Quick Answer
The answer is OPENJSON, the correct function to flatten nested Parquet data in Azure Synapse serverless SQL pool. This is because when Parquet files contain complex nested columns like structs and arrays, the serverless SQL pool exposes them as JSON strings, and OPENJSON is designed to parse that JSON text and return the nested objects and properties as relational rows and columns. On the Microsoft Azure Data Engineer Associate DP-203 exam, this question tests your understanding of how serverless SQL pool handles semi-structured data from ADLS Gen2, and a common trap is confusing OPENJSON with JSON_VALUE or JSON_QUERY, which extract scalar values or objects but do not flatten arrays into multiple rows. Remember that OPENJSON is the only function that can turn a nested array into a result set with multiple rows, making it essential for flattening. A helpful memory tip: think of OPENJSON as opening up the nested structure to get a flat, relational view.
This DP-203 practice question tests your understanding of develop data processing. 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.
You are working with Azure Synapse Analytics serverless SQL pool. You need to query a set of Parquet files located in ADLS Gen2. The files have nested columns (structs and arrays). Which function should you use to flatten the nested data?
OPENJSON
OPENJSON can parse nested JSON structures and flatten them into rows.
PIVOT
Why wrong: PIVOT is for converting rows to columns, not flattening nested data.
UNNEST
Why wrong: UNNEST is not a T-SQL function; it exists in other SQL dialects like PostgreSQL.
CROSS APPLY
Why wrong: CROSS APPLY is used with table-valued functions, but not specifically for flattening nested columns in Parquet.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
OPENJSON
OPENJSON is the correct function because it parses JSON text and returns objects and properties from JSON input as rows and columns. In Azure Synapse serverless SQL pool, when Parquet files contain nested columns (structs and arrays), they are exposed as JSON strings, and OPENJSON can flatten these nested structures into a relational format. This allows you to query complex nested data directly without needing to pre-process the files.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
OPENJSON
Why this is correct
OPENJSON can parse nested JSON structures and flatten them into rows.
Related concept
Read the scenario before looking for a memorised answer.
PIVOT
Why it's wrong here
PIVOT is for converting rows to columns, not flattening nested data.
UNNEST
Why it's wrong here
UNNEST is not a T-SQL function; it exists in other SQL dialects like PostgreSQL.
CROSS APPLY
Why it's wrong here
CROSS APPLY is used with table-valued functions, but not specifically for flattening nested columns in Parquet.
Common exam traps
The trap here is that candidates confuse CROSS APPLY with a flattening function, but CROSS APPLY only invokes a table-valued function (like OPENJSON) and does not flatten data by itself.
Detailed technical explanation
Under the hood, Azure Synapse serverless SQL pool reads Parquet files and presents nested columns as JSON strings. OPENJSON uses a schema (WITH clause) to define the output structure, enabling you to extract specific fields from arrays and structs. A real-world scenario is querying IoT sensor data stored as Parquet with nested readings; OPENJSON can flatten each reading into separate rows for time-series analysis without requiring a separate ETL step.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
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FAQ
Develop data processing — This question tests Develop data processing — Read the scenario before looking for a memorised answer..
The correct answer is: OPENJSON — OPENJSON is the correct function because it parses JSON text and returns objects and properties from JSON input as rows and columns. In Azure Synapse serverless SQL pool, when Parquet files contain nested columns (structs and arrays), they are exposed as JSON strings, and OPENJSON can flatten these nested structures into a relational format. This allows you to query complex nested data directly without needing to pre-process the files.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This DP-203 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-203 exam.
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