Databricks-GenAI-Assoc Application Development Practice Question
A developer is building a retrieval-augmented generation (RAG) application on Databricks. They need to ensure that embeddings are updated automatically when the underlying Delta table changes. Which approach is the most efficient and scalable?
⚠ Common exam trap
Candidates often suggest manual batch jobs or triggers, which are less efficient and harder to scale than Delta Live Tables' native streaming capabilities for continuous data processing.
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
✓
Implement a Delta Live Tables pipeline using streaming tables and a Python UDF to generate embeddings on arrival.
Delta Live Tables (DLT) with streaming tables allows for continuous data processing and incremental updates. By integrating embedding generation directly into the pipeline, the developer ensures that the vector database stays in sync with the source data without manual intervention or complex scheduling. This architecture minimizes latency and improves data consistency, which is a foundational requirement for production-grade RAG applications within the Databricks ecosystem.
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 a manual PySpark job that runs every hour to scan the entire Delta table and recompute all embeddings.
Why it's wrong here
Scanning the entire table every hour is computationally expensive and inefficient for large datasets. This approach increases cloud costs significantly and introduces unnecessary latency between data updates and embedding availability, making it unsuitable for production environments where timely information retrieval is critical for accurate LLM responses.
- ✗
Use a Databricks Job with a notebook task triggered by a cron schedule to process changes using a watermark.
Why it's wrong here
While this method uses watermarks, it requires manual management of state and scheduling. Databricks Jobs are less integrated than DLT for this specific use case, as they lack the built-in declarative syntax for maintaining stateful streaming pipelines, making the maintenance overhead higher compared to using native streaming tables.
- ✓
Implement a Delta Live Tables pipeline using streaming tables and a Python UDF to generate embeddings on arrival.
Why this is correct
Delta Live Tables streaming tables automatically handle incremental data ingestion and state management. By applying a transformation function during the stream, the pipeline computes embeddings only for new or updated records, significantly reducing compute overhead and ensuring the vector index is always current without manual maintenance or scheduling logic.
- ✗
Deploy a Unity Catalog volume to store the embeddings and trigger an external API call from an event-driven function.
Why it's wrong here
Using an external event-driven function introduces complexity regarding authentication, latency, and consistency. Managing state and synchronization between the Delta table and an external vector database becomes difficult, as Databricks native integration provides more robust mechanisms for handling data lineage and automated processing compared to external event-based triggers.
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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
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