Databricks Certified Data Engineer - Associate

Explanation

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

1. Bronze: Raw, untransformed data
2. Silver: Cleaned, processed, and enriched data
3. Gold: Aggregated, business-ready data for analytics

Analyzing each option:

• Option A & B: These perform aggregation operations (groupBy and agg) which are more typical of Silver-to-Gold transformations (aggregating cleaned data for analytics). They don't show data cleaning or transformation from raw format.

• Option C: This query performs a data transformation by computing avgPrice (dividing sales by units), which represents data cleaning and enrichment. The output table name "cleanedSales" clearly indicates it's creating a Silver-level table from potentially raw data. The append output mode suggests incremental processing of cleaned data.

• Option D: This reads from a raw location and writes to "uncleanedSales", suggesting it's still in Bronze layer (raw data).

• Option E: Incomplete syntax, not a valid query.

Key indicators of Bronze-to-Silver transformation:

1. Data cleaning operations (null handling, type conversions)
2. Deriving new calculated columns
3. Filtering out invalid records
4. Standardizing formats
5. Output table names suggesting cleaned/processed data

Option C demonstrates all these characteristics with the withColumn transformation and the "cleanedSales" output table name.