Azure Synapse Analytics for Row-Level Security and Complex Queries
A financial services company has raw transaction data stored in Azure Data Lake Storage Gen2 (ADLS Gen2) as Parquet files, partitioned by date. The analytics team needs to run complex SQL queries that join multiple datasets, including reference data from an Azure SQL Database, to generate risk reports. They require enterprise-grade security features such as row-level security (RLS) and column-level security. They also want to use the same service for data transformation and loading (ETL) into a curated layer. Which Azure service should they choose?
Quick Answer
Azure Synapse Analytics is the correct choice because it unifies enterprise data warehousing and big data analytics into a single service, directly supporting complex SQL queries that join Parquet files in ADLS Gen2 with reference data from Azure SQL Database. Its built-in row-level security and column-level security enforce granular access control at the row and column level, meeting the enterprise-grade security requirement, while Synapse Pipelines handle ETL transformations to build a curated layer without needing separate tools. On the DP-900 exam, this scenario tests your understanding of Synapse Analytics as the go-to service for combining serverless SQL queries on data lakes with dedicated SQL pools and integrated security—a common trap is choosing Azure Data Factory alone, which lacks native SQL querying and RLS. Remember the memory tip: “Synapse stitches security, SQL, and ETL in one synapse.”
⚠ Common exam trap
Many exam-takers confuse Azure Data Factory as a complete analytics solution because of its ETL capabilities, overlooking that it lacks a native SQL query engine and built-in row/column-level security for direct data access.
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
✓
Azure Synapse Analytics
Azure Synapse Analytics is the correct choice because it provides a unified analytics platform that combines enterprise data warehousing with big data analytics. It directly supports complex SQL queries across multiple datasets (including Parquet files in ADLS Gen2 and Azure SQL Database), offers built-in row-level security (RLS) and column-level security for enterprise-grade access control, and includes a built-in pipeline orchestration engine (via Synapse Pipelines) for ETL/ELT transformations into a curated layer. This single service eliminates the need to stitch together separate tools for querying, security, and data transformation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Azure Synapse Analytics
Why this is correct
Correct. Azure Synapse Analytics offers a unified experience for data integration, enterprise data warehousing, and big data analytics, with built-in security features like RLS and column-level security. It can query ADLS Gen2 using serverless SQL pool and orchestrate ETL with pipelines.
- ✗
Azure Data Factory
Why it's wrong here
Azure Data Factory is primarily a data integration and orchestration service. While it can move and transform data, it does not natively support running complex SQL queries with row-level security.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks is an Apache Spark-based analytics platform. It excels at data engineering and machine learning but does not provide built-in row-level or column-level security for SQL queries. Security is typically managed at the file or workspace level.
- ✗
Azure Analysis Services
Why it's wrong here
Azure Analysis Services is an analytical engine used to create semantic models. It does not directly query ADLS Gen2; data must be loaded from a source. It also does not include ETL capabilities.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Azure SQL Database
Azure SQL Database is a fully managed relational database-as-a-service (DBaaS) in Microsoft Azure, based on the SQL Server engine, that handles scaling, backups, patching, and high availability automatically.
Key term
Big data
Big data refers to extremely large and complex datasets that traditional data processing tools cannot handle efficiently, requiring specialized technologies to store, process, and analyze them.
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Same concept, more angles
1 more way this is tested on DP-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company uses Azure Data Lake Storage Gen2 as a data lake. They need to enforce row-level security for sensitive data so that sales representatives can only see rows for their assigned region. Which approach should they use?
hard- A.Apply sensitivity labels in Microsoft Purview
- ✓ B.Load data into Azure Synapse Analytics dedicated SQL pool and implement row-level security (RLS)
- C.Use Azure RBAC roles on the storage account
- D.Use Azure Data Lake Storage Gen2 access control lists (ACLs) on folders per region
Why B: Row-level security (RLS) in Azure Synapse Analytics dedicated SQL pool allows you to restrict data access at the row level based on a user's identity or group membership. By loading the data into a dedicated SQL pool and defining a security policy with a predicate function that filters rows by region, you can ensure sales representatives only see rows for their assigned region. This is the correct approach because RLS is designed specifically for this purpose and integrates with Azure Active Directory for user authentication.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.