LFC Delta Live Tables 3

Question 1

Which DLT interface provides ongoing visibility into data quality metrics such as records passed, dropped, or failed?

Accepted Answer

Data Quality tab and Event Log in the pipeline UI

Answer

The pipeline UI's Data Quality tab and the underlying Event Log Delta table record expectation metrics per update, showing passed, failed, and dropped counts.

Question 2

In DLT Python, how do you correctly define a streaming live table?

Accepted Answer

@dlt.table decorator with a function body that uses spark.readStream

Answer

A streaming live table is created with @dlt.table whose function body reads from a source using spark.readStream, signaling DLT to treat it as a streaming dataset.

Question 3

What is the DLT pipeline Event Log?

Accepted Answer

A Delta table that records all pipeline events including quality metrics, statuses, and errors

Answer

The Event Log is a queryable Delta table storing every pipeline event — including update status, data quality results, and any errors — providing a full audit trail.

Question 4

How does DLT handle schema evolution in source data by default?

Accepted Answer

It automatically adapts to additive schema changes in the source data

Answer

DLT supports automatic schema evolution, allowing it to detect and adapt to additive changes in source schemas without manual intervention.

Question 5

Which description best matches the Medallion Architecture as implemented with Delta Live Tables?

Accepted Answer

A three-tier pattern with Bronze (raw), Silver (validated/cleaned), and Gold (aggregated/business-ready) layers

Answer

The Medallion Architecture layers data into Bronze (raw ingestion), Silver (cleaned and validated), and Gold (business-level aggregates), each typically implemented as DLT tables.

(LFC) Lakehouse Fundamentals Certification Practice Test

(LFC) Lakehouse Fundamentals Certification Practice Test

LFC Delta Live Tables 3