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

1. 39 of 55.
A Spark developer is developing a Spark application to monitor task performance across a cluster.
One requirement is to track the maximum processing time for tasks on each worker node and consolidate this information on the driver for further analysis.
Which technique should the developer use?

A) Broadcast a variable to share the maximum time among workers.
B) Configure the Spark UI to automatically collect maximum times.
C) Use an RDD action like reduce() to compute the maximum time.
D) Use an accumulator to record the maximum time on the driver.

2. A data engineer needs to write a DataFrame df to a Parquet file, partitioned by the column country, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?

A) df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
B) df.write.mode("overwrite").parquet("/data/output")
C) df.write.partitionBy("country").parquet("/data/output")
D) df.write.mode("append").partitionBy("country").parquet("/data/output")

3. 45 of 55.
Which feature of Spark Connect should be considered when designing an application that plans to enable remote interaction with a Spark cluster?

A) It is primarily used for data ingestion into Spark from external sources.
B) It provides a way to run Spark applications remotely in any programming language.
C) It can be used to interact with any remote cluster using the REST API.
D) It allows for remote execution of Spark jobs.

4. 44 of 55.
A data engineer is working on a real-time analytics pipeline using Spark Structured Streaming.
They want the system to process incoming data in micro-batches at a fixed interval of 5 seconds.
Which code snippet fulfills this requirement?

A) query = df.writeStream \
.outputMode("append") \
.start()
B) query = df.writeStream \
.outputMode("append") \
.trigger(continuous="5 seconds") \
.start()
C) query = df.writeStream \
.outputMode("append") \
.trigger(processingTime="5 seconds") \
.start()
D) query = df.writeStream \
.outputMode("append") \
.trigger(once=True) \
.start()

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

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

Solutions: