Describe an analytics workload on Azure →mediumMultiple ChoiceObjective-mapped

DP-900 Describe an analytics workload on Azure Practice Question

Exhibit

Refer to the exhibit.

```json
{
  "dataflows": [
    {
      "name": "ProcessSales",
      "source": {
        "type": "AzureSqlDatabase",
        "connection": "SalesDB",
        "query": "SELECT * FROM Sales"
      },
      "sink": {
        "type": "AzureBlobStorage",
        "folderPath": "salesdata",
        "fileType": "Parquet"
      }
    }
  ]
}
```

You are reviewing the Azure Data Factory mapping data flow configuration above. Which transformation is missing to ensure that only sales from the current year are loaded?

⚠ Common exam trap

It's easy for candidates to confuse column-level transformations (Derived column) with row-level filtering, assuming that extracting the year automatically filters data, whereas Filter is the only transformation that actually removes 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

✓

Filter transformation

The Filter transformation is used in mapping data flows to restrict rows based on a condition. To load only sales from the current year, you would apply a filter condition such as `year(SalesDate) == year(currentDate())`, which removes all rows not matching the current year. This is the correct transformation for row-level filtering.

Answer analysis

Option-by-option breakdown

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

✗
Derived column transformation
Why it's wrong here
The Derived Column transformation in mapping data flows creates or modifies columns using expressions, but it never removes rows from the dataset. Its purpose is row-level schema shaping — for example, calculating a new field or updating an existing value — and it passes every input row through to the output. To limit rows to only the current year, you need a row-level predicate, which a derived column does not provide.
✗
Aggregate transformation
Why it's wrong here
The Aggregate transformation is designed to collapse multiple rows into grouped summary rows, such as SUM, COUNT, or AVG, based on group-by keys. It fundamentally changes the granularity of the data by reducing the row count according to grouping, not by evaluating a boolean condition per row. Filtering to the current year requires preserving every individual row that matches the date range, which aggregation cannot do because it merges rows rather than selecting a subset of them.
✗
Window transformation
Why it's wrong here
The Window transformation is intended for analytical operations over a set of rows that share a common key, enabling ranking, running totals, lag/lead, and other over-window functions. It requires an OVER clause with partitioning and ordering, and it outputs the same number of rows as its input — it does not discard rows. A filter, in contrast, evaluates a simple predicate and removes rows that fail it, which is the correct approach for isolating data from the current year.
✓
Filter transformation
Why this is correct
The Filter transformation in Azure Data Factory mapping data flows is the row-level predicate operation that keeps only rows satisfying a specified condition. By setting the condition to something like year(OrderDate) == year(currentDate()) or to_date(OrderDate) >= '2025-01-01', you can restrict the dataset to the current year. This transformation is purpose-built for row selection and does not alter the schema or group data, making it the correct choice for this requirement.

Go deeper

DP-900 Describe an analytics workload on Azure Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

Related to this question