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1

In a PySpark ETL pipeline, a DataFrame transformation has been defined but no computation appears to run yet. Why?

A. Spark transformations are lazy and execute when an action requires a result B. Every transformation immediately writes results to disk C. The DataFrame is automatically converted to a Python list D. Spark ignores all transformations

2

When transforming a DataFrame, a DataFrame has many small partitions and too much task scheduling overhead. Which adjustment might be appropriate after measuring the workload?

A. Reduce partition count using an appropriate coalesce or repartition strategy B. Always increase the number of partitions to one million C. Collect all records to the driver D. Add a sleep statement between every transformation

3

For a production data engineering job, a developer wants to inspect column names and data types without scanning all rows for display. Which method is appropriate?

A. printSchema() B. collect() C. repartition(1) D. drop()

4

In a distributed data processing task, a DataFrame transformation has been defined but no computation appears to run yet. Why?

A. Spark transformations are lazy and execute when an action requires a result B. Every transformation immediately writes results to disk C. The DataFrame is automatically converted to a Python list D. Spark ignores all transformations

5

When optimizing a DataFrame workflow, a DataFrame has many small partitions and too much task scheduling overhead. Which adjustment might be appropriate after measuring the workload?

A. Reduce partition count using an appropriate coalesce or repartition strategy B. Always increase the number of partitions to one million C. Collect all records to the driver D. Add a sleep statement between every transformation

6

In a PySpark ETL pipeline, a developer wants to inspect column names and data types without scanning all rows for display. Which method is appropriate?

A. printSchema() B. collect() C. repartition(1) D. drop()

7

When transforming a DataFrame, a DataFrame transformation has been defined but no computation appears to run yet. Why?

A. Spark transformations are lazy and execute when an action requires a result B. Every transformation immediately writes results to disk C. The DataFrame is automatically converted to a Python list D. Spark ignores all transformations

8

For a production data engineering job, a DataFrame has many small partitions and too much task scheduling overhead. Which adjustment might be appropriate after measuring the workload?

A. Reduce partition count using an appropriate coalesce or repartition strategy B. Always increase the number of partitions to one million C. Collect all records to the driver D. Add a sleep statement between every transformation

9

In a distributed data processing task, a developer wants to inspect column names and data types without scanning all rows for display. Which method is appropriate?

A. printSchema() B. collect() C. repartition(1) D. drop()

10

When optimizing a DataFrame workflow, a DataFrame transformation has been defined but no computation appears to run yet. Why?

A. Spark transformations are lazy and execute when an action requires a result B. Every transformation immediately writes results to disk C. The DataFrame is automatically converted to a Python list D. Spark ignores all transformations

Databricks Certified Associate Developer for Apache Spark

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Course Questions

In a PySpark ETL pipeline, a DataFrame transformation has been defined but no computation appears to run yet. Why?

When transforming a DataFrame, a DataFrame has many small partitions and too much task scheduling overhead. Which adjustment might be appropriate after measuring the workload?

For a production data engineering job, a developer wants to inspect column names and data types without scanning all rows for display. Which method is appropriate?

In a distributed data processing task, a DataFrame transformation has been defined but no computation appears to run yet. Why?

When optimizing a DataFrame workflow, a DataFrame has many small partitions and too much task scheduling overhead. Which adjustment might be appropriate after measuring the workload?

In a PySpark ETL pipeline, a developer wants to inspect column names and data types without scanning all rows for display. Which method is appropriate?

When transforming a DataFrame, a DataFrame transformation has been defined but no computation appears to run yet. Why?

For a production data engineering job, a DataFrame has many small partitions and too much task scheduling overhead. Which adjustment might be appropriate after measuring the workload?

In a distributed data processing task, a developer wants to inspect column names and data types without scanning all rows for display. Which method is appropriate?

When optimizing a DataFrame workflow, a DataFrame transformation has been defined but no computation appears to run yet. Why?

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