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Question 294 of 846
Develop data processing →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is that the source file does not contain a column named 'Name'. This is the most likely cause because when a Synapse pipeline encounters a source dataset configured to read a column that does not exist in the actual file, Azure Data Factory interprets the missing column as a type mismatch error, even though the data types themselves are compatible. The service cannot map a nonexistent source column to the sink column 'FullName', so it throws a misleading 'Type mismatch' error that actually points to a schema mismatch rather than a data type conflict. On the DP-203 exam, this scenario tests your understanding of how Synapse pipelines handle schema drift and column mapping, often appearing as a trap where candidates assume the error relates to data types like String versus Integer. A key memory tip is to remember that "missing column" can masquerade as a "type mismatch" in Azure Data Factory, so always verify the source file's actual column names before troubleshooting data types.

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.

Exhibit

{
  "type": "Microsoft.DataFactory/factories/pipelines",
  "properties": {
    "activities": [
      {
        "name": "CopyData",
        "type": "Copy",
        "policy": {
          "retry": 2,
          "timeout": "0:10:00"
        },
        "inputs": [
          {
            "referenceName": "DelimitedTextInput",
            "type": "DatasetReference"
          }
        ],
        "outputs": [
          {
            "referenceName": "ParquetOutput",
            "type": "DatasetReference"
          }
        ],
        "typeProperties": {
          "source": {
            "type": "DelimitedTextSource",
            "storeSettings": {
              "type": "AzureBlobStorageReadSettings",
              "recursive": true
            }
          },
          "sink": {
            "type": "ParquetSink",
            "storeSettings": {
              "type": "AzureBlobFSWriteSettings"
            },
            "formatSettings": {
              "type": "ParquetWriteSettings"
            }
          },
          "translator": {
            "type": "TabularTranslator",
            "mappings": [
              {
                "source": {
                  "name": "Name",
                  "type": "String"
                },
                "sink": {
                  "name": "FullName",
                  "type": "String"
                }
              }
            ]
          }
        }
      }
    ]
  }
}

Refer to the exhibit. The pipeline fails with a 'Type mismatch' error. The source file has a column 'Name' of type String, and the destination table expects 'FullName' of type String. What is the most likely cause of the failure?

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
Read the full NAT/PAT explanation →

Exhibit

{
  "type": "Microsoft.DataFactory/factories/pipelines",
  "properties": {
    "activities": [
      {
        "name": "CopyData",
        "type": "Copy",
        "policy": {
          "retry": 2,
          "timeout": "0:10:00"
        },
        "inputs": [
          {
            "referenceName": "DelimitedTextInput",
            "type": "DatasetReference"
          }
        ],
        "outputs": [
          {
            "referenceName": "ParquetOutput",
            "type": "DatasetReference"
          }
        ],
        "typeProperties": {
          "source": {
            "type": "DelimitedTextSource",
            "storeSettings": {
              "type": "AzureBlobStorageReadSettings",
              "recursive": true
            }
          },
          "sink": {
            "type": "ParquetSink",
            "storeSettings": {
              "type": "AzureBlobFSWriteSettings"
            },
            "formatSettings": {
              "type": "ParquetWriteSettings"
            }
          },
          "translator": {
            "type": "TabularTranslator",
            "mappings": [
              {
                "source": {
                  "name": "Name",
                  "type": "String"
                },
                "sink": {
                  "name": "FullName",
                  "type": "String"
                }
              }
            ]
          }
        }
      }
    ]
  }
}

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 source file does not contain a column named 'Name'.

The pipeline fails with a 'Type mismatch' error because the source file does not contain a column named 'Name'. When the source dataset is configured to read a 'Name' column but the actual file lacks that column, Azure Data Factory or Synapse Pipelines cannot map it to the sink column 'FullName', resulting in a type mismatch error. The error message is misleading because the mismatch is not about data types but about missing source columns, which the service treats as a type incompatibility.

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.

  • ✗

    The recursive setting on the source store is causing duplicate reads.

    Why it's wrong here

    Recursive=true reads subfolders, but that would not cause a type mismatch error.

  • ✓

    The source file does not contain a column named 'Name'.

    Why this is correct

    The column mapping references a source column 'Name', but if the actual file has a different column header, the copy activity fails with type mismatch.

    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 source file has leading/trailing whitespace in the column names.

    Why it's wrong here

    Whitespace in column names would cause a column not found error, not a type mismatch.

  • ✗

    The sink column 'FullName' is expecting a different data type than String.

    Why it's wrong here

    Both are defined as String, so no type mismatch there.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates assume 'Type mismatch' always refers to incompatible data types (e.g., String vs. Int), when in fact it can also be triggered by a missing source column that the pipeline expects to map, causing a schema-level mismatch.

Detailed technical explanation

How to think about this question

In Azure Data Factory, when a source dataset defines a column that does not exist in the actual file, the service cannot perform the mapping and raises a 'Type mismatch' error because it attempts to read a null or missing value into a typed sink column. This behavior is consistent with the way ADF handles schema drift and column mapping: if a source column is absent, the pipeline treats it as a schema mismatch rather than a missing data issue. In real-world scenarios, this often occurs when source files have inconsistent headers (e.g., renamed columns) and the dataset schema is not updated accordingly.

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 source file does not contain a column named 'Name'. — The pipeline fails with a 'Type mismatch' error because the source file does not contain a column named 'Name'. When the source dataset is configured to read a 'Name' column but the actual file lacks that column, Azure Data Factory or Synapse Pipelines cannot map it to the sink column 'FullName', resulting in a type mismatch error. The error message is misleading because the mismatch is not about data types but about missing source columns, which the service treats as a type incompatibility.

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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This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.