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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. 22 of 55.
A Spark application needs to read multiple Parquet files from a directory where the files have differing but compatible schemas.
The data engineer wants to create a DataFrame that includes all columns from all files.
Which code should the data engineer use to read the Parquet files and include all columns using Apache Spark?

A) spark.read.option("mergeSchema", True).parquet("/data/parquet/")
B) spark.read.format("parquet").option("inferSchema", "true").load("/data/parquet/")
C) spark.read.parquet("/data/parquet/")
D) spark.read.parquet("/data/parquet/").option("mergeAllCols", True)

2. An application architect has been investigating Spark Connect as a way to modernize existing Spark applications running in their organization.
Which requirement blocks the adoption of Spark Connect in this organization?

A) Complete Spark API support: the ability to migrate all existing code to Spark Connect without modification, including the RDD APIs
B) Debuggability: the ability to perform interactive debugging directly from the application code
C) Stability: isolation of application code and dependencies from each other and the Spark driver
D) Upgradability: the ability to upgrade the Spark applications independently from the Spark driver itself

3. A data engineer needs to write a Streaming DataFrame as Parquet files.
Given the code:

Which code fragment should be inserted to meet the requirement?
A)

B)

C)

D)

Which code fragment should be inserted to meet the requirement?

A) CopyEdit
.option("format", "parquet")
.option("destination", "path/to/destination/dir")
B) .format("parquet")
.option("location", "path/to/destination/dir")
C) .format("parquet")
.option("path", "path/to/destination/dir")
D) .option("format", "parquet")
.option("location", "path/to/destination/dir")

4. Given the code:

df = spark.read.csv("large_dataset.csv")
filtered_df = df.filter(col("error_column").contains("error"))
mapped_df = filtered_df.select(split(col("timestamp"), " ").getItem(0).alias("date"), lit(1).alias("count")) reduced_df = mapped_df.groupBy("date").sum("count") reduced_df.count() reduced_df.show() At which point will Spark actually begin processing the data?

A) When the show action is applied
B) When the count action is applied
C) When the filter transformation is applied
D) When the groupBy transformation is applied

5. A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?
Options:

A) Use the sortBy() transformation to reorganize the data
B) Use the repartition() transformation with a lower number of partitions
C) Use the coalesce() transformation with a lower number of partitions
D) Use the distinct() transformation to combine similar partitions

Solutions: