DP-203 Develop data processing Practice Question
You are developing an Azure Databricks notebook to process streaming data from Azure Event Hubs. The notebook must write the processed data to a Delta table with exactly-once processing guarantees. You need to configure the write operation. Which option should you use?
⚠ Common exam trap
Test-takers frequently confuse at-least-once with exactly-once, and assuming any streaming sink with a checkpoint provides exactly-once guarantees.
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
✓
Write the stream to a Delta table using the `writeStream` method with `format("delta")` and a checkpoint location.
Structured streaming to Delta Lake with `writeStream` and a checkpoint location provides exactly-once processing because Delta's transaction log and the checkpoint work together to ensure each record is processed once. Other formats like Parquet lack transactional guarantees, and batch writes cannot handle streaming data. Writing to SQL via JDBC also lacks built-in exactly-once semantics.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Write the stream to a Delta table using the `writeStream` method with `format("delta")` and a checkpoint location.
Why this is correct
Using `writeStream` with Delta format and a checkpoint location enables structured streaming with exactly-once semantics. Delta Lake's transaction log ensures idempotent writes, and the checkpoint tracks progress to avoid reprocessing. This is the correct approach for streaming ingestion into Delta tables with exactly-once guarantees.
- ✗
Write the stream to a Parquet file using `writeStream` with `format("parquet")` and a checkpoint location.
Why it's wrong here
Parquet does not provide ACID transactions or exactly-once guarantees. While `writeStream` with a checkpoint can provide at-least-once semantics, it cannot guarantee exactly-once because there is no transactional log to deduplicate writes. This option fails the requirement for exactly-once processing when writing to a Delta table.
- ✗
Write the stream to an Azure SQL Database using `writeStream` with `format("jdbc")` and a checkpoint location.
Why it's wrong here
Writing to Azure SQL Database via JDBC does not provide exactly-once guarantees by default. It may result in duplicate rows if a failure occurs after writing but before checkpointing. Additionally, the requirement specifies writing to a Delta table, not a SQL database, so this option does not meet the target storage requirement.
- ✗
Use `write` instead of `writeStream` to write the DataFrame to a Delta table.
Why it's wrong here
The `write` method is for batch writes, not streaming. Using it on a streaming DataFrame will fail because streaming DataFrames require `writeStream`. Even if the DataFrame were static, `write` does not provide the checkpointing and continuous processing needed for streaming ingestion, so it cannot meet the exactly-once requirement.
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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
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