DP-750 Certification Practice Question #49

Question

You are authoring a notebook-based Spark Structured Streaming job that incrementally reads from a Delta source table, enriches each micro-batch, and writes the result to a Unity Catalog Delta table with exactly-once, fault-tolerant guarantees. Arrange the code building blocks into the correct execution order to form a single valid streaming query.

Drag each tile into the correct slot in the pipeline sequence.

```mermaid
flowchart LR
    S1["Slot 1"] --> S2["Slot 2"] --> S3["Slot 3"] --> S4["Slot 4"] --> S5["Slot 5"]

subgraph TILES["Available tiles (unordered)"]
        T1["Tile: .writeStream"]
        T2["Tile: spark.readStream.table('main.bronze.events')"]
        T3["Tile: .option('checkpointLocation', '/Volumes/main/raw/_chk/events')"]
        T4["Tile: .withColumn('ingest_ts', current_timestamp())"]
        T5["Tile: .toTable('main.silver.events')"]
    end
```

Accepted Answer

A Structured Streaming query begins with a streaming read (`spark.readStream.table(...)`), applies transformations on the streaming DataFrame (`.withColumn(...)`), transitions to a `DataStreamWriter` with `.writeStream`, configures the durable `checkpointLocation` that provides the query's identity and exactly-once/fault-tolerance guarantees, and finally starts the query with the terminal `.toTable(...)` sink. The checkpoint option must be set on the writer chain before the terminal action; the Delta `toTable` sink then guarantees exactly-once writes via the transaction log.

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