DP-203 Develop data processing Practice Question
You are implementing a medallion architecture in Azure Databricks. The silver layer must contain deduplicated, conformed records, and the gold layer must serve aggregated reporting tables. You need to choose Delta Lake operations that support incremental, idempotent updates as new bronze files arrive. Which two operations should you use? (Choose two.)
⚠ Common exam trap
The trap here is equating file compaction or full-table rebuilds with idempotent incremental processing, when only keyed upserts provide that guarantee.
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
✓
Stream bronze files into the silver table with Structured Streaming and foreachBatch applying MERGE
Incremental, idempotent updates in a medallion pipeline rely on keyed upserts rather than full rebuilds. MERGE INTO delivers atomic, key-based convergence, and Structured Streaming with foreachBatch applies that same MERGE per micro-batch while checkpointing progress. Together they let new bronze files flow into silver without duplication, and the gold aggregates can then be computed from the conformed silver data.
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 CREATE OR REPLACE TABLE AS SELECT to rebuild the silver table from all bronze files each run
Why it's wrong here
Rebuilding the entire table each run is idempotent but not incremental; it reprocesses every historical file and scales poorly as bronze grows. It also discards any silver-only enrichment that is not reproducible from bronze. The scenario explicitly requires incremental processing, so a full rebuild contradicts the stated goal even though it yields a consistent result.
- ✓
Stream bronze files into the silver table with Structured Streaming and foreachBatch applying MERGE
Why this is correct
Structured Streaming with foreachBatch lets each micro-batch run a MERGE against the silver table, combining incremental ingestion with idempotent upserts keyed on a business key. Checkpointing tracks processed offsets so already-consumed files are not reprocessed, and the MERGE guarantees that any replay converges to the same state. This matches the incremental, idempotent requirement precisely.
- ✗
Write the gold aggregates with overwrite mode partitioned by report date
Why it's wrong here
Overwriting partitions replaces their contents atomically, which can be idempotent for a given partition, but it discards incremental history and is not suitable for the silver layer's deduplication requirement. For gold reporting tables it is a reasonable pattern, yet the scenario asks for operations that support incremental, idempotent updates as bronze files arrive, which overwrite does not provide for the conformed layer.
- ✗
OPTIMIZE the silver table with ZORDER BY the business key after each load
Why it's wrong here
OPTIMIZE with ZORDER compacts small files and co-locates related rows to speed up reads, but it does not deduplicate or apply updates. Running it after each load improves query performance and file layout, yet it cannot make an incremental load idempotent. It is a maintenance operation, not a mechanism for conforming and deduplicating incoming records.
- ✓
MERGE INTO the silver table using the bronze staging data matched on a business key
Why this is correct
MERGE INTO applies inserts, updates, and deletes in a single atomic transaction keyed on a business key. Re-running the same batch against the same key updates existing rows rather than duplicating them, which is exactly the idempotent behavior needed when bronze files may be reprocessed. This makes the silver layer converge to a deduplicated, conformed state regardless of how many times the batch is applied.
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Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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