Pass Using Databricks-Certified-Professional-Data-Engineer Exam Dumps

Exact extract: “If no trigger is specified, the default processing-time trigger runs micro-batches as fast as possible.”

Exact extract: “Trigger once processes all available data once and then stops.”

This is the correct answer because it characterizes the general programming model used by Spark Structured Streaming, which is to treat a live data stream as a table that is being continuously appended. This leads to a new stream processing model that is very similar to a batch processing model, where users can express their streaming computation using the same Dataset/DataFrame API as they would use for static data. The Spark SQL engine will take care of running the streaming query incrementally and continuously and updating the final result as streaming data continues to arrive. Verified References: [Databricks Certified Data Engineer Professional], under “Structured Streaming” section;  Databricks Documentation , under “Overview” section.

With schema evolution enabled in Databricks Delta tables, when a new field is added to a record through a MERGE operation, Databricks automatically modifies the table schema to include the new field. In existing records where this new field is not present, Databricks will insert NULL values for that field. This ensures that the schema remains consistent across all records in the table, with the new field being present in every record, even if it is NULL for records that did not originally include it.

Lakeflow Declarative Pipelines (LDP), formerly Delta Live Tables (DLT), supports enforcing data quality using expectations . Expectations can either:

Track violations (expect) → records that do not meet conditions are flagged but still included in the pipeline.

Drop violations (expect_or_drop) → records that do not meet conditions are excluded from downstream tables.

Fail pipeline on violations (expect_or_fail) → records that fail conditions stop the pipeline.

In this scenario, the requirement explicitly states that invalid records (where customer_id is null or amount ≤ 0) must be dropped . According to the official documentation, the correct method is .expect_or_drop( " expectation_name " , " SQL_predicate " ) applied on the streaming input.

Option A is correct: It uses .expect_or_drop directly within the transformation chain for both rules, ensuring records that fail are removed before writing to the silver table.

Option B incorrectly uses @dlt.expect decorators, which only track violations but do not drop invalid rows.

Option C uses .expect, which also only flags rows, not drop them.

Option D uses @dlt.expect_or_drop decorator syntax, which is not supported in Python API; expect_or_drop must be applied as a method on the DataFrame, not as a decorator.

Therefore, the correct solution is Option A , which ensures compliance by enforcing data quality and dropping invalid rows programmatically during ingestion.