DP-700 Ingest and Transform Data Practice Question
You are designing a data pipeline in Microsoft Fabric to ingest incremental data from an on-premises SQL Server. You need to ensure that only records modified since the last pipeline run are ingested. Which approach should you implement to maintain optimal performance and minimal data redundancy?
⚠ Common exam trap
Candidates often select full table reloads or static partitioning strategies, neglecting the requirement to dynamically track incremental changes using a watermark column.
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
✓
Configure the Copy activity to use a dynamic query with a watermark column from a control table.
Implementing a watermark column strategy is the standard practice for incremental loading in Fabric data pipelines. By storing the last processed timestamp or incremental ID in a control table, the pipeline can dynamically filter incoming data. This approach significantly reduces compute costs and ingestion time compared to full table loads, ensuring that only delta changes are processed while maintaining data consistency across the lakehouse destination.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Execute a full truncate and load operation using a Lookup activity before the Copy activity.
Why it's wrong here
Full truncate and load operations are inefficient for high-volume datasets as they consume excessive bandwidth and compute resources. This strategy ignores the need for incremental processing and increases the risk of performance bottlenecks, negating the benefits of using Fabric pipelines to handle delta changes efficiently in modern data architectures.
- ✓
Configure the Copy activity to use a dynamic query with a watermark column from a control table.
Why this is correct
Using a dynamic query with a watermark column allows the Copy activity to fetch only new or updated records based on the last ingestion time. This methodology minimizes network overhead and storage costs, providing a robust pattern for incremental data integration that scales effectively as your source data volume grows over time.
- ✗
Use a Dataflow Gen2 to perform a full join between the source and the existing lakehouse table.
Why it's wrong here
Performing a full join inside Dataflow Gen2 to identify changes is resource-intensive because it requires reading the entire dataset from both source and destination. This approach leads to poor performance and increased costs, as it does not leverage the native incremental push-down capabilities supported by SQL-based source connectors.
- ✗
Enable Change Data Capture (CDC) on the source SQL Server and ingest all logs into a CSV file.
Why it's wrong here
While CDC is effective for tracking changes, simply ingesting raw logs into a CSV file does not inherently update the target table. This process adds unnecessary complexity and requires additional downstream transformation steps to parse the logs and merge them into the destination lakehouse, which is less efficient than watermark filtering.
About these practice questions
Courseiva writes every DP-700 question from scratch — 152 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.