Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Exam Dumps : Databricks Certified Associate Developer for Apache Spark 3.5 – Python

Exactly-once guarantees in Spark Structured Streaming require micro-batch mode (default), not continuous mode.

Continuous mode (.trigger(continuous=...)) only supports at-least-once semantics and lacks full fault-tolerance.

trigger(availableNow=True) is a batch-style trigger, not suited for low-latency streaming.

So:

Option A uses micro-batching with a tight trigger interval → minimal latency + exactly-once guarantee.

Final Answer: A

To handle JSON files with evolving or differing schemas, Spark SQL supports the option mergeSchema 'true', which merges all fields across files into a unified schema.

Correct syntax:

CREATE EXTERNAL TABLE users

USING json

OPTIONS (path '/data/input.json', mergeSchema 'true');

This creates an external table directly on the JSON data, inferring schema automatically and merging variations.

Why the other options are incorrect:

B: Missing schema merge configuration — fails with inconsistent files.

C: inferSchema applies to CSV/other file types, not JSON.

D: mergeAll is not a valid Spark SQL option.

According to Spark’s watermarking rules:

“Records that are older than the watermark (event time < current watermark) are considered too late and are dropped.”

So, if a record’s event_time is earlier than (max event_time seen so far - 10 minutes), it is discarded.