The answer is to set the sink transformation's 'Optimize' tab to 'Single partition'. This is correct because reducing partitions in mapping data flow for performance minimizes the metadata overhead and file count when writing to a Delta table; a single partition forces all 50 million rows into one output file, avoiding the small-file problem that slows down write operations and downstream reads. On the DP-203 exam, this scenario tests your understanding of how partition optimization in Mapping Data Flows directly impacts sink performance, often appearing as a trap where candidates mistakenly adjust source partitioning or use "Auto" settings. A common memory tip is to think "sink shrink": when your data is moderate and you want fewer, larger files, you shrink the sink partitions to one.
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 configuring a data pipeline in Azure Data Factory that uses a Mapping Data Flow. The source is a SQL Server table with 50 million rows. The sink is a Delta table in ADLS Gen2. The pipeline runs slowly. You need to improve performance by reducing the number of partitions in the data flow. Which setting should you adjust?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
In the sink transformation, set the 'Optimize' tab to 'Single partition'.
Option C is correct because setting the sink transformation's 'Optimize' tab to 'Single partition' forces all data to be written into a single partition, which reduces the number of output files and the associated metadata overhead. This can improve performance when the downstream processing or storage benefits from fewer, larger files, especially for small-to-medium datasets or when the sink is a Delta table that must be compacted later.
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.
✗
In the source transformation, set the 'Optimize' tab to 'Use current partitioning'.
Why it's wrong here
Source optimization affects read, not sink write partitions.
✗
Decrease the cluster size to 4 cores.
Why it's wrong here
Cluster size affects parallelism but not partition count directly.
✓
In the sink transformation, set the 'Optimize' tab to 'Single partition'.
Why this is correct
Sink optimization controls how data is written; setting to single partition reduces file count.
Related concept
Read the scenario before looking for a memorised answer.
✗
In the pipeline activity, set the 'Data flow' property 'partitionOption' to 'Single partition'.
Why it's wrong here
The partition option is set within the data flow transformations, not at pipeline level.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse the 'Optimize' tab settings on the sink with the 'Partition option' in the source or pipeline activity, or mistakenly think that reducing cluster size (Option B) is a valid performance tuning technique for partition reduction.
Detailed technical explanation
How to think about this question
In Mapping Data Flows, the 'Optimize' tab on sink transformations allows you to control file partitioning at write time. Setting 'Single partition' forces the data into one partition, which reduces the number of small files (a common performance bottleneck in Delta Lake). Under the hood, this setting changes the Spark repartitioning strategy, merging all partitions into a single coalesced partition before writing, which can significantly reduce I/O operations and metadata transactions in ADLS Gen2.
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
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: In the sink transformation, set the 'Optimize' tab to 'Single partition'. — Option C is correct because setting the sink transformation's 'Optimize' tab to 'Single partition' forces all data to be written into a single partition, which reduces the number of output files and the associated metadata overhead. This can improve performance when the downstream processing or storage benefits from fewer, larger files, especially for small-to-medium datasets or when the sink is a Delta table that must be compacted later.
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.
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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