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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. A data engineer replaces the exact percentile() function with approx_percentile() to improve performance, but the results are drifting too far from expected values.
Which change should be made to solve the issue?

A) Decrease the value of the accuracy parameter in order to decrease the memory usage but also improve the accuracy
B) Increase the last value of the percentage parameter to increase the accuracy of the percentile ranges
C) Increase the value of the accuracy parameter in order to increase the memory usage but also improve the accuracy
D) Decrease the first value of the percentage parameter to increase the accuracy of the percentile ranges

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

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

4. Given a CSV file with the content:

And the following code:
from pyspark.sql.types import *
schema = StructType([
StructField("name", StringType()),
StructField("age", IntegerType())
])
spark.read.schema(schema).csv(path).collect()
What is the resulting output?

A) [Row(name='bambi', age=None), Row(name='alladin', age=20)]
B) [Row(name='alladin', age=20)]
C) The code throws an error due to a schema mismatch.
D) [Row(name='bambi'), Row(name='alladin', age=20)]

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

Solutions: