Quick Answer
The answer is Option C because Azure Data Factory mapping data flows provide a serverless, code-free batch processing transformation technology that scales automatically and charges only per execution, making it the most cost-effective and low-overhead choice for transforming 2 TB of daily CSV data before loading into Azure Synapse Analytics. This option eliminates the need to manage staging tables, Spark clusters, or Synapse Spark pools, directly addressing the exam’s focus on minimizing operational burden while meeting performance requirements. On the DP-203 exam, this scenario tests your understanding of when to choose serverless transformation services over cluster-based alternatives; a common trap is assuming that Spark-based options like Databricks or Synapse notebooks are always faster, but for batch workloads of this size with simple filter-aggregate-join logic, mapping data flows offer comparable performance without cluster management. Remember the mnemonic: “Serverless saves salary” — no cluster babysitting means lower cost and less overhead.
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.
A data engineering team is building a batch processing solution for a financial services company. Data is ingested daily from multiple sources into Azure Data Lake Storage Gen2 in CSV format. The data must be transformed (filtered, aggregated, joined) and loaded into Azure Synapse Analytics dedicated SQL pool. The team must optimize for cost and performance. The total data volume is 2 TB per day. The team has the following options:
Option A: Use Azure Data Factory pipelines with copy activity to load raw CSV files into Synapse staging tables, then use T-SQL stored procedures in Synapse to perform transformations.
Option B: Use Azure Databricks with Auto Loader to incrementally ingest CSV files, perform transformations in Spark, and write the results to Synapse using the Spark Synapse connector.
Option C: Use Azure Data Factory with mapping data flows to transform the data in a serverless environment and then write to Synapse.
Option D: Use Azure Synapse Pipelines (built on ADF) with a notebook activity that runs a PySpark notebook in Synapse Spark pool to transform and load data.
Which option should the team choose to minimize cost and management overhead while meeting performance requirements?
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.
Option B
Why wrong: Requires cluster management and higher cost.
Option C
Serverless, cost-effective, low overhead.
Option A
Why wrong: Requires manual staging and T-SQL management.
Option D
Why wrong: Requires Synapse Spark pool management.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Option C
Option C is correct because mapping data flows run on a serverless ADF integration runtime, which scales automatically and incurs cost only per execution, minimizing management overhead. Option A requires managing staging tables and stored procedures. Option B requires managing Spark clusters. Option D requires managing Synapse Spark pools. Mapping data flows are cost-effective for batch transformations of this size.
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.
Option B
Why it's wrong here
Requires cluster management and higher cost.
Option C
Why this is correct
Serverless, cost-effective, low overhead.
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.
Option A
Why it's wrong here
Requires manual staging and T-SQL management.
Option D
Why it's wrong here
Requires Synapse Spark pool management.
Common exam traps
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Identify which DP-203 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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: Option C — Option C is correct because mapping data flows run on a serverless ADF integration runtime, which scales automatically and incurs cost only per execution, minimizing management overhead. Option A requires managing staging tables and stored procedures. Option B requires managing Spark clusters. Option D requires managing Synapse Spark pools. Mapping data flows are cost-effective for batch transformations of this size.
Identify which DP-203 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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.
Read the scenario before looking for a memorised answer.
About these practice questions
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Same concept, more angles
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 a data engineer at a retail company. You need to design a batch processing solution that ingests daily sales data from multiple stores. Each store uploads a CSV file to a dedicated folder in Azure Data Lake Storage Gen2. The files have the same schema but may have minor variations in column order and include null values. After ingestion, you must clean the data by removing rows with null values in the 'SalesAmount' column, convert the 'Date' column from string to date type, and aggregate sales by product category. The output should be stored as Parquet files partitioned by year and month in the same Data Lake. You need to choose a compute service and implement the transformation with minimal coding effort. The solution must be cost-effective and require no cluster management. What should you do?
mediumWhy C: Azure Data Factory Mapping Data Flows provide a code-free visual interface to perform transformations like filter, derived column, and aggregate. It can read from ADLS Gen2, handle schema drift, and write partitioned Parquet files. Option A is correct. Option B requires coding in PySpark and cluster management. Option C uses serverless SQL which is not ideal for complex transformations and file partitioning. Option D uses SQL pool which requires provisioning and is more expensive.
Variation 2. You are designing a batch processing solution for a data lake. Source files arrive daily in Parquet format in Azure Data Lake Storage Gen2. The data must be cleaned, aggregated, and loaded into an Azure Synapse SQL pool. The solution should minimize compute costs and management overhead. Which technology should you use for the transformation?
easyWhy B: Option C is correct because Synapse Pipelines (built on ADF) can orchestrate mapping data flows that run on serverless Spark clusters, minimizing management overhead. Option A (HDInsight) requires cluster management. Option B (Databricks) incurs higher costs for simple batch. Option D (SSIS) is legacy and not cloud-native.
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Last reviewed: Jun 21, 2026
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