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

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

Which statement describes the correct use of pyspark.sql.functions.broadcast?

A. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
B. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
C. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.
D. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
E. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.

Question 2

A data engineer is designing an append-only pipeline that needs to handle both batch and streaming data in Delta Lake. The team wants to ensure that the streaming component can efficiently track which data has already been processed. Which configuration should be set to enable this?

A. overwriteSchema
B. mergeSchema
C. checkpointLocation
D. partitionBy

Question 3

A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

A. The Delta log is scanned for min and max statistics for the latitude column
B. All records are cached to an operational database and then the filter is applied
C. The Parquet file footers are scanned for min and max statistics for the latitude column
D. The Hive metastore is scanned for min and max statistics for the latitude column
E. All records are cached to attached storage and then the filter is applied

Question 4

A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays.
Which limitation will the team face while diagnosing this problem?

A. Updating the table schema requires a default value provided for each file added.
B. New fields will not be computed for historic records.
C. New fields cannot be added to a production Delta table.
D. Updating the table schema will invalidate the Delta transaction log metadata.
E. Spark cannot capture the topic partition fields from the kafka source.

Question 5

Which statement characterizes the general programming model used by Spark Structured Streaming?

A. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
B. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.
C. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
D. Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.
E. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.

Solutions: