DP-203 Develop data processing Practice Question
Exhibit
{
"dataflows": [
{
"name": "TransformSales",
"properties": {
"sources": [
{
"name": "SalesSource",
"dataset": {
"referenceName": "SalesDataset",
"type": "DatasetReference"
}
}
],
"transformations": [
{
"name": "AggregateSales",
"type": "Aggregate",
"inputs": ["SalesSource"],
"aggregates": [
{
"column": "TotalAmount",
"function": "SUM",
"input": "Amount"
}
]
}
],
"sink": {
"name": "SalesSink",
"dataset": {
"referenceName": "AggregatedSalesDataset",
"type": "DatasetReference"
}
}
}
}
]
}Refer to the exhibit. You have a mapping data flow in Azure Data Factory that aggregates sales data. The data flow runs successfully but the sink table contains only the total sum per run instead of per product. What is missing?
⚠ Common exam trap
Many exam-takers confuse row-level filtering or sink configuration with aggregation granularity — candidates overlook that an empty Group By in the Aggregate transformation collapses all rows into a single global total.
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 aggregate transformation does not have a groupBy column
In an ADF mapping data flow, the Aggregate transformation computes aggregates based on the columns listed in the Group By tab. If no groupBy column is specified, the transformation treats the entire dataset as a single group, producing one row with the total sum. Adding the product column to groupBy makes the aggregation produce one row per product.
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 source dataset is not filtering by date
Why it's wrong here
Date filtering restricts which rows enter the flow; it does not change the aggregation grain, so the sink still receives one total per run. Filtering is tempting because partitioning by date is standard in pipelines, but the symptom is a missing group-by column in the aggregate transformation, not excess input rows.
- ✓
The aggregate transformation does not have a groupBy column
Why this is correct
An aggregate transformation without a groupBy column collapses all rows into a single group, producing one total sum per run. Adding ProductID as the groupBy column makes the aggregation produce one row per product, matching the expected sink output.
- ✗
The data flow is missing a filter transformation
Why it's wrong here
A filter transformation removes rows but leaves the aggregate's group-by unchanged, so output remains a single total. Filtering is tempting because it is a common data flow step, yet the defect is the aggregate transformation's missing product column, not unwanted records entering the stream.
- ✗
The sink dataset is not configured to append
Why it's wrong here
Append mode only adds rows; it cannot create the per-product grouping the sink lacks. The aggregate transformation's group-by must include product, otherwise every row collapses into one total. Append is tempting because it preserves history across runs, which suits fact-table loading, not fixing aggregation granularity.
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Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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