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

2. 49 of 55.
In the code block below, aggDF contains aggregations on a streaming DataFrame:
aggDF.writeStream \
.format("console") \
.outputMode("???") \
.start()
Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?

A) APPEND
B) AGGREGATE
C) REPLACE
D) COMPLETE

3. 23 of 55.
A data scientist is working with a massive dataset that exceeds the memory capacity of a single machine. The data scientist is considering using Apache Spark™ instead of traditional single-machine languages like standard Python scripts.
Which two advantages does Apache Spark™ offer over a normal single-machine language in this scenario? (Choose 2 answers)

A) It can distribute data processing tasks across a cluster of machines, enabling horizontal scalability.
B) It processes data solely on disk storage, reducing the need for memory resources.
C) It eliminates the need to write any code, automatically handling all data processing.
D) It requires specialized hardware to run, making it unsuitable for commodity hardware clusters.
E) It has built-in fault tolerance, allowing it to recover seamlessly from node failures during computation.

4. A DataFrame df has columns name, age, and salary. The developer needs to sort the DataFrame by age in ascending order and salary in descending order.
Which code snippet meets the requirement of the developer?

A) df.sort("age", "salary", ascending=[False, True]).show()
B) df.sort("age", "salary", ascending=[True, True]).show()
C) df.orderBy(col("age").asc(), col("salary").asc()).show()
D) df.orderBy("age", "salary", ascending=[True, False]).show()

5. Given the code fragment:

import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A) psdf.to_dataframe()
B) psdf.to_pandas()
C) psdf.to_spark()
D) psdf.to_pyspark()

Solutions: