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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q22-Q27):
NEW QUESTION # 22
The following code fragment results in an error:
Which code fragment should be used instead?
Answer: D
NEW QUESTION # 23
An engineer wants to join two DataFramesdf1anddf2on the respectiveemployee_idandemp_idcolumns:
df1:employee_id INT,name STRING
df2:emp_id INT,department STRING
The engineer uses:
result = df1.join(df2, df1.employee_id == df2.emp_id, how='inner')
What is the behaviour of the code snippet?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In PySpark, when performing a join between two DataFrames, the columns do not have to share the same name. You can explicitly provide a join condition by comparing specific columns from each DataFrame.
This syntax is correct and fully supported:
df1.join(df2, df1.employee_id == df2.emp_id, how='inner')
This will perform an inner join betweendf1anddf2using theemployee_idfromdf1andemp_idfromdf2.
Reference: Databricks Spark 3.5 Documentation # DataFrame API # join()
NEW QUESTION # 24
A data engineer is working on a real-time analytics pipeline using Apache Spark Structured Streaming. The engineer wants to process incoming data and ensure that triggers control when the query is executed. The system needs to process data in micro-batches with a fixed interval of 5 seconds.
Which code snippet the data engineer could use to fulfil this requirement?
A)
B)
C)
D)
Options:
Answer: D
Explanation:
To define a micro-batch interval, the correct syntax is:
query = df.writeStream
outputMode("append")
trigger(processingTime='5 seconds')
start()
This schedules the query to execute every 5 seconds.
Continuous mode (used in Option A) is experimental and has limited sink support.
Option D is incorrect because processingTime must be a string (not an integer).
Option B triggers as fast as possible without interval control.
Reference:Spark Structured Streaming - Triggers
NEW QUESTION # 25
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?
Answer: D
Explanation:
Spark uses lazy evaluation. Transformations like filter, select, and groupBy only define the DAG (Directed Acyclic Graph). No execution occurs until an action is triggered.
The first action in the code is:reduced_df.count()
So Spark starts processing data at this line.
Reference:Apache Spark Programming Guide - Lazy Evaluation
NEW QUESTION # 26
A developer wants to refactor some older Spark code to leverage built-in functions introduced in Spark 3.5.0.
The existing code performs array manipulations manually. Which of the following code snippets utilizes new built-in functions in Spark 3.5.0 for array operations?
A)
B)
C)
D)
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct answer isBbecause it uses the new function count_if, introduced in Spark 3.5.0, which simplifies conditional counting within aggregations.
* F.count_if(condition) counts the number of rows that meet the specified boolean condition.
* In this example, it directly counts how many times spot_price >= min_price evaluates to true, replacing the older verbose combination of when/otherwise and filtering or summing.
Official Spark 3.5.0 documentation notes the addition of count_if to simplify this kind of logic:
"Added count_if aggregate function to count only the rows where a boolean condition holds (SPARK-
43773)."
Why other options are incorrect or outdated:
* Auses a legacy-style method of adding a flag column (when().otherwise()), which is verbose compared to count_if.
* Cperforms a simple min/max aggregation-useful but unrelated to conditional array operations or the updated functionality.
* Dincorrectly applies .filter() after .agg() which will cause an error, and misuses string "min_price" rather than the variable.
Therefore,Bis the only option leveraging new functionality from Spark 3.5.0 correctly and efficiently.
NEW QUESTION # 27
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