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Develop data processing →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is that an explicitly defined sink mapping without auto-mapping is the most likely reason the target table lacks the expected drifted columns. When you enable allowSchemaDrift on the source, Azure Data Factory’s Mapping Data Flow detects extra columns in the CSV file, but those drifted columns are only written to the sink if the sink mapping uses auto-mapping. If you define explicit column-by-column mappings, that configuration overrides auto-mapping, causing any drifted columns to be silently ignored—even though validateSchema is false. On the Microsoft Azure Data Engineer Associate DP-203 exam, this scenario tests your understanding of how sink mappings interact with schema drift, a common trap where candidates assume allowSchemaDrift alone guarantees all columns appear in the output. Remember the key rule: schema drift detection happens at the source, but propagation to the sink requires auto-mapping. A useful memory tip is “Drift needs auto-lift”—if you want drifted columns to land in the sink, keep the mapping on auto.

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.

Exhibit

Refer to the exhibit.

{
  "type": "MappingDataFlow",
  "typeProperties": {
    "sources": [
      {
        "dataset": {
          "referenceName": "DelimitedTextSource",
          "type": "DatasetReference"
        },
        "script": "source(\n  output() as (\n    col1 string,\n    col2 string\n  ),\n  allowSchemaDrift: true,\n  validateSchema: false\n) ~> Source1"
      }
    ],
    "sinks": [
      {
        "dataset": {
          "referenceName": "AzureSynapseTableSink",
          "type": "DatasetReference"
        },
        "script": "Source1 sink(\n  input() as (\n    col1 string,\n    col2 string\n  ),\n  allowSchemaDrift: true,\n  validateSchema: false\n) ~> Sink1"
      }
    ]
  }
}

You are reviewing a Mapping Data Flow in Azure Data Factory that copies data from a CSV file to an Azure Synapse table. The data flow uses 'allowSchemaDrift: true' and 'validateSchema: false'. After running the pipeline, you notice that the target table does not have the expected columns. The CSV file sometimes has extra columns. What is the most likely reason?

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.

Question 1hardmultiple choice
Full question →

Exhibit

Refer to the exhibit.

{
  "type": "MappingDataFlow",
  "typeProperties": {
    "sources": [
      {
        "dataset": {
          "referenceName": "DelimitedTextSource",
          "type": "DatasetReference"
        },
        "script": "source(\n  output() as (\n    col1 string,\n    col2 string\n  ),\n  allowSchemaDrift: true,\n  validateSchema: false\n) ~> Source1"
      }
    ],
    "sinks": [
      {
        "dataset": {
          "referenceName": "AzureSynapseTableSink",
          "type": "DatasetReference"
        },
        "script": "Source1 sink(\n  input() as (\n    col1 string,\n    col2 string\n  ),\n  allowSchemaDrift: true,\n  validateSchema: false\n) ~> Sink1"
      }
    ]
  }
}

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

✓

The sink mapping is explicitly defined and does not include auto-mapping for drifted columns.

Option B is correct because when 'allowSchemaDrift' is enabled on the source, drifted columns are detected but will only be written to the sink if auto-mapping is used. If the sink mapping is explicitly defined (e.g., column-by-column mappings), it overrides auto-mapping and drifted columns are ignored. Since the target table is missing expected columns, the explicit mapping likely excludes the drifted columns.

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.

  • ✗

    Schema drift is not enabled on the source.

    Why it's wrong here

    The source has allowSchemaDrift: true.

  • ✓

    The sink mapping is explicitly defined and does not include auto-mapping for drifted columns.

    Why this is correct

    The sink script defines input columns, so extra columns are not mapped.

    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.

  • ✗

    The sink dataset has a fixed schema that does not allow drift.

    Why it's wrong here

    The sink has allowSchemaDrift: true.

  • ✗

    The source dataset has a fixed schema that does not include extra columns.

    Why it's wrong here

    The source dataset is DelimitedText without schema.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates assume enabling 'allowSchemaDrift' on the source automatically writes all columns to the sink, but they overlook that explicit sink mappings override auto-mapping and exclude drifted columns.

Detailed technical explanation

How to think about this question

In Mapping Data Flows, schema drift is handled at the source by reading all columns, including extra ones, into a 'drifted' column set. The sink's mapping mode (auto-mapping vs. explicit mapping) determines whether these drifted columns are written. Auto-mapping dynamically maps all source columns (including drifted) to the sink, while explicit mapping only writes columns defined in the mapping rules. This is crucial when source schemas vary, as explicit mappings can silently drop unexpected columns.

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: The sink mapping is explicitly defined and does not include auto-mapping for drifted columns. — Option B is correct because when 'allowSchemaDrift' is enabled on the source, drifted columns are detected but will only be written to the sink if auto-mapping is used. If the sink mapping is explicitly defined (e.g., column-by-column mappings), it overrides auto-mapping and drifted columns are ignored. Since the target table is missing expected columns, the explicit mapping likely excludes the drifted columns.

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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Last reviewed: Jun 24, 2026

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