Databricks Certified Data Engineer - Associate

Explanation

Option E is the correct answer because it represents 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) to read streaming data from raw files
2. Streaming write - Uses .writeStream to write as a streaming job
3. Checkpointing - Includes .option("checkpointLocation", checkpointPath) for fault tolerance
4. Append mode - Uses .outputMode("append") which is appropriate for streaming writes to Bronze 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 (likely a Bronze or Silver table), not from raw data. They're performing transformations on existing tables, not the initial ingestion from raw data.

Option D: This uses batch processing (spark.read.load) not streaming, and doesn't include checkpointing, which is essential for streaming jobs.

Bronze Table Context:

In Databricks medallion architecture:

• Bronze tables store raw, unprocessed data as it arrives from source systems
• Streaming hops to Bronze tables typically involve reading from raw data sources (files, Kafka, etc.) and writing to Delta tables with minimal or no transformations
• The checkpoint location ensures exactly-once processing semantics and fault tolerance

Option E correctly demonstrates the pattern for streaming ingestion from raw data files into a Bronze table.