Databricks Associate-Developer-Apache-Spark-3.5 real exam prep : Databricks Certified Associate Developer for Apache Spark 3.5 - Python

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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. 34 of 55.
A data engineer is investigating a Spark cluster that is experiencing underutilization during scheduled batch jobs.
After checking the Spark logs, they noticed that tasks are often getting killed due to timeout errors, and there are several warnings about insufficient resources in the logs.
Which action should the engineer take to resolve the underutilization issue?

A) Increase the number of executor instances to handle more concurrent tasks.
B) Reduce the size of the data partitions to improve task scheduling.
C) Increase the executor memory allocation in the Spark configuration.
D) Set the spark.network.timeout property to allow tasks more time to complete without being killed.

2. 30 of 55.
A data engineer is working on a num_df DataFrame and has a Python UDF defined as:
def cube_func(val):
return val * val * val
Which code fragment registers and uses this UDF as a Spark SQL function to work with the DataFrame num_df?

A) spark.createDataFrame(cube_func("num")).show()
B) spark.udf.register("cube_func", cube_func)
num_df.selectExpr("cube_func(num)").show()
C) num_df.select(cube_func("num")).show()
D) num_df.register("cube_func").select("num").show()

3. A data engineer needs to write a DataFrame df to a Parquet file, partitioned by the column country, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?

A) df.write.mode("append").partitionBy("country").parquet("/data/output")
B) df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
C) df.write.partitionBy("country").parquet("/data/output")
D) df.write.mode("overwrite").parquet("/data/output")

4. A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:

The resulting Python dictionary must contain a mapping of region -> region id containing the smallest 3 region_id values.
Which code fragment meets the requirements?
A)

B)

C)

D)

The resulting Python dictionary must contain a mapping of region -> region_id for the smallest 3 region_id values.
Which code fragment meets the requirements?

A) regions = dict(
regions_df
.select('region', 'region_id')
.sort(desc('region_id'))
.take(3)
)
B) regions = dict(
regions_df
.select('region_id', 'region')
.limit(3)
.collect()
)
C) regions = dict(
regions_df
.select('region', 'region_id')
.sort('region_id')
.take(3)
)
D) regions = dict(
regions_df
.select('region_id', 'region')
.sort('region_id')
.take(3)
)

5. Which command overwrites an existing JSON file when writing a DataFrame?

A) df.write.json("path/to/file", overwrite=True)
B) df.write.format("json").save("path/to/file", mode="overwrite")
C) df.write.mode("overwrite").json("path/to/file")
D) df.write.overwrite.json("path/to/file")

Solutions: