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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

A data engineer is designing a pipeline in Databricks that processes records from a Kafka stream where late-arriving data is common. Which approach should the data engineer use?

A. Implement a custom solution using Databricks Jobs to periodically reprocess all historical data.
B. Use batch processing and overwrite the entire output table each time to ensure late data is incorporated correctly.
C. Use a watermark to specify the allowed lateness to accommodate records that arrive after their expected window, ensuring correct aggregation and state management.
D. Use an Auto CDC pipeline with batch tables to simplify late data handling.

Question 2

A data engineer is building a Lakeflow Declarative Pipelines pipeline to process healthcare claims data. A metadata JSON file defines data quality rules for multiple tables, including:
{
"claims": [
{"name": "valid_patient_id", "constraint": "patient_id IS NOT NULL"},
{"name": "non_negative_amount", "constraint": "claim_amount >= 0"}
]
}
The pipeline must dynamically apply these rules to the claims table without hardcoding the rules.
How should the data engineer achieve this?

A. Load the JSON metadata, loop through its entries, and apply expectations using dlt.expect_all.
B. Reference each expectation with @dlt.expect decorators in the table declaration.
C. Use a SQL CONSTRAINT block referencing the JSON file path.
D. Invoke an external API to validate records against the metadata rules.

Question 3

A Structured Streaming job deployed to production has been experiencing delays during peak hours of the day. At present, during normal execution, each microbatch of data is processed in less than 3 seconds. During peak hours of the day, execution time for each microbatch becomes very inconsistent, sometimes exceeding 30 seconds. The streaming write is currently configured with a trigger interval of 10 seconds.
Holding all other variables constant and assuming records need to be processed in less than 10 seconds, which adjustment will meet the requirement?

A. Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.
B. The trigger interval cannot be modified without modifying the checkpoint directory; to maintain the current stream state, increase the number of shuffle partitions to maximize parallelism.
C. Decrease the trigger interval to 5 seconds; triggering batches more frequently allows idle executors to begin processing the next batch while longer running tasks from previous batches finish.
D. Increase the trigger interval to 30 seconds; setting the trigger interval near the maximum execution time observed for each batch is always best practice to ensure no records are dropped.
E. Use the trigger once option and configure a Databricks job to execute the query every 10 seconds; this ensures all backlogged records are processed with each batch.

Question 4

Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

A. spark.sql.autoBroadcastJoinThreshold
B. spark.sql.adaptive.advisoryPartitionSizeInBytes
C. spark.sql.files.maxPartitionBytes
D. spark.sql.files.openCostInBytes
E. spark.sql.adaptive.coalescePartitions.minPartitionNum

Question 5

A view is registered with the following code:

Both users and orders are Delta Lake tables.
Which statement describes the results of querying recent_orders?

A. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
B. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
C. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
D. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.

Solutions: