DP-700 Ingest and Transform Data Practice Question
You are configuring a Dataflow Gen2 to perform incremental updates for a large fact table. Which TWO steps are essential to ensure the dataflow only processes new or changed records during each run?
⚠ Common exam trap
Candidates often miss the need for a parameter. Without a 'last refresh' parameter, the system cannot identify which records are new, leading to full reloads or duplicate data ingestion.
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
✓
Create a parameter to store the last successful refresh date.
Incremental loading in Dataflows Gen2 requires a mechanism to identify new data and a logic to handle how that data is integrated into the destination. This typically involves using parameters to filter source data based on a 'last modified' date and configuring the destination to append new records.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Create a parameter to store the last successful refresh date.
Why this is correct
Parameters are used to store stateful information like timestamps. By filtering the source query using a parameter that tracks the last refresh, the Dataflow only retrieves records created or modified after that date, significantly reducing the volume of data processed during each execution.
- ✓
Set the destination table action to 'Append'.
Why this is correct
The Append action ensures that the records identified as new by the incremental logic are added to the existing target table. If 'Replace' were used, the entire table would be overwritten with only the new records, resulting in the loss of all historical data.
- ✗
Enable 'High Concurrency' mode for the Dataflow.
Why it's wrong here
High Concurrency is a Spark session setting that allows multiple jobs to run on the same cluster. It does not affect the logical filtering of data for incremental loads and is not a configuration option within the Dataflow Gen2 user interface or its refresh settings.
- ✗
Use the 'Remove Duplicates' transformation on the entire table.
Why it's wrong here
While removing duplicates might be necessary for data quality, it is a transformation step that processes all rows in the current buffer. It does not facilitate incremental loading from the source; rather, it is a data cleaning step applied after the data has been ingested.
- ✗
Configure a 'Tumbling Window' trigger in the Lakehouse.
Why it's wrong here
Tumbling Window triggers are components of Data Factory pipelines, not Dataflows themselves. While a pipeline can trigger a Dataflow, the internal logic for incremental processing must be defined within the Dataflow's Power Query steps and parameters to be effective.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
This DP-700 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-700 exam.