Databricks Certified Data Engineer - Associate

Explanation

Option E is the correct answer because it demonstrates a streaming hop from raw data to a Bronze table. Here's why:

Key Characteristics of a Streaming Hop:

1. Reads from raw data source - Uses spark.readStream.load(rawSalesLocation) which reads streaming data from a raw location
2. Writes to a table - Uses .table("newSales") to write to a Bronze table
3. Uses streaming API - Both readStream and writeStream indicate streaming processing
4. Includes checkpointing - Has .option("checkpointLocation", checkpointPath) for fault tolerance
5. Append mode - Uses .outputMode("append") which is typical for streaming writes to tables

Why Other Options Are Incorrect:

Option A, B, C: These all read from spark.table("sales") which means they're reading from an existing table (not raw data), and performing transformations before writing. This represents a transformation hop rather than a raw-to-bronze hop.

Option D: This uses batch processing (spark.read.load and .write) rather than streaming, so it's not a streaming hop.

Bronze Table Concept:

In the Databricks Lakehouse architecture:

• Raw/Bronze layer: Contains raw, unprocessed data from source systems
• Silver layer: Contains cleaned, validated, and enriched data
• Gold layer: Contains aggregated, business-ready data

A streaming hop from raw data to Bronze typically involves reading streaming data from sources (like Kafka, Event Hubs, files) and writing it directly to Bronze tables with minimal transformation, which is exactly what Option E demonstrates.