This DP-203 practice question tests your understanding of develop data processing. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Refer to the exhibit. You have a Mapping Data Flow in Azure Data Factory that reads JSON files from a folder partitioned by year/month/day. The source setting includes a row limit of 10,000. The sink writes Parquet files with a file pattern and partition columns. You notice that the job processes only the first 10,000 rows from the entire dataset instead of 10,000 rows per partition. How should you modify the data flow to achieve row limit per partition?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Remove the rowLimit from source settings. In the Optimize tab of the source, set 'Partition option' to 'Set partitioning' with 'Value' as 'year,month'. Then add a 'Top N' transformation after the source to limit rows per partition.
This approach partitions the data first and then applies a per-partition row limit using Top N.
B
Add a 'RowNumber' transformation after the source, then filter rows where row number <= 10000 per partition.
Why wrong: Data Flow does not have a 'RowNumber' transformation; you would use a Window transformation.
C
In the source settings, change rowLimit to a dynamic expression that uses partition variables to limit per partition.
Why wrong: Source settings do not support dynamic per-partition row limits.
D
Use a 'Distinct' transformation with 'All columns' to remove duplicate rows, which inherently limits rows.
Why wrong: Distinct removes duplicates but does not limit rows; it may not reduce row count to 10,000.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Remove the rowLimit from source settings. In the Optimize tab of the source, set 'Partition option' to 'Set partitioning' with 'Value' as 'year,month'. Then add a 'Top N' transformation after the source to limit rows per partition.
The row limit in the source settings applies to the entire dataset, not per partition. To achieve row limit per partition, you need to set the row limit in a derived column or filter transformation after partitioning. However, the correct approach is to remove the row limit from source and instead use a 'Top N' transformation after a 'Partition' transformation, but Data Flow does not have a Top N per partition. The practical solution is to use a 'Window' transformation to rank rows within each partition and then filter. Alternatively, you can use a 'SurrogateKey' to assign row numbers per partition. The correct answer here is to enable 'Sampling' within each partition using the 'Optimize' tab, but the closest option is to apply the row limit in a filter transformation after partitioning. Option A correctly suggests using the 'Optimize' tab to set 'Partition' and then apply a 'Top N' filter. Option B is wrong because row limit cannot be set per partition in source settings. Option C is wrong because Data Flows do not have a 'RowNumber' transformation. Option D is wrong because 'Distinct' does not limit rows.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
Remove the rowLimit from source settings. In the Optimize tab of the source, set 'Partition option' to 'Set partitioning' with 'Value' as 'year,month'. Then add a 'Top N' transformation after the source to limit rows per partition.
Why this is correct
This approach partitions the data first and then applies a per-partition row limit using Top N.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
✗
Add a 'RowNumber' transformation after the source, then filter rows where row number <= 10000 per partition.
Why it's wrong here
Data Flow does not have a 'RowNumber' transformation; you would use a Window transformation.
✗
In the source settings, change rowLimit to a dynamic expression that uses partition variables to limit per partition.
Why it's wrong here
Source settings do not support dynamic per-partition row limits.
✗
Use a 'Distinct' transformation with 'All columns' to remove duplicate rows, which inherently limits rows.
Why it's wrong here
Distinct removes duplicates but does not limit rows; it may not reduce row count to 10,000.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this DP-203 question in full detail.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DP-203 NAT questions on configuration and troubleshooting.
Develop data processing — This question tests Develop data processing — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Remove the rowLimit from source settings. In the Optimize tab of the source, set 'Partition option' to 'Set partitioning' with 'Value' as 'year,month'. Then add a 'Top N' transformation after the source to limit rows per partition. — The row limit in the source settings applies to the entire dataset, not per partition. To achieve row limit per partition, you need to set the row limit in a derived column or filter transformation after partitioning. However, the correct approach is to remove the row limit from source and instead use a 'Top N' transformation after a 'Partition' transformation, but Data Flow does not have a Top N per partition. The practical solution is to use a 'Window' transformation to rank rows within each partition and then filter. Alternatively, you can use a 'SurrogateKey' to assign row numbers per partition. The correct answer here is to enable 'Sampling' within each partition using the 'Optimize' tab, but the closest option is to apply the row limit in a filter transformation after partitioning. Option A correctly suggests using the 'Optimize' tab to set 'Partition' and then apply a 'Top N' filter. Option B is wrong because row limit cannot be set per partition in source settings. Option C is wrong because Data Flows do not have a 'RowNumber' transformation. Option D is wrong because 'Distinct' does not limit rows.
What should I do if I get this DP-203 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DP-203 NAT questions on configuration and troubleshooting.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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