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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:

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

1. A data engineer wants to create a Streaming DataFrame that reads from a Kafka topic called feed.

Which code fragment should be inserted in line 5 to meet the requirement?
Code context:
spark \
.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers", "host1:port1,host2:port2") \
.[LINE 5] \
.load()
Options:

A) .option("topic", "feed")
B) .option("kafka.topic", "feed")
C) .option("subscribe", "feed")
D) .option("subscribe.topic", "feed")

2. Which command overwrites an existing JSON file when writing a DataFrame?

A) df.write.format("json").save("path/to/file", mode="overwrite")
B) df.write.json("path/to/file", overwrite=True)
C) df.write.mode("overwrite").json("path/to/file")
D) df.write.overwrite.json("path/to/file")

3. 20 of 55.
What is the difference between df.cache() and df.persist() in Spark DataFrame?

A) persist() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and cache() - Can be used to set different storage levels.
B) cache() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_DESER), and persist() - Can be used to set different storage levels to persist the contents of the DataFrame.
C) Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_DESER).
D) Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.

4. 34 of 55.
A data engineer is investigating a Spark cluster that is experiencing underutilization during scheduled batch jobs.
After checking the Spark logs, they noticed that tasks are often getting killed due to timeout errors, and there are several warnings about insufficient resources in the logs.
Which action should the engineer take to resolve the underutilization issue?

A) Increase the number of executor instances to handle more concurrent tasks.
B) Increase the executor memory allocation in the Spark configuration.
C) Reduce the size of the data partitions to improve task scheduling.
D) Set the spark.network.timeout property to allow tasks more time to complete without being killed.

5. 6 of 55.
Which components of Apache Spark's Architecture are responsible for carrying out tasks when assigned to them?

A) CPU Cores
B) Driver Nodes
C) Executors
D) Worker Nodes

Solutions: