Databricks-DE-Assoc Data Transformation and Modeling Practice Question
A data engineer is using Delta Live Tables to build a pipeline. They need to create a table that contains the latest record for each customer based on a `last_updated` timestamp. The source is a streaming table with append-only data. Which Delta Live Tables operation should be used to achieve this?
⚠ Common exam trap
The trap here is assuming that a simple streaming aggregation or window function can maintain the latest record per key. In Delta Live Tables, APPLY CHANGES INTO is the purpose-built operation for this pattern.
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 APPLY CHANGES INTO operation with a streaming source and specify customer_id as the key and last_updated as the sequencing column.
The correct operation is APPLY CHANGES INTO, which is designed for change data capture and deduplication. By specifying the key and sequencing column, Delta Live Tables automatically retains the latest record for each customer. Other approaches either do not support streaming updates or require inefficient full reprocessing.
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 with a GROUP BY customer_id and MAX(last_updated) to get the latest timestamp, then join back to the source.
Why it's wrong here
Aggregations like GROUP BY on a streaming table require watermarks and are not supported for arbitrary updates. Even if it worked, it would only give the latest timestamp, not the entire latest record. Joining back would be complex and might not handle out-of-order data correctly. This approach is not suitable for maintaining a latest-state table.
- ✗
Use a batch live table that reads the entire source and uses a MERGE statement to upsert records.
Why it's wrong here
A batch live table would reprocess the entire source on each run, which is inefficient for streaming data. While a MERGE could achieve the latest state, Delta Live Tables does not support MERGE directly in batch live tables; it requires using APPLY CHANGES. This approach would not leverage the streaming nature and would be less scalable.
- ✗
Use a streaming live table with a window function to rank records by last_updated and filter for rank 1.
Why it's wrong here
Window functions in a streaming live table require event-time watermarks and are limited to certain operations. While you could use windowing, it would not guarantee the latest record per customer because streaming windows are based on event time and may not capture all updates. Also, streaming tables are append-only and do not support deduplication natively in this manner.
- ✓
Use the APPLY CHANGES INTO operation with a streaming source and specify customer_id as the key and last_updated as the sequencing column.
Why this is correct
APPLY CHANGES INTO (formerly Delta Live Tables CDC) is designed to handle updates and deduplication from a streaming source. By specifying customer_id as the key and last_updated as the sequence column, Delta Live Tables will automatically keep the latest record for each customer based on the timestamp. This is the correct and efficient way to maintain a latest-state table.
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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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