Question 413 of 851

Develop data processing →mediumMultiple SelectObjective-mapped

Error Handling in Mapping Data Flows: Allow Errors and Conditional Split

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.

You are designing a data processing pipeline in Azure Data Factory that uses a Mapping Data Flow. You need to handle errors gracefully, such as when a row fails to convert a column value. Which TWO actions should you take? (Choose two.)

A
Wrap the data flow in a Try-Catch activity in the pipeline.
Why wrong: Mapping Data Flows do not have try-catch; you handle errors within the data flow.
B
Set the data flow's error handling to 'Abort on error' to stop processing on first failure.
Why wrong: Aborting is not graceful; it stops the entire pipeline.
C
Enable schema drift on the source to automatically handle data type mismatches.
Why wrong: Schema drift handles new columns, not conversion errors.
D
Configure the sink transformation to allow errors and log error rows to a separate file.
Sink can be configured to continue on error and write error rows to a file.
E
Use a Conditional Split transformation to separate rows that cause errors based on a condition.
Conditional split allows routing error rows to a separate sink for logging.

Full breakdown with real-world context →

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

✓

Configure the sink transformation to allow errors and log error rows to a separate file.

Option D is correct because configuring the sink transformation to allow errors and log error rows to a separate file enables graceful error handling in Mapping Data Flows. This approach captures rows that fail during transformation (e.g., type conversion errors) and writes them to a designated error output, allowing the pipeline to continue processing valid rows. Option E is correct because a Conditional Split transformation can proactively identify rows that are likely to cause errors based on a condition (e.g., checking for null or invalid data types) and route them to a separate path for logging or remediation, preventing them from reaching the sink and causing failures.

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.

✗
Wrap the data flow in a Try-Catch activity in the pipeline.
Why it's wrong here
Mapping Data Flows do not have try-catch; you handle errors within the data flow.
✗
Set the data flow's error handling to 'Abort on error' to stop processing on first failure.
Why it's wrong here
Aborting is not graceful; it stops the entire pipeline.
✗
Enable schema drift on the source to automatically handle data type mismatches.
Why it's wrong here
Schema drift handles new columns, not conversion errors.
✓
Configure the sink transformation to allow errors and log error rows to a separate file.
Why this is correct
Sink can be configured to continue on error and write error rows to a file.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use a Conditional Split transformation to separate rows that cause errors based on a condition.
Why this is correct
Conditional split allows routing error rows to a separate sink for logging.
Related concept
Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often confuse pipeline-level error handling (like Try-Catch) with data flow-level error handling, or they assume schema drift can fix data type mismatches, when in fact it only handles structural changes at the source.

Detailed technical explanation

How to think about this question

In Mapping Data Flows, the sink transformation's 'Allow error' option (under Sink settings) writes failed rows to a separate file or folder while continuing to process valid rows, using a row-by-row error handling mechanism. The Conditional Split transformation evaluates expressions row-by-row and can check for conversion errors using functions like `isNull()` or `tryCast()` before the data reaches a transformation that might fail. A real-world scenario is ingesting semi-structured JSON logs where a timestamp field occasionally contains malformed strings; using Conditional Split to route those rows to a dead-letter queue allows the pipeline to process the rest without interruption.

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 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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. 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.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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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 — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Configure the sink transformation to allow errors and log error rows to a separate file. — Option D is correct because configuring the sink transformation to allow errors and log error rows to a separate file enables graceful error handling in Mapping Data Flows. This approach captures rows that fail during transformation (e.g., type conversion errors) and writes them to a designated error output, allowing the pipeline to continue processing valid rows. Option E is correct because a Conditional Split transformation can proactively identify rows that are likely to cause errors based on a condition (e.g., checking for null or invalid data types) and route them to a separate path for logging or remediation, preventing them from reaching the sink and causing failures.

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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