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DP-900 Describe core data concepts Practice Question

A company receives data from a point-of-sale system. Each row contains TransactionID, ProductID, Quantity, and Price. The data has a fixed schema and is stored in a table. How should this data be classified?

⚠ Common exam trap

Many exam-takers confuse 'transactional data' (a workload pattern) with 'structured data' (a data classification), leading them to pick Option D because the data comes from a point-of-sale system, but the question explicitly asks about data structure, not data source or usage.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Structured data

The data has a fixed schema with clearly defined columns (TransactionID, ProductID, Quantity, Price) and each row follows the same structure, which is the definition of structured data. In Azure, this would map directly to a table in Azure SQL Database or a fixed-schema table in Azure Synapse Analytics. The rigid schema and consistent data types make it ideal for relational storage and querying.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Structured data

    Why this is correct

    Structured data has a fixed, predefined schema: every row in this POS dataset contains the same columns (TransactionID, ProductID, Quantity, Price) with consistent data types, allowing direct querying with SQL. This tabular format—organized into rows and columns with strict formatting—is the defining characteristic of structured data. Because the schema is known ahead of time and every record conforms to it, this dataset clearly fits the structured data classification.

  • Semi-structured data

    Why it's wrong here

    Semi-structured data does not have a rigid, fixed schema, even though it contains organizational markers such as tags, keys, or hierarchical nesting—for example, JSON, XML, or Parquet with a flexible schema. The POS data described here has a consistent set of columns and data types in every row, with no nested or variant attributes. Because the schema is fixed and uniform across all records, the dataset does not qualify as semi-structured, which tolerates missing fields or differing structures within the same collection.

  • Unstructured data

    Why it's wrong here

    Unstructured data has no predefined schema or formal structure and cannot be queried using conventional relational methods; typical examples include images, video, audio, and free-form text documents. The POS rows, by contrast, contain columns with specific data types (such as numeric quantities and prices) that can be directly placed into a relational table. Since the dataset is organized into records with identifiable fields, it is inherently structured rather than unstructured, which would lack any fixed format or ability to be directly loaded into tabular storage.

  • Transactional data

    Why it's wrong here

    Transactional data is a classification based on the data's purpose—recording business events such as sales or purchases—not on its structural format. A transactional record can be stored as structured data, as JSON/XML (semi-structured), or even as an image (unstructured) depending on how it is captured. The question is asking how the data is classified based on its structure, not its business function, so 'transactional data' is an incorrect answer despite the POS context.

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