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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 scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFrame df with columns user_id, product_id, and purchase_amount and needs to perform some operations on this data efficiently.
Which sequence of operations results in transformations that require a shuffle followed by transformations that do not?

A) df.withColumn("purchase_date", current_date()).where("total_purchase > 50")
B) df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
C) df.withColumn("discount", df.purchase_amount * 0.1).select("discount")
D) df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")

2. A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set for spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?

A) A broadcast nested loop join
B) A sort-merge join
C) A shuffled hash join
D) A Cartesian join

3. A data engineer wants to write a Spark job that creates a new managed table. If the table already exists, the job should fail and not modify anything.
Which save mode and method should be used?

A) saveAsTable with mode Overwrite
B) saveAsTable with mode ErrorIfExists
C) save with mode Ignore
D) save with mode ErrorIfExists

4. 15 of 55.
A data engineer is working on a Streaming DataFrame (streaming_df) with the following streaming data:
id
name
count
timestamp
1
Delhi
20
2024-09-19T10:11
1
Delhi
50
2024-09-19T10:12
2
London
50
2024-09-19T10:15
3
Paris
30
2024-09-19T10:18
3
Paris
20
2024-09-19T10:20
4
Washington
10
2024-09-19T10:22
Which operation is supported with streaming_df?

A) streaming_df.count()
B) streaming_df.show()
C) streaming_df.filter("count < 30")
D) streaming_df.select(countDistinct("name"))

5. A data engineer is asked to build an ingestion pipeline for a set of Parquet files delivered by an upstream team on a nightly basis. The data is stored in a directory structure with a base path of "/path/events/data". The upstream team drops daily data into the underlying subdirectories following the convention year/month/day.
A few examples of the directory structure are:

Which of the following code snippets will read all the data within the directory structure?

A) df = spark.read.option("recursiveFileLookup", "true").parquet("/path/events/data/")
B) df = spark.read.parquet("/path/events/data/*")
C) df = spark.read.option("inferSchema", "true").parquet("/path/events/data/")
D) df = spark.read.parquet("/path/events/data/")

Solutions: