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

Pipeline Lineage Graph

Answer

Cluster Metrics Dashboard

Answer

SQL Query History panel

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

@dlt.table(streaming=True)

Answer

@dlt.stream_table decorator

Answer

spark.read with outputMode='append'

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

A raw storage bucket for input data audit

Answer

A configuration file for pipeline alerting rules

Answer

An automatic schema inference log

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

It rejects schema changes and fails the pipeline immediately

Answer

It requires manual schema migration scripts before the pipeline can continue

Answer

It converts all new or changed columns to string type

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

A security model defining three levels of data access control

Answer

A storage format offering three levels of data compression

Answer

A cluster topology with three distinct node type categories

Question 6

What mechanism does DLT use to track which data has already been processed in a streaming pipeline?

Accepted Answer

Checkpoint files stored in cloud storage

Answer

Watermarks stored in the pipeline metadata database

Answer

A separate external tracking database

Answer

Internal state stored only in the pipeline event log

Question 7

When is a DLT LIVE VIEW preferable to a LIVE TABLE?

Accepted Answer

When you want a reusable intermediate transformation that does not need its own persistent storage

Answer

When you need to persist aggregated results for downstream BI queries

Answer

When you need to ingest streaming data from Apache Kafka

Answer

When you need to apply a machine learning model inside the pipeline