Databricks-DE-Pro Developing Code (Python/SQL) Practice Question
A data engineer is using Delta Live Tables (DLT) to create a pipeline that processes streaming data. They need to ensure that the pipeline only processes new data since the last run and that the pipeline can recover from failures without reprocessing all data. Which combination of features should they use to achieve this?
⚠ Common exam trap
The trap here is thinking that you need to manually specify a checkpoint location for DLT streaming tables, when DLT handles checkpointing automatically.
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 streaming live table and rely on DLT's automatic checkpointing and state management.
DLT streaming live tables automatically manage checkpoints and state, ensuring that only new data is processed and that the pipeline can recover from failures without reprocessing all data. This is a core feature of DLT. The other options either incorrectly rely on manual checkpointing, use batch constructs that don't support incremental processing, or misunderstand the capabilities of materialized views.
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 a streaming live table and rely on DLT's automatic checkpointing and state management.
Why this is correct
DLT automatically manages checkpoints and state for streaming live tables. It tracks the progress of the stream and ensures that only new data is processed. On failure, DLT can recover from the last checkpoint without reprocessing all data. This is the correct approach because DLT handles the complexity of checkpointing and state management internally.
- ✗
Use a streaming live table with a checkpoint location specified in the pipeline configuration.
Why it's wrong here
DLT automatically manages checkpoints for streaming tables; specifying a checkpoint location manually is not required and may not be supported in all DLT configurations. DLT uses its own managed checkpoints to track progress. Therefore, this approach is not the correct way to ensure incremental processing and recovery in DLT.
- ✗
Use a materialized view with a trigger to process only new data based on a timestamp column.
Why it's wrong here
Materialized views in DLT are for batch processing and do not inherently support incremental processing of streaming data. While triggers can schedule updates, they do not provide the same checkpointing and exactly-once semantics as streaming tables. This approach would not guarantee processing only new data or efficient recovery.
- ✗
Use a batch live table with a filter on the ingestion timestamp to process only new records.
Why it's wrong here
Batch live tables process all data on each run unless incremental patterns are used. Filtering on ingestion timestamp might reduce data but does not provide checkpointing or state management. It would reprocess all data each time, which is inefficient and not the intended solution for incremental streaming.
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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 Databricks exam blueprint
This Databricks-DE-Pro practice question is part of Courseiva's free Databricks 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 Databricks-DE-Pro exam.