI, az storage container create: "Creates a new container in the specified account." This confirms the command is for containers, which are part of Blob storage.

Source: Microsoft Learn, az storage container, Section: az-storage-container-create.

4. Microsoft Azure Documentation, Azure CLI, az storage share create: "Creates a new Azure file share under the specified account." This confirms the command is for the Azure Files service.

Source: Microsoft Learn, az storage share, Section: az-storage-share-create.

Diagnostic Analytics focuses on the "why" by looking at historical data to identify patterns and root causes of specific outcomes (e.g., sales spikes).

Prescriptive Analytics goes beyond predicting outcomes to recommending specific actions or "guidance" to achieve a goal, such as budget optimization.

Cognitive Analytics (or AI) mimics human brain functions like vision and pattern recognition. Detecting specific logos within unstructured data like PDFs falls under Computer Vision, a branch of cognitive services.

Gartner. Glossary: Diagnostic Analytics. Describes the process of examining data to answer the question, "Why did it happen?"

Microsoft Learn. Explore fundamentals of data analytics (Azure Data Fundamentals). Defines Prescriptive analytics as the phase that suggests a course of action.

Microsoft Learn. Introduction to AI (AI-900). Defines Cognitive Services and Computer Vision as the ability of AI to process and categorize visual information in documents.

IBM Cloud Education. Descriptive, Predictive, Prescriptive, and Diagnostic Analytics. Provides the standard hierarchy of analytics types and their respective business applications.