Describe core data concepts

A retail company stores customer order records where every record has the same predefined columns, such as OrderID, CustomerName, Quantity, and Price, all conforming to a fixed schema enforced by the storage system. How should this data be classified?

A retail analyst needs to export daily sales figures from a spreadsheet so they can be shared with a partner company and easily imported into many different tools. The data is purely tabular, with rows and columns, and the partner has requested a lightweight, plain-text format with no proprietary software required to open it. Which data file format best meets these requirements?

A retail company wants to consolidate several years of sales history from multiple operational systems into a single repository. Business analysts will run large aggregation queries across billions of rows to identify seasonal trends and regional performance. Which type of Azure data store is most appropriate for this workload?

A media company needs to store millions of large raw video and audio files that are uploaded by content creators. The files are unstructured, vary greatly in size, and must be stored cost-effectively while remaining accessible over HTTP for later processing. Which Azure data store is the most appropriate choice?

A retail company stores product listings, each containing a variable set of attributes such as color, size, weight, and warranty details. Some products have many optional fields while others have only a few. The development team wants to store each listing as a self-describing document that can be retrieved and updated by its unique product ID without requiring schema migrations when new attributes are added. Which type of data store best fits this requirement?

A company runs several Azure virtual machines that all need to read and write to the same set of files using standard SMB protocol, so applications can access a shared network drive without code changes. Which Azure storage option best meets this requirement?

An online retailer is building a product catalog where different product categories have widely varying attributes (a laptop has CPU and RAM fields, while a shirt has size and color fields). The development team wants to store each product as a self-describing document without enforcing a rigid table schema, and needs fast retrieval by product ID. Which Azure data store is the best fit?

A data engineering team ingests large volumes of raw log files and CSV exports each night. They want a single storage service that organizes data into a hierarchical namespace of directories, integrates natively with big data analytics engines like Azure Databricks and Synapse, and is optimized for high-throughput batch processing of these files. Which Azure storage option should they choose?

A manufacturing company collects continuous temperature and vibration readings from thousands of factory sensors. Each reading arrives as a small record with a timestamp and needs to be stored so that engineers can later query trends over time. The team wants a fully managed Azure service purpose-built for high-throughput ingestion and analysis of this time-stamped telemetry. Which Azure service should they choose?

A retail company runs an e-commerce website that repeatedly reads the same product catalog and session data. The development team wants a data store optimized for extremely fast lookups using a simple identifier for each item, without the need for complex queries or relationships between tables. Which type of data store best fits this requirement?

A retail company is building an order-processing system. When a customer places an order, a message must be sent so that separate services (inventory, billing, and shipping) can each pick up and process the work independently and reliably, even if one service is temporarily offline. Which type of Azure data store is the most appropriate choice for holding these messages?

A retail company is building an online order-processing system that requires strong consistency, enforced relationships between customers and orders, and support for frequent INSERT and UPDATE operations. The team wants a fully managed relational service on Azure that minimizes administrative overhead. Which Azure data store should they choose?

A retail company collects application log entries that arrive continuously as individual records. Each record has the same fields (timestamp, level, message, source). The team wants to store these logs in a human-readable, line-oriented text format where each log entry is a self-contained object that can be appended one line at a time. Which data file format best fits this requirement?

A development team needs to store application configuration that includes nested settings, arrays of feature flags, and key-value pairs. The configuration must be human-readable, easily editable by developers, and consumed by a web API. Which data file format best fits these requirements?

A data engineering team is designing a data lake to store large volumes of historical sales data that will be queried mainly for analytical aggregations, such as summing revenue across millions of rows for specific columns. They want a file format that supports efficient columnar compression and reduces the amount of data scanned during column-based queries. Which file format best meets these requirements?

A retail company's order-processing database has been running slowly during peak hours. Management wants someone to review query performance, adjust indexes, configure backups, and manage user access permissions to keep the database healthy and available. Which role is best suited to these tasks?

A logistics company scans paper invoices and stores the resulting JPEG image files. These files have no fixed schema and cannot be organized into rows and columns. How should this data be classified?

A marketing team collects social media posts that include free-form text captions, embedded images, and short video clips. The team wants to store these files in their raw, native form for later processing. How should this data be classified?

A retail company is building a new analytics platform. Raw sales data arrives from multiple point-of-sale systems in different formats, and the business needs it cleaned, transformed, and loaded into a central data store on a scheduled basis so it can be analyzed. Which role is primarily responsible for designing and building these data ingestion and transformation pipelines?

An online retail company runs a system that processes thousands of customer purchases per minute, where each order must insert, update, and confirm records quickly and reliably. Separately, the business intelligence team needs to run large queries that aggregate several years of sales history to identify seasonal trends. How should each workload be classified?