Describe core data concepts →mediumMultiple ChoiceObjective-mapped

DP-900 Describe core data concepts Practice Question

Exhibit

Refer to the exhibit.

```json
{
  "properties": {
    "folder": "transform",
    "type": "MappingDataFlow",
    "typeProperties": {
      "sources": [
        {
          "name": "source1",
          "dataset": {
            "referenceName": "SalesCSV",
            "type": "DatasetReference"
          },
          "sourceSettings": {
            "format": "DelimitedText",
            "delimiter": ","
          }
        }
      ],
      "sinks": [
        {
          "name": "sink1",
          "dataset": {
            "referenceName": "SalesParquet",
            "type": "DatasetReference"
          },
          "sinkSettings": {
            "format": "Parquet"
          }
        }
      ],
      "transformations": [
        {
          "name": "RemoveDuplicates",
          "type": "Aggregate",
          "inputs": ["source1"],
          "aggregates": [
            {"column": "OrderID", "operation": "count", "output": "Count"}
          ]
        }
      ]
    }
  }
}
```

You are reviewing a Data Factory mapping data flow definition. What is the primary purpose of this data flow?

⚠ Common exam trap

Test-takers frequently confuse the Aggregate transformation's count with a Filter or Pivot operation, not recognizing that grouping by a column and counting inherently removes duplicates by collapsing rows.

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

✓

Remove duplicate OrderIDs by counting them

The mapping data flow includes an Aggregate transformation configured with a group by on OrderID and a count aggregation. This removes duplicate OrderIDs by collapsing multiple rows with the same OrderID into a single row and counting the occurrences, which is the primary purpose of the data flow.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Pivot the data by OrderID
Why it's wrong here
This is incorrect because an Aggregate transformation, not a Pivot, is used in the data flow definition. A Pivot transformation rotates unique row values into columns (e.g., turning each OrderID into a separate column), which would not count or consolidate duplicate OrderIDs. The described data flow has no pivot transformation.
✗
Filter rows where OrderID is null
Why it's wrong here
A Filter transformation evaluates a boolean condition to include or exclude rows; filtering on OrderID is null only removes orders with missing key values. It does not group or aggregate data, so it cannot count how many times each OrderID appears. The data flow definition contains no filter transformation, making this option incorrect.
✓
Remove duplicate OrderIDs by counting them
Why this is correct
This is correct because the Aggregate transformation in the data flow groups rows by OrderID and applies a count expression, such as count(OrderID), to calculate occurrences per OrderID. Rows with a count greater than 1 are duplicates, allowing the definition to identify (and subsequently remove) duplicate OrderIDs. This matches the requirement to remove duplicate OrderIDs by counting them.
✗
Merge two data sources
Why it's wrong here
This is incorrect because merging (via Join or Union) combines two separate data streams into one, but the data flow definition has only a single source transformation. With only one input stream, there are no two data sources to join or union. The duplicate-detection logic is performed by grouping and counting within that one source.

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DP-900 Describe core data concepts Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

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