DP-203 Develop data processing Practice Question
Which TWO are valid ways to process data in Azure Synapse Analytics?
⚠ Common exam trap
It's easy for candidates to confuse general Azure services (Logic Apps, Functions, Power BI) with native Synapse Analytics processing capabilities, forgetting that only Synapse SQL and Synapse Spark are first-class compute engines within the service.
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
✓
Use Synapse SQL pool to run T-SQL queries.
Option C is correct because a Synapse SQL pool (dedicated or serverless) is a core Synapse Analytics engine that executes T-SQL queries directly against data in the workspace, making it a valid data-processing method. Option E is correct because Synapse Spark notebooks run Apache Spark code, including Scala, Python, SQL, and R, and are a first-class way to transform and process data in Azure Synapse Analytics. Option A is not appropriate because Logic Apps is a workflow/automation service for orchestrating triggers and connectors, not a data transformation engine. Option B is not the intended Synapse processing method because Azure Functions is a separate serverless compute service outside Synapse's built-in SQL and Spark engines. Option D is not correct because Power BI is a visualization and reporting tool; its data transformation (Power Query) is for modeling/reporting, not a Synapse data-processing workload.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Logic Apps to run data transformations.
Why it's wrong here
Logic Apps orchestrates workflows and connectors; it does not run data transformations inside Synapse. It is tempting because Logic Apps can trigger pipelines and move data between systems, and would be correct for integration or approval workflows, but Synapse processing uses Spark pools, SQL pools, or Data Flow.
- ✗
Use Azure Functions to process data in a serverless manner.
Why it's wrong here
Azure Functions runs outside Synapse's workspace compute; processing inside Synapse uses Spark pools, SQL pools, or Data Flow mappings. It is tempting because serverless functions genuinely process data, and would be correct for event-driven or lightweight API-triggered workloads, but the question asks for Synapse-native processing methods.
- ✓
Use Synapse SQL pool to run T-SQL queries.
Why this is correct
A dedicated Synapse SQL pool is a provisioned MPP engine that executes T-SQL, including distributed queries, stored procedures and CETAS, directly against data held in its own distributions. This satisfies the requirement for a T-SQL-based processing path within Synapse Analytics.
- ✗
Use Power BI to transform data.
Why it's wrong here
Power BI is a visualisation and reporting layer; it does not perform data processing within Synapse. It is tempting because Power BI can apply Power Query transformations, and would be correct for shaping data for reports, but Synapse processing is delivered through Spark pools, dedicated SQL pools, or Data Flow.
- ✓
Use Synapse Spark notebooks to run Scala code.
Why this is correct
Synapse Spark pools run Apache Spark, so notebooks can execute Scala, PySpark or Spark SQL against data in the linked Data Lake. This provides the code-first processing route that complements the T-SQL engine, satisfying the requirement for a second valid processing method.
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
Implement Azure Synapse Analytics
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
Key term
Data Transformation Pipelines
Data transformation pipelines are automated sequences of steps that take raw data from a source, clean and reshape it into a usable format, and then load it into a destination for analysis or storage.
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