Databricks Certified Data Engineer - Associate

Explanation

In the Databricks Lakehouse architecture, data typically flows through three layers:

1. Bronze layer: Contains raw, unprocessed data
2. Silver layer: Contains cleaned, validated, and enriched data
3. Gold layer: Contains aggregated, business-ready data for consumption

Gold tables are the final, refined layer where data is:

• Aggregated for business intelligence
• Optimized for query performance
• Structured for specific business use cases
• Ready for consumption by dashboards, reports, and analytics tools

Let's analyze each option:

A. A job that enriches data by parsing its timestamps into a human-readable format

• This is typically a Silver layer operation where raw data is cleaned and enriched
• Gold tables would already have properly formatted timestamps

B. A job that aggregates uncleaned data to create standard summary statistics

• This would work with Bronze or Silver data, not Gold
• Gold tables should already contain cleaned, aggregated data

C. A job that cleans data by removing malformed records

• This is a Silver layer operation (data cleaning)
• Gold tables should already have clean data

D. A job that queries aggregated data designed to feed into a dashboard ✓

• CORRECT: Gold tables contain aggregated, business-ready data specifically designed for consumption by dashboards and reports
• Dashboards typically query from Gold tables for performance and data quality reasons

E. A job that ingests raw data from a streaming source into the Lakehouse

• This is a Bronze layer operation (raw data ingestion)
• Gold tables are downstream from ingestion processes

Key Concept: Gold tables represent the final, business-ready layer where data is aggregated and optimized for consumption. Jobs that feed dashboards, reports, or business analytics applications typically use Gold tables as their source because they contain the refined, aggregated data that business users need.