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Databricks Certified Associate Developer for Apache Spark 3.0 (Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0) - Databricks Exam Questions

Last updated on June 20, 2026

97% Exam Compliance
180 Total Questions
1
Question
Which of the following code blocks returns a copy of DataFrame itemsDf where the column supplier has been renamed to manufacturer?
Options
A itemsDf.withColumn(["supplier", "manufacturer"])
B itemsDf.withColumnRenamed("supplier", "manufacturer")
C itemsDf.withColumn("supplier").alias("manufacturer")
D itemsDf.withColumnsRenamed("supplier", "manufacturer")
E itemsDf.withColumnRenamed(col("manufacturer"), col("supplier"))
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2
Question
The code block shown below should return a one-column DataFrame where the column storeId is converted to string type. Choose the answer that correctly fills the blanks in the code block to accomplish this. transactionsDf.__1__(__2__.__3__(__4__))
Options
A 1. select
2. col("storeId")
3. as
4. StringType
B 1. select
2. col("storeId")
3. cast
4. StringType()
C 1. select
2. col("storeId")
3. cast
4. StringType
D 1. select
2. storeId
3. cast
4. StringType()
E 1. cast
2. "storeId"
3. as
4. StringType()
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3
Question
Which of the following describes a narrow transformation?
Options
A narrow transformation is an operation in which data is exchanged across partitions.
B A narrow transformation is an operation in which data is exchanged across the cluster.
C A narrow transformation is a process in which data from multiple RDDs is used.
D A narrow transformation is a process in which 32-bit float variables are cast to smaller float variables, like 16-bit or 8-bit float variables.
E A narrow transformation is an operation in which no data is exchanged across the cluster.
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4
Question
Which of the following code blocks returns a single-column DataFrame showing the number of words in column supplier of DataFrame itemsDf?

Sample of DataFrame itemsDf:

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

2. |itemId|attributes |supplier |

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

4. |1 |[blue, winter, cozy] |Sports Company Inc.|

5. |2 |[red, summer, fresh, cooling]|YetiX |

6. |3 |[green, summer, travel] |Sports Company Inc.|

7. +------+-----------------------------+-------------------+
Options
A itemsDf.select(size(split("supplier", " ")))
B itemsDf.select(word_count("supplier"))
C itemsDf.split("supplier", " ").size()
D spark.select(size(split(col(supplier), " ")))
E itemsDf.split("supplier", " ").count()
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5
Question
Which of the following is not a feature of Adaptive Query Execution?
Options
A Reroute a query in case of an executor failure.
B Split skewed partitions into smaller partitions to avoid differences in partition processing time.
C Collect runtime statistics during query execution.
D Coalesce partitions to accelerate data processing.
E Replace a sort merge join with a broadcast join, where appropriate.
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