DP-700 Ingest and Transform Data Practice Question
You are configuring a medallion architecture in Microsoft Fabric using Lakehouse Delta tables. You need to implement an incremental data load pattern from a bronze table to a silver table using a PySpark notebook. Which TWO actions must you perform to ensure idempotency and high performance? Choose exactly two.
⚠ Common exam trap
Candidates often select 'full overwrite' as an option, which destroys idempotency, or forget to mention the business surrogate key, which is essential for correctly merging data updates.
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 the Delta Lake merge operation based on a unique business surrogate key to handle both inserts and updates idempotently.
Implementing merge operations with watermarking and schema evolution enables incremental updates while maintaining table consistency. Idempotency guarantees that running the pipeline multiple times for the same time window yields identical results without data duplication, which is critical for robust enterprise data engineering pipelines in Microsoft Fabric.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use the Delta Lake merge operation based on a unique business surrogate key to handle both inserts and updates idempotently.
Why this is correct
The Delta Lake merge command allows conditional matching based on primary or business keys, enabling seamless upserts. This ensures that rerun pipelines do not duplicate records, maintaining strict idempotency across incremental processing cycles in the silver layer.
- ✗
Overwrite the entire silver table partition dynamically on every execution without checking for existing record identifiers.
Why it's wrong here
Dynamic partition overwrites clear existing partition data entirely, which can lead to data loss or performance degradation if the source data payload is incomplete or if concurrent read-write operations collide within the target Lakehouse table structure.
- ✓
Read only the newly arrived files or records using streaming queries or explicit watermarking filters based on ingestion timestamps.
Why this is correct
Filtering source data using watermarks or structured streaming sources ensures that only delta changes are processed. This minimizes compute resource consumption and accelerates transformation pipelines by avoiding full table scans of historical bronze data.
- ✗
Disable Delta Lake transaction logs during the write phase to accelerate the throughput of incremental insert operations.
Why it's wrong here
Disabling transaction logs compromises ACID compliance, data consistency, and time-travel capabilities. Delta tables rely entirely on transaction logs to manage concurrent reads and writes safely, making this configuration structurally dangerous for production architectures.
- ✗
Cache the entire bronze table into the Spark driver memory before executing the join and transformation logic.
Why it's wrong here
Caching large bronze tables into memory introduces severe Out-Of-Memory risks as data volumes scale. Incremental processing patterns are designed specifically to avoid loading entire datasets into memory by leveraging lazy evaluation and distributed execution.
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.