DP-700 Ingest and Transform Data Practice Question
You are configuring a Fabric pipeline to perform incremental loads from a source system. Which approach is considered the best practice to track changes efficiently?
⚠ Common exam trap
Candidates frequently choose full table overwrites or complex custom logic instead of the standard watermark pattern, failing to recognize that watermarking is the industry-standard for incremental efficiency.
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
✓
Use a watermark column for filtering
Using a watermarking column (such as a 'LastModified' timestamp or an incrementing ID) is the standard industry practice for incremental loading. By querying only rows with a value greater than the previous high-water mark, you significantly reduce the amount of data transferred and processed. This approach minimizes source system load and execution time, making it essential for building performant, cost-effective pipelines that handle large-scale data updates over time.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Truncate and reload the entire dataset every time
Why it's wrong here
Truncate and reload is inefficient for large datasets. It consumes unnecessary bandwidth and compute resources, increases the load on the source system, and creates long-running pipelines. It is only suitable for very small lookup tables that change infrequently and require full replacement.
- ✓
Use a watermark column for filtering
Why this is correct
Using a watermark column to track incremental changes is the most efficient and scalable approach. It allows the pipeline to only ingest records created or modified since the last successful load, which minimizes data movement and optimizes both the source and target system performance.
- ✗
Compare every row using a hash function
Why it's wrong here
Calculating hashes for every row in the source and the target to detect changes is computationally expensive. It requires reading the entire source dataset and the entire target dataset to perform a comparison, which negates the performance benefits of an incremental loading strategy.
- ✗
Delete all data older than one month
Why it's wrong here
Deleting data based on a static timeframe is not a reliable incremental loading strategy. It does not account for updates to older records or data that may have been missed in previous loads. This approach risks data loss and does not track actual changes in the source.
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.