DP-900 Describe core data concepts Practice Question
A data engineer loads raw log files into a storage system. The structure of the data is interpreted at the time of reading, allowing queries to apply schema on the fly without preprocessing. This approach is best described as:
⚠ Common exam trap
Many candidates confuse schema-on-read with data virtualization, as both involve querying data without moving it, but schema-on-read specifically refers to interpreting the structure at read time from raw files, not abstracting multiple sources.
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
✓
Schema-on-read
Schema-on-read means the data is stored in its raw, unstructured form, and the schema is applied dynamically when the data is queried. This is exactly what happens when raw log files are loaded into a storage system like Azure Data Lake Storage and queried with tools like Azure Synapse Serverless SQL or Apache Spark, which infer the schema at query time without requiring preprocessing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Schema-on-write
Why it's wrong here
Schema-on-write enforces and validates a fixed data structure at the exact moment data is ingested, rejecting or transforming records that do not conform to the predefined columns, types, and constraints. Because the scenario says raw log files are stored first and the schema is applied later, schema-on-write is the opposite behavior and would require up-front transformation, defeating the purpose of retaining raw, flexible data.
- ✓
Schema-on-read
Why this is correct
Schema-on-read applies a logical structure to data only when it is accessed by a query engine, such as Azure Synapse Serverless SQL, Spark, or a metastore catalog that overlays schema metadata on raw files. Raw log files can be stored as-is in a data lake in open formats like JSON, CSV, or Parquet, and the schema is interpreted or inferred at read time. This is the correct answer because it matches the data engineer's workflow of loading raw files into storage without imposing structure until analysis.
- ✗
Data warehouse
Why it's wrong here
A data warehouse is an optimized, curated repository of cleansed, integrated, and highly structured data, typically with a fixed schema enforced during ETL/ELT processes before the data is stored. Loading raw, unprocessed log files directly into a data warehouse would require schema-on-write validation, transformation, and normalisation, which contradicts the premise of loading raw files with schema applied later. The raw files described in the question belong in a data lake or similar storage, not a warehouse built for governed, pre-structured data.
- ✗
Data virtualization
Why it's wrong here
Data virtualization is an architecture that provides a unified, abstracted view of data from multiple source systems through middle-tier views or connectors, without physically moving or copying the data into a new storage system. While it can apply schema mapping at query time, it does not describe the act of loading raw log files into a storage system for deferred schema definition; rather, it federates queries across existing data sources. The question specifically focuses on storage of raw files and read-time schema application, not on creating a virtual integration layer.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Schema
A schema is a blueprint or logical structure that defines how data is organized, stored, and accessed in a database or information system.
Key term
Data lake
A data lake is a centralized storage repository that holds vast amounts of raw data in its native format until it is needed for analysis.
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Written by Johnson Ajibi, MSc IT Security
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This DP-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-900 exam.