Question 266 of 846

Develop data processing →mediumMultiple ChoiceObjective-mapped

Quick Answer

The correct answer is to use Azure Data Factory with Mapping Data Flows to clean, validate, and aggregate the data, then load into Synapse dedicated SQL pool. This approach is optimal because Mapping Data Flows provide a code-free, visual ETL environment that runs on serverless Spark clusters, allowing you to read CSV files from SFTP, apply transformations like filtering invalid rows and aggregating sales per store, and write directly to Synapse using the PolyBase sink—all without managing any infrastructure. On the DP-203 exam, this scenario tests your understanding of minimizing administrative overhead while enabling monitoring; a common trap is choosing a custom solution with Azure Databricks or SSIS, which adds management complexity. Remember that ADF’s native integration with Azure Monitor and its pipeline run views give you easy monitoring out of the box. Memory tip: “SFTP to Synapse? Let ADF Mapping Flows do the heavy lifting—no cluster to tune, just drag and drop.”

DP-203 Develop data processing Practice Question

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 designing a data transformation solution for a retail company. The company receives daily CSV files from 200 stores via SFTP. The files must be cleaned, validated, and aggregated before loading into Azure Synapse dedicated SQL pool. The solution must minimize administrative overhead and support easy monitoring. Which approach do you recommend?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

Question 1mediummultiple choice

Full question →

A
Use Azure Functions to process each file and write to Synapse via REST API
Why wrong: Azure Functions require custom code and lack the visual transformation capabilities and monitoring of ADF.
B
Use PolyBase external tables to load raw data and then use T-SQL stored procedures for transformation
Why wrong: This approach works but involves more manual steps and does not provide the visual interface and easy monitoring of ADF.
C
Use Azure Databricks with Python notebooks to process the files and write to Synapse
Why wrong: Databricks requires cluster management and is more complex than needed for simple transformations.
D
Use Azure Data Factory with Mapping Data Flows to clean, validate, and aggregate the data, then load into Synapse SQL pool
Mapping Data Flows provide a visual interface for transformations, are serverless, and have rich monitoring via ADF.

Full breakdown with real-world context →

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 Azure Data Factory with Mapping Data Flows to clean, validate, and aggregate the data, then load into Synapse SQL pool

Option D is correct because Azure Data Factory (ADF) with Mapping Data Flows provides a fully managed, code-free ETL service that can read CSV files from SFTP, perform cleaning, validation, and aggregation at scale using Spark clusters, and load the results directly into Azure Synapse dedicated SQL pool via the PolyBase sink. This minimizes administrative overhead by eliminating infrastructure management and supports easy monitoring through ADF’s built-in integration with Azure Monitor and pipeline run views.

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.

✗
Use Azure Functions to process each file and write to Synapse via REST API
Why it's wrong here
Azure Functions require custom code and lack the visual transformation capabilities and monitoring of ADF.
✗
Use PolyBase external tables to load raw data and then use T-SQL stored procedures for transformation
Why it's wrong here
This approach works but involves more manual steps and does not provide the visual interface and easy monitoring of ADF.
✗
Use Azure Databricks with Python notebooks to process the files and write to Synapse
Why it's wrong here
Databricks requires cluster management and is more complex than needed for simple transformations.
✓
Use Azure Data Factory with Mapping Data Flows to clean, validate, and aggregate the data, then load into Synapse SQL pool
Why this is correct
Mapping Data Flows provide a visual interface for transformations, are serverless, and have rich monitoring via ADF.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often overestimate the simplicity of Azure Functions for batch ETL or assume PolyBase alone handles transformations, when in fact ADF Mapping Data Flows are purpose-built for visual, scalable, and monitorable ETL with minimal overhead.

Detailed technical explanation

How to think about this question

Under the hood, ADF Mapping Data Flows execute on Azure Integration Runtime using managed Spark clusters that automatically scale based on workload, and the PolyBase sink uses the COPY INTO command to efficiently bulk load data into Synapse with minimal latency. A real-world scenario where this matters is when handling 200 daily files—ADF can trigger pipelines on file arrival via event-based triggers, process files in parallel, and use data flow debug mode for iterative development without provisioning clusters manually.

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

An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this DP-203 question test?

Develop data processing — This question tests Develop data processing — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Use Azure Data Factory with Mapping Data Flows to clean, validate, and aggregate the data, then load into Synapse SQL pool — Option D is correct because Azure Data Factory (ADF) with Mapping Data Flows provides a fully managed, code-free ETL service that can read CSV files from SFTP, perform cleaning, validation, and aggregation at scale using Spark clusters, and load the results directly into Azure Synapse dedicated SQL pool via the PolyBase sink. This minimizes administrative overhead by eliminating infrastructure management and supports easy monitoring through ADF’s built-in integration with Azure Monitor and pipeline run views.

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.

Are there clue words in this question I should notice?

Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

About these practice questions

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Same concept, more angles

1 more ways this is tested on DP-203

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. You are designing a data processing solution for a marketing company that uses Azure Synapse Analytics. The solution needs to process customer data from multiple sources, including CRM and web analytics. The data must be cleansed and transformed before loading into a dedicated SQL pool. The transformations include string manipulations, date conversions, and lookups. You need to choose a serverless transformation approach that integrates with Azure Synapse pipelines. Which approach should you use?

easy

A.Use Azure Stream Analytics to transform the data in real time.
B.Use PolyBase to load data and then use T-SQL stored procedures to transform.
C.Use Azure Databricks notebooks with Spark to perform transformations.
✓ D.Use mapping data flows in Azure Synapse pipelines.

Why D: Option C is correct because Azure Synapse pipelines support mapping data flows, which are serverless and provide a visual interface for transformations. Option A is wrong because PolyBase is for loading, not transformation. Option B is wrong because Azure Databricks would require a cluster. Option D is wrong because Azure Stream Analytics is for streaming, not batch transformations.

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