Mock Interview – Databricks Engineer Role

[vithobha]

Here’s a scenario: “Optimize a Delta Lake pipeline with late-arriving data.” How would you approach it?

[David]

I’d use merge (upsert) with partition pruning. Also enable ZORDER for efficient querying.

• This reply was modified 3 weeks ago by David.

[Wills]

Don’t forget to vacuum and optimize the Delta table regularly to maintain performance.

[Meera]

Here’s a mock interview question I was recently asked:

“How does Delta Lake handle ACID transactions, and what are some scenarios where you would recommend using Delta over traditional Parquet tables?”

My answer was focused on the transaction log (_delta_log), schema enforcement, and time travel features.
I also mentioned its advantages in streaming + batch workflows.

[Meera]

Here are a another questions that came up during my interviews:

“Tell us about a time you had to debug a failing Spark job in production. How did you approach it?”

In my answer, I followed the STAR format:

Situation: Daily ETL job was failing intermittently

Task: Identify root cause and fix without data loss

Action: Checked job logs and Spark UI, traced a data skew issue due to a join on a non-partitioned column

[Arjun]

Can somebody answer this..
Design a scalable data pipeline in Databricks that ingests streaming data from IoT devices, processes it in real time, and stores the results in a Delta Lake table.

[Author]

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