This DP-203 practice question tests your understanding of develop data processing. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 running a pipeline in Azure Data Factory that uses a Mapping Data Flow. The data flow reads from Azure SQL Database and writes to Azure Synapse Analytics. You find that the data flow is very slow. Which configuration change would most likely improve performance?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the 'Compute type' to 'Memory Optimized' and the 'Core count'
Mapping Data Flows in Azure Data Factory execute on Spark clusters. The default compute configuration may not provide sufficient memory or parallelism for large data volumes. Increasing the 'Compute type' to 'Memory Optimized' and raising the 'Core count' directly allocates more memory and processing cores to the Spark cluster, which accelerates transformations and data movement between Azure SQL Database and Azure Synapse Analytics.
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.
✗
Set the 'Staging' option to 'Use staging'
Why it's wrong here
Staging is not applicable to data flows.
✓
Increase the 'Compute type' to 'Memory Optimized' and the 'Core count'
Why this is correct
More compute resources speed up data flow execution.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Enable staging for the sink and use PolyBase
Why it's wrong here
This is a valid optimization for copy activities, not data flows.
✗
Set the 'Partition option' to 'Round robin' on the source
Why it's wrong here
Partitioning may help, but compute resources are the primary factor.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Mapping Data Flow performance tuning with Copy Activity optimizations, such as PolyBase or staging, which are irrelevant to Spark-based data flows.
Detailed technical explanation
How to think about this question
Mapping Data Flows run on Azure Integration Runtime (IR) clusters that scale based on core count and compute type. Memory Optimized compute types allocate more memory per core, which is critical for operations like joins, aggregations, and window functions that spill to disk when memory is insufficient. In real-world scenarios, a data flow reading millions of rows from Azure SQL Database and writing to Synapse can be bottlenecked by Spark executor memory, not by network or I/O, making compute tuning the most impactful change.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
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: Increase the 'Compute type' to 'Memory Optimized' and the 'Core count' — Mapping Data Flows in Azure Data Factory execute on Spark clusters. The default compute configuration may not provide sufficient memory or parallelism for large data volumes. Increasing the 'Compute type' to 'Memory Optimized' and raising the 'Core count' directly allocates more memory and processing cores to the Spark cluster, which accelerates transformations and data movement between Azure SQL Database and Azure Synapse Analytics.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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