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

2. A data engineer has been asked to produce a Parquet table which is overwritten every day with the latest data. The downstream consumer of this Parquet table has a hard requirement that the data in this table is produced with all records sorted by the market_time field.
Which line of Spark code will produce a Parquet table that meets these requirements?

A) final_df \
.sortWithinPartitions("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
B) final_df \
.sort("market_time") \
.coalesce(1) \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
C) final_df \
.orderBy("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")
D) final_df \
.sort("market_time") \
.write \
.format("parquet") \
.mode("overwrite") \
.saveAsTable("output.market_events")

3. Given the schema:

event_ts TIMESTAMP,
sensor_id STRING,
metric_value LONG,
ingest_ts TIMESTAMP,
source_file_path STRING
The goal is to deduplicate based on: event_ts, sensor_id, and metric_value.
Options:

A) dropDuplicates on the exact matching fields
B) dropDuplicates with no arguments (removes based on all columns)
C) dropDuplicates on all columns (wrong criteria)
D) groupBy without aggregation (invalid use)

4. You have:
DataFrame A: 128 GB of transactions
DataFrame B: 1 GB user lookup table
Which strategy is correct for broadcasting?

A) DataFrame A should be broadcasted because it is larger and will eliminate the need for shuffling DataFrame B
B) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling DataFrame A
C) DataFrame A should be broadcasted because it is smaller and will eliminate the need for shuffling itself
D) DataFrame B should be broadcasted because it is smaller and will eliminate the need for shuffling itself

5. 24 of 55.
Which code should be used to display the schema of the Parquet file stored in the location events.parquet?

A) spark.sql("SELECT schema FROM events.parquet").show()
B) spark.sql("SELECT * FROM events.parquet").show()
C) spark.read.parquet("events.parquet").printSchema()
D) spark.read.format("parquet").load("events.parquet").show()

Solutions: