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Free PDF Databricks - Associate-Developer-Apache-Spark-3.5 - Valid Databricks Certified Associate Developer for Apache Spark 3.5 - Python Latest Test Testking

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q57-Q62):

NEW QUESTION # 57
A data scientist at a financial services company is working with a Spark DataFrame containing transaction records. The DataFrame has millions of rows and includes columns fortransaction_id,account_number, transaction_amount, andtimestamp. Due to an issue with the source system, some transactions were accidentally recorded multiple times with identical information across all fields. The data scientist needs to remove rows with duplicates across all fields to ensure accurate financial reporting.
Which approach should the data scientist use to deduplicate the orders using PySpark?

  • A. df = df.dropDuplicates()
  • B. df = df.dropDuplicates(["transaction_amount"])
  • C. df = df.groupBy("transaction_id").agg(F.first("account_number"), F.first("transaction_amount"), F.first ("timestamp"))
  • D. df = df.filter(F.col("transaction_id").isNotNull())

Answer: A

Explanation:
dropDuplicates() with no column list removes duplicates based on all columns.
It's the most efficient and semantically correct way to deduplicate records that are completely identical across all fields.
From the PySpark documentation:
dropDuplicates(): Return a new DataFrame with duplicate rows removed, considering all columns if none are specified.
- Source:PySpark DataFrame.dropDuplicates() API


NEW QUESTION # 58
A DataFramedfhas columnsname,age, andsalary. The developer needs to sort the DataFrame byagein ascending order andsalaryin descending order.
Which code snippet meets the requirement of the developer?

  • A. df.orderBy(col("age").asc(), col("salary").asc()).show()
  • B. df.sort("age", "salary", ascending=[False, True]).show()
  • C. df.orderBy("age", "salary", ascending=[True, False]).show()
  • D. df.sort("age", "salary", ascending=[True, True]).show()

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To sort a PySpark DataFrame by multiple columns with mixed sort directions, the correct usage is:
python
CopyEdit
df.orderBy("age","salary", ascending=[True,False])
agewill be sorted in ascending order
salarywill be sorted in descending order
TheorderBy()andsort()methods in PySpark accept a list of booleans to specify the sort direction for each column.
Documentation Reference:PySpark API - DataFrame.orderBy


NEW QUESTION # 59
A data engineer writes the following code to join two DataFramesdf1anddf2:
df1 = spark.read.csv("sales_data.csv") # ~10 GB
df2 = spark.read.csv("product_data.csv") # ~8 MB
result = df1.join(df2, df1.product_id == df2.product_id)

Which join strategy will Spark use?

  • A. Shuffle join because no broadcast hints were provided
  • B. Shuffle join, because AQE is not enabled, and Spark uses a static query plan
  • C. Broadcast join, as df2 is smaller than the default broadcast threshold
  • D. Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The default broadcast join threshold in Spark is:
spark.sql.autoBroadcastJoinThreshold = 10MB
Sincedf2is only 8 MB (less than 10 MB), Spark will automatically apply a broadcast join without requiring explicit hints.
From the Spark documentation:
"If one side of the join is smaller than the broadcast threshold, Spark will automatically broadcast it to all executors." A is incorrect because Spark does support auto broadcast even with static plans.
B is correct: Spark will automatically broadcast df2.
C and D are incorrect because Spark's default logic handles this optimization.
Final Answer: B


NEW QUESTION # 60
An engineer has two DataFrames: df1 (small) and df2 (large). A broadcast join is used:
python
CopyEdit
frompyspark.sql.functionsimportbroadcast
result = df2.join(broadcast(df1), on='id', how='inner')
What is the purpose of using broadcast() in this scenario?
Options:

  • A. It ensures that the join happens only when the id values are identical.
  • B. It increases the partition size for df1 and df2.
  • C. It filters the id values before performing the join.
  • D. It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.

Answer: D

Explanation:
broadcast(df1) tells Spark to send the small DataFrame (df1) to all worker nodes.
This eliminates the need for shuffling df1 during the join.
Broadcast joins are optimized for scenarios with one large and one small table.
Reference:Spark SQL Performance Tuning Guide - Broadcast Joins


NEW QUESTION # 61
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
Options:

  • A. A job contains multiple stages, and each stage contains multiple tasks.
  • B. A stage contains multiple tasks, and each task contains multiple jobs.
  • C. A job contains multiple tasks, and each task contains multiple stages.
  • D. A stage contains multiple jobs, and each job contains multiple tasks.

Answer: A

Explanation:
A Sparkjobis triggered by an action (e.g., count, show).
The job is broken intostages, typically one per shuffle boundary.
Eachstageis divided into multipletasks, which are distributed across worker nodes.
Reference:Spark Execution Model


NEW QUESTION # 62
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