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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:

1. 3 of 55. A data engineer observes that the upstream streaming source feeds the event table frequently and sends duplicate records. Upon analyzing the current production table, the data engineer found that the time difference in the event_timestamp column of the duplicate records is, at most, 30 minutes.
To remove the duplicates, the engineer adds the code:
df = df.withWatermark("event_timestamp", "30 minutes")
What is the result?

A) It removes all duplicates regardless of when they arrive.
B) It is not able to handle deduplication in this scenario.
C) It removes duplicates that arrive within the 30-minute window specified by the watermark.
D) It accepts watermarks in seconds and the code results in an error.

2. Given:
python
CopyEdit
spark.sparkContext.setLogLevel("<LOG_LEVEL>")
Which set contains the suitable configuration settings for Spark driver LOG_LEVELs?

A) ERROR, WARN, TRACE, OFF
B) ALL, DEBUG, FAIL, INFO
C) WARN, NONE, ERROR, FATAL
D) FATAL, NONE, INFO, DEBUG

3. A developer wants to test Spark Connect with an existing Spark application.
What are the two alternative ways the developer can start a local Spark Connect server without changing their existing application code? (Choose 2 answers)

A) Set the environment variable SPARK_REMOTE="sc://localhost" before starting the pyspark shell
B) Ensure the Spark property spark.connect.grpc.binding.port is set to 15002 in the application code
C) Execute their pyspark shell with the option --remote "sc://localhost"
D) Add .remote("sc://localhost") to their SparkSession.builder calls in their Spark code
E) Execute their pyspark shell with the option --remote "https://localhost"

4. 11 of 55.
Which Spark configuration controls the number of tasks that can run in parallel on an executor?

A) spark.executor.cores
B) spark.sql.shuffle.partitions
C) spark.executor.memory
D) spark.task.maxFailures

5. 16 of 55.
A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior? (Choose 2 answers)

A) The Spark engine optimizes the execution plan during the transformations, causing delays.
B) Transformations are evaluated lazily.
C) Transformations are executed immediately to build the lineage graph.
D) The Spark engine requires manual intervention to start executing transformations.
E) Only actions trigger the execution of the transformation pipeline.

Solutions: