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Free PDF Quiz 2025 Databricks Associate-Developer-Apache-Spark-3.5: Databricks Certified Associate Developer for Apache Spark 3.5 - Python Updated Trustworthy Pdf

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q75-Q80):

NEW QUESTION # 75
A Spark application is experiencing performance issues in client mode because the driver is resource- constrained.
How should this issue be resolved?

A. Add more executor instances to the cluster
B. Switch the deployment mode to cluster mode
C. Switch the deployment mode to local mode
D. Increase the driver memory on the client machine

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Spark's client mode, the driver runs on the local machine that submitted the job. If that machine is resource- constrained (e.g., low memory), performance degrades.
From the Spark documentation:
"In cluster mode, the driver runs inside the cluster, benefiting from cluster resources and scalability." Option A is incorrect - executors do not help the driver directly.
Option B might help short-term but does not scale.
Option C is correct - switching to cluster mode moves the driver to the cluster.
Option D (local mode) is for development/testing, not production.
Final Answer: C

NEW QUESTION # 76
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 show action is applied
B. When the count action is applied
C. When the groupBy transformation is applied
D. When the filter transformation is applied

Answer: B

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 # 77
What is the behavior for functiondate_sub(start, days)if a negative value is passed into thedaysparameter?

A. An error message of an invalid parameter will be returned
B. The number of days specified will be removed from the start date
C. The number of days specified will be added to the start date
D. The same start date will be returned

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The functiondate_sub(start, days)subtracts the number of days from the start date. If a negative number is passed, the behavior becomes a date addition.
Example:
SELECT date_sub('2024-05-01', -5)
-- Returns: 2024-05-06
So, a negative value effectively adds the absolute number of days to the date.
Reference: Spark SQL Functions # date_sub()

NEW QUESTION # 78
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 increases the partition size for df1 and df2.
B. It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
C. It ensures that the join happens only when the id values are identical.
D. It filters the id values before performing the join.

Answer: B

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 # 79
A data analyst wants to add a column date derived from a timestamp column.
Options:

A. dates_df.withColumn("date", f.to_date("timestamp")).show()
B. dates_df.withColumn("date", f.unix_timestamp("timestamp")).show()
C. dates_df.withColumn("date", f.date_format("timestamp", "yyyy-MM-dd")).show()
D. dates_df.withColumn("date", f.from_unixtime("timestamp")).show()

Answer: A

Explanation:
f.to_date() converts a timestamp or string to a DateType.
Ideal for extracting the date component (year-month-day) from a full timestamp.
Example:
frompyspark.sql.functionsimportto_date
dates_df.withColumn("date", to_date("timestamp"))
Reference:Spark SQL Date Functions

NEW QUESTION # 80
......

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