The correct answer is schema-on-read. This approach is best described as schema-on-read because the data is stored in its raw, unstructured form—such as raw log files in Azure Data Lake Storage—and the schema is applied dynamically at query time by tools like Azure Synapse Serverless SQL or Apache Spark, which interpret the structure on the fly without any preprocessing. On the Microsoft Azure Data Fundamentals DP-900 exam, this concept tests your understanding of how Azure handles flexible data ingestion versus schema-on-write, where structure is enforced before storage. A common trap is confusing schema-on-read with schema-on-write, but remember: schema-on-read means you store first and ask questions later, while schema-on-write requires you to define the structure upfront. A helpful memory tip is to think of “read” as “relaxed”—you can read raw data and impose order only when you need it.
DP-900 Describe core data concepts Practice Question
This DP-900 practice question tests your understanding of describe core data concepts. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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:
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 structure at ingestion time, opposite of the scenario where schema is applied later.
✓
Schema-on-read
Why this is correct
Schema-on-read applies the data structure when the data is accessed, typical in data lake architectures.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Data warehouse
Why it's wrong here
A data warehouse uses schema-on-write with curated, structured data, not raw files.
✗
Data virtualization
Why it's wrong here
Data virtualization provides a unified view without moving data, but does not specifically describe applying schema at read time.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing 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.
Trap categories for this question
Scenario analysis trap
Schema-on-write enforces structure at ingestion time, opposite of the scenario where schema is applied later.
Detailed technical explanation
How to think about this question
Under the hood, schema-on-read leverages file formats like Parquet, Avro, or JSON, where metadata (e.g., column names, data types) is embedded in the file itself or inferred from the data. In Azure, PolyBase or Synapse Serverless SQL uses external tables with LOCATION pointing to raw files, and the schema is resolved at query execution via the OPENROWSET function. A real-world scenario is a data lake ingesting IoT sensor logs in JSON format, where new fields appear unpredictably—schema-on-read allows queries to adapt without reprocessing historical data.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-900 question in full detail.
Describe core data concepts — This question tests Describe core data concepts — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this DP-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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