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Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Practice Test Questions Answers

Exam Code: Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 (Updated 180 Q&As with Explanation)
Exam Name: Databricks Certified Associate Developer for Apache Spark 3.0 Exam
Last Update: 14-Dec-2025
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Questions Include:

  • Single Choice: 180 Q&A's

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    Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Questions and Answers

    Question # 1

    Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating partitions that do not fit in memory when they are needed?

    A.

    from pyspark import StorageLevel

    transactionsDf.cache(StorageLevel.MEMORY_ONLY)

    B.

    transactionsDf.cache()

    C.

    transactionsDf.storage_level('MEMORY_ONLY')

    D.

    transactionsDf.persist()

    E.

    transactionsDf.clear_persist()

    F.

    from pyspark import StorageLevel

    transactionsDf.persist(StorageLevel.MEMORY_ONLY)

    Question # 2

    Which of the following code blocks returns only rows from DataFrame transactionsDf in which values in column productId are unique?

    A.

    transactionsDf.distinct("productId")

    B.

    transactionsDf.dropDuplicates(subset=["productId"])

    C.

    transactionsDf.drop_duplicates(subset="productId")

    D.

    transactionsDf.unique("productId")

    E.

    transactionsDf.dropDuplicates(subset="productId")

    Question # 3

    Which of the following code blocks returns a new DataFrame with the same columns as DataFrame transactionsDf, except for columns predError and value which should be removed?

    A.

    transactionsDf.drop(["predError", "value"])

    B.

    transactionsDf.drop("predError", "value")

    C.

    transactionsDf.drop(col("predError"), col("value"))

    D.

    transactionsDf.drop(predError, value)

    E.

    transactionsDf.drop("predError & value")

    Question # 4

    Which of the following code blocks returns a one-column DataFrame of all values in column supplier of DataFrame itemsDf that do not contain the letter X? In the DataFrame, every value should

    only be listed once.

    Sample of DataFrame itemsDf:

    1.+------+--------------------+--------------------+-------------------+

    2.|itemId| itemName| attributes| supplier|

    3.+------+--------------------+--------------------+-------------------+

    4.| 1|Thick Coat for Wa...|[blue, winter, cozy]|Sports Company Inc.|

    5.| 2|Elegant Outdoors ...|[red, summer, fre...| YetiX|

    6.| 3| Outdoors Backpack|[green, summer, t...|Sports Company Inc.|

    7.+------+--------------------+--------------------+-------------------+

    A.

    itemsDf.filter(col(supplier).not_contains('X')).select(supplier).distinct()

    B.

    itemsDf.select(~col('supplier').contains('X')).distinct()

    C.

    itemsDf.filter(not(col('supplier').contains('X'))).select('supplier').unique()

    D.

    itemsDf.filter(~col('supplier').contains('X')).select('supplier').distinct()

    E.

    itemsDf.filter(!col('supplier').contains('X')).select(col('supplier')).unique()

    Question # 5

    Which of the following describes the difference between client and cluster execution modes?

    A.

    In cluster mode, the driver runs on the worker nodes, while the client mode runs the driver on the client machine.

    B.

    In cluster mode, the driver runs on the edge node, while the client mode runs the driver in a worker node.

    C.

    In cluster mode, each node will launch its own executor, while in client mode, executors will exclusively run on the client machine.

    D.

    In client mode, the cluster manager runs on the same host as the driver, while in cluster mode, the cluster manager runs on a separate node.

    E.

    In cluster mode, the driver runs on the master node, while in client mode, the driver runs on a virtual machine in the cloud.

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