- A
Modify the Spark job definition to include 'overwrite' save mode.
Why wrong: While modifying the job definition is a valid approach, the question asks for a pipeline configuration change. However, this option is also plausible but not a pipeline change.
- B
Change the output path in the arguments to a non-existing folder.
Why wrong: This would avoid the error but not overwrite the existing data as intended.
- C
Set the pipeline property 'outputMode' to 'overwrite' on the SynapseSparkJob activity.
Why wrong: There is no such property as 'outputMode' on the SynapseSparkJob activity.
- D
Add a Delete activity before the Spark job to delete the output folder if it exists.
Using a Delete activity in the pipeline to remove the existing output folder before running the Spark job ensures the job can write without conflict.
Quick Answer
The answer is to add a Delete activity before the Spark job to remove the existing output directory. This is correct because Spark jobs in Azure Synapse pipelines default to failing when the output path already exists, as the default save mode is "errorifexists." The Delete activity explicitly clears the folder before execution, allowing the job to write fresh data without changing the Spark job definition or code. On the DP-203 exam, this scenario tests your understanding of pipeline orchestration versus Spark configuration—many candidates mistakenly try to set `spark.sql.sources.partitionOverwriteMode` or use overwrite mode in code, but the exhibit shows a pipeline-level job definition where the simplest fix is a preceding Delete activity. A common trap is confusing Spark’s internal overwrite settings with pipeline-level data flow control. Memory tip: "Delete first, then write—pipeline logic, not Spark magic."
DP-203 Develop data processing Practice Question
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. The pipeline executes a Spark job definition that reads from the input path and writes to the output path. The job fails with an error indicating that the output path already exists. The job is configured with default behavior. What configuration change should you make to allow the job to overwrite the existing output?
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
Add a Delete activity before the Spark job to delete the output folder if it exists.
Spark jobs by default fail if the output directory exists. To overwrite, you need to set the Spark configuration 'spark.hadoop.parquet.enable.summary-metadata' is not relevant. The correct approach is to set the Spark configuration 'spark.sql.sources.partitionOverwriteMode' to 'STATIC' or use 'overwrite' save mode in the code. However, in the exhibit, the job is a Spark job definition that calls a file; the simplest way is to set the argument to use overwrite mode. Option B (Delete activity) is a separate activity. Option C changes the job definition. Option D (output mode) is not a pipeline property.
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.
- ✗
Modify the Spark job definition to include 'overwrite' save mode.
Why it's wrong here
While modifying the job definition is a valid approach, the question asks for a pipeline configuration change. However, this option is also plausible but not a pipeline change.
- ✗
Change the output path in the arguments to a non-existing folder.
Why it's wrong here
This would avoid the error but not overwrite the existing data as intended.
- ✗
Set the pipeline property 'outputMode' to 'overwrite' on the SynapseSparkJob activity.
Why it's wrong here
There is no such property as 'outputMode' on the SynapseSparkJob activity.
- ✓
Add a Delete activity before the Spark job to delete the output folder if it exists.
Why this is correct
Using a Delete activity in the pipeline to remove the existing output folder before running the Spark job ensures the job can write without conflict.
Related concept
Static NAT maps one inside address to one outside address.
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.
Trap categories for this question
Command / output trap
There is no such property as 'outputMode' on the SynapseSparkJob activity.
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.
What to study next
Got this wrong? Here's your next step.
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.
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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 — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Add a Delete activity before the Spark job to delete the output folder if it exists. — Spark jobs by default fail if the output directory exists. To overwrite, you need to set the Spark configuration 'spark.hadoop.parquet.enable.summary-metadata' is not relevant. The correct approach is to set the Spark configuration 'spark.sql.sources.partitionOverwriteMode' to 'STATIC' or use 'overwrite' save mode in the code. However, in the exhibit, the job is a Spark job definition that calls a file; the simplest way is to set the argument to use overwrite mode. Option B (Delete activity) is a separate activity. Option C changes the job definition. Option D (output mode) is not a pipeline property.
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.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 21, 2026
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