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Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Practice Test Questions Answers

Exam Code: Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 (Updated 136 Q&As with Explanation)
Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 – Python
Last Update: 14-Dec-2025
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Questions Include:

  • Single Choice: 131 Q&A's
  • Multiple Choice: 4 Q&A's

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    Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Questions and Answers

    Question # 1

    A Spark engineer is troubleshooting a Spark application that has been encountering out-of-memory errors during execution. By reviewing the Spark driver logs, the engineer notices multiple "GC overhead limit exceeded" messages.

    Which action should the engineer take to resolve this issue?

    A.

    Optimize the data processing logic by repartitioning the DataFrame.

    B.

    Modify the Spark configuration to disable garbage collection

    C.

    Increase the memory allocated to the Spark Driver.

    D.

    Cache large DataFrames to persist them in memory.

    Question # 2

    A Spark application is experiencing performance issues in client mode because the driver is resource-constrained.

    How should this issue be resolved?

    A.

    Add more executor instances to the cluster

    B.

    Increase the driver memory on the client machine

    C.

    Switch the deployment mode to cluster mode

    D.

    Switch the deployment mode to local mode

    Question # 3

    41 of 55.

    A data engineer is working on the DataFrame df1 and wants the Name with the highest count to appear first (descending order by count), followed by the next highest, and so on.

    The DataFrame has columns:

    id | Name | count | timestamp

    ---------------------------------

    1 | USA | 10

    2 | India | 20

    3 | England | 50

    4 | India | 50

    5 | France | 20

    6 | India | 10

    7 | USA | 30

    8 | USA | 40

    Which code fragment should the engineer use to sort the data in the Name and count columns?

    A.

    df1.orderBy(col("count").desc(), col("Name").asc())

    B.

    df1.sort("Name", "count")

    C.

    df1.orderBy("Name", "count")

    D.

    df1.orderBy(col("Name").desc(), col("count").asc())

    Question # 4

    38 of 55.

    A data engineer is working with Spark SQL and has a large JSON file stored at /data/input.json.

    The file contains records with varying schemas, and the engineer wants to create an external table in Spark SQL that:

      Reads directly from /data/input.json.

      Infers the schema automatically.

      Merges differing schemas.

    Which code snippet should the engineer use?

    A.

    CREATE EXTERNAL TABLE users

    USING json

    OPTIONS (path '/data/input.json', mergeSchema 'true');

    B.

    CREATE TABLE users

    USING json

    OPTIONS (path '/data/input.json');

    C.

    CREATE EXTERNAL TABLE users

    USING json

    OPTIONS (path '/data/input.json', inferSchema 'true');

    D.

    CREATE EXTERNAL TABLE users

    USING json

    OPTIONS (path '/data/input.json', mergeAll 'true');

    Question # 5

    31 of 55.

    Given a DataFrame df that has 10 partitions, after running the code:

    df.repartition(20)

    How many partitions will the result DataFrame have?

    A.

    5

    B.

    20

    C.

    Same number as the cluster executors

    D.

    10

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