The correct approach is to create views in Azure Synapse Serverless SQL with security predicates, as this is the only native Azure service that provides declarative row-level filtering directly on data stored in Azure Data Lake Storage Gen2. This works by using the SECURITY_POLICY and FILTER_PREDICATE functions to dynamically filter rows based on the user’s identity, such as department membership, without duplicating data or managing separate files. On the DP-203 exam, this scenario tests your understanding of how to enforce row-level security on ADLS Gen2 data without moving or transforming it, often appearing as a trap where candidates mistakenly choose Azure Purview or RBAC on storage containers. The key distinction is that Synapse Serverless SQL applies filtering at query time, not at the storage layer. Remember: for row-level security on data lake files, think “Synapse Serverless SQL views with predicates” — the mnemonic “RLS = View + Predicate” will help you avoid the common pitfall of selecting storage-level access controls.
DP-203 Design and implement data storage Practice Question
This DP-203 practice question tests your understanding of design and implement data storage. 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 company is designing a data lake on Azure Data Lake Storage Gen2. They need to enforce row-level security on the data for different departments. Which approach should they use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Create views in Azure Synapse Serverless SQL with security predicates
Azure Synapse Serverless SQL supports row-level security (RLS) through the creation of views that use the `SECURITY_POLICY` and `FILTER_PREDICATE` functions. This allows you to filter rows based on the user's identity (e.g., department membership) without duplicating data or managing separate files. It is the only native Azure service that provides declarative row-level filtering directly on data stored in Azure Data Lake Storage Gen2.
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.
✓
Create views in Azure Synapse Serverless SQL with security predicates
Why this is correct
Serverless SQL can query ADLS and apply RLS via views.
Related concept
Read the scenario before looking for a memorised answer.
✗
Assign Azure RBAC roles for each department on the storage account
Why it's wrong here
RBAC roles control access at the storage account/container level, not rows.
✗
Implement Azure Purview data policies for row-level security
Why it's wrong here
Purview does not enforce row-level security.
✗
Use Azure Data Lake Storage Gen2 access control lists (ACLs) at the file level
Why it's wrong here
ACLs work at file/folder level, not row level.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Azure RBAC or ACLs (which control access to storage objects) with row-level security (which controls access to rows within a data set), leading them to choose a storage-level permission model instead of a query-level filtering mechanism.
Detailed technical explanation
How to think about this question
Row-level security in Azure Synapse Serverless SQL is implemented by creating a security policy that uses an inline table-valued function as a filter predicate. This predicate is evaluated against the `USER_NAME()` or `SESSION_CONTEXT()` to dynamically restrict rows. Under the hood, the serverless SQL engine pushes the predicate down to the data source (e.g., Parquet files) to minimize data movement, making it efficient for large datasets.
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-203 question in full detail.
Design and implement data storage — This question tests Design and implement data storage — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create views in Azure Synapse Serverless SQL with security predicates — Azure Synapse Serverless SQL supports row-level security (RLS) through the creation of views that use the `SECURITY_POLICY` and `FILTER_PREDICATE` functions. This allows you to filter rows based on the user's identity (e.g., department membership) without duplicating data or managing separate files. It is the only native Azure service that provides declarative row-level filtering directly on data stored in Azure Data Lake Storage Gen2.
What should I do if I get this DP-203 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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