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Interview/Databricks

Delta Live Tables (Lakeflow) interview questions

Interactive Databricks interview questions on Delta Live Tables (Lakeflow). Same topic as /learn/databricks/dlt. Declare bronze/silver/gold tables and expectations. DLT infers the graph, checkpoints each table, and stops downstream on failure.

Lesson · Simulation

beginner
Delta Live Tables (Lakeflow)

What is Delta Live Tables (Lakeflow Declarative Pipelines)?

Answer it out loud, then reveal. Play steps through like the simulators.

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Production scenario

Delta Live Tables (Lakeflow)

Pipeline green, 18% of ids missing in gold

Symptoms

  • expect_or_drop on invalid ids
  • Nobody watched the metric
Open lessonSee it in the simulator

All questions on this page

Indexed as FAQ. Open any item if you prefer a list to Play.

beginner

What is Delta Live Tables (Lakeflow Declarative Pipelines)?

Delta Live Tables (Lakeflow) · tap to open the answer

Short: You declare tables and expectations; DLT infers the graph and runs in order.

Detailed: dlt.read / @dlt.table instead of a 12-task Workflow of notebooks. Expectations drop or fail bad rows. Still Delta under the hood.

Common mistake: DLT as a replacement for Spark.

Follow-up: How does DLT know bronze runs before gold?

Lesson · Simulation

intermediate

Expectations vs a filter in a notebook job — why bother?

Delta Live Tables (Lakeflow) · tap to open the answer

Short: Expectations are cataloged, metrics are visible, and bad rows can fail the pipeline.

Detailed: A silent filter hides data quality. DLT event log shows expectation pass rate. That's what on-call wants at 3am.

Common mistake: expect_or_drop everything so the pipeline never fails.

Follow-up: When is a Workflow of notebooks still better than DLT?

Lesson · Simulation

senior

DLT pipeline is green but gold is wrong. Where do you look?

Delta Live Tables (Lakeflow) · tap to open the answer

Short: Expectations that drop rows, a wrong grain in silver, or a live table reading a stale source snapshot.

Detailed: Event log + UC lineage. Don't start in Spark UI Stages — DLT may have many datasets. Compare bronze counts vs gold. Check if development vs production mode used different data.

Senior: Full refresh on a sampled bronze, expect metrics, then prod. Never full-refresh prod as a test.

Common mistake: Scaling the DLT cluster first.

Follow-up: How do you test a DLT graph locally-ish?

Lesson · Simulation