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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q85-Q90):

NEW QUESTION # 85
An engineer notices a significant increase in the job execution time during the execution of a Spark job. After some investigation, the engineer decides to check the logs produced by the Executors.
How should the engineer retrieve the Executor logs to diagnose performance issues in the Spark application?

A. Use the Spark UI to select the stage and view the executor logs directly from the stages tab.
B. Locate the executor logs on the Spark master node, typically under the /tmp directory.
C. Fetch the logs by running a Spark job with the spark-sql CLI tool.
D. Use the command spark-submit with the -verbose flag to print the logs to the console.

Answer: A

Explanation:
The Spark UI is the standard and most effective way to inspect executor logs, task time, input size, and shuffles.
From the Databricks documentation:
"You can monitor job execution via the Spark Web UI. It includes detailed logs and metrics, including task-level execution time, shuffle reads/writes, and executor memory usage." (Source: Databricks Spark Monitoring Guide) Option A is incorrect: logs are not guaranteed to be in /tmp, especially in cloud environments.
B . -verbose helps during job submission but doesn't give detailed executor logs.
D . spark-sql is a CLI tool for running queries, not for inspecting logs.
Hence, the correct method is using the Spark UI → Stages tab → Executor logs.

NEW QUESTION # 86
A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?

A. By configuring the option recoveryLocation during the SparkSession initialization
B. By configuring the option checkpointLocation during writeStream
C. By configuring the option recoveryLocation during writeStream
D. By configuring the option checkpointLocation during readStream

Answer: B

Explanation:
To enable a Structured Streaming query to recover from failures or intentional shutdowns, it is essential to specify the checkpointLocation option during the writeStream operation. This checkpoint location stores the progress information of the streaming query, allowing it to resume from where it left off.
According to the Databricks documentation:
"You must specify the checkpointLocation option before you run a streaming query, as in the following example:
.option("checkpointLocation", "/path/to/checkpoint/dir")
.toTable("catalog.schema.table")
- Databricks Documentation: Structured Streaming checkpoints
By setting the checkpointLocation during writeStream, Spark can maintain state information and ensure exactly-once processing semantics, which are crucial for reliable streaming applications.

NEW QUESTION # 87
Given the code fragment:

import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?

A. psdf.to_spark()
B. psdf.to_pandas()
C. psdf.to_dataframe()
D. psdf.to_pyspark()

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Pandas API on Spark (pyspark.pandas) allows interoperability with PySpark DataFrames. To convert apyspark.pandas.DataFrameto a standard PySpark DataFrame, you use.to_spark().
Example:
df = psdf.to_spark()
This is the officially supported method as per Databricks Documentation.
Incorrect options:
B, D: Invalid or nonexistent methods.
C: Converts to a local pandas DataFrame, not a PySpark DataFrame.

NEW QUESTION # 88
25 of 55.
A Data Analyst is working on employees_df and needs to add a new column where a 10% tax is calculated on the salary.
Additionally, the DataFrame contains the column age, which is not needed.
Which code fragment adds the tax column and removes the age column?

A. employees_df = employees_df.withColumn("tax", col("salary") + 0.1).drop("age")
B. employees_df = employees_df.withColumn("tax", col("salary") * 0.1).drop("age")
C. employees_df = employees_df.withColumn("tax", lit(0.1)).drop("age")
D. employees_df = employees_df.dropField("age").withColumn("tax", col("salary") * 0.1)

Answer: B

Explanation:
To create a new calculated column in Spark, use the .withColumn() method.
To remove an unwanted column, use the .drop() method.
Correct syntax:
from pyspark.sql.functions import col
employees_df = employees_df.withColumn("tax", col("salary") * 0.1).drop("age")
.withColumn("tax", col("salary") * 0.1) → adds a new column where tax = 10% of salary.
.drop("age") → removes the age column from the DataFrame.
Why the other options are incorrect:
B: lit(0.1) creates a constant value, not a calculated tax.
C: .dropField() is not a DataFrame API method (used only in struct field manipulations).
D: Adds 0.1 to salary instead of calculating 10%.
Reference:
PySpark DataFrame API - withColumn(), drop(), and col().
Databricks Exam Guide (June 2025): Section "Developing Apache Spark DataFrame/DataSet API Applications" - manipulating, renaming, and dropping columns.

NEW QUESTION # 89
What is a feature of Spark Connect?

A. It supports DataStreamReader, DataStreamWriter, StreamingQuery, and Streaming APIs
B. It supports only PySpark applications
C. It has built-in authentication
D. Supports DataFrame, Functions, Column, SparkContext PySpark APIs

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Spark Connect is a client-server architecture introduced in Apache Spark 3.4, designed to decouple the client from the Spark driver, enabling remote connectivity to Spark clusters.
According to the Spark 3.5.5 documentation:
"Majority of the Streaming API is supported, including DataStreamReader, DataStreamWriter, StreamingQuery and StreamingQueryListener." This indicates that Spark Connect supports key components of Structured Streaming, allowing for robust streaming data processing capabilities.
Regarding other options:
B).While Spark Connect supports DataFrame, Functions, and Column APIs, it does not support SparkContext and RDD APIs.
C).Spark Connect supports multiple languages, including PySpark and Scala, not just PySpark.
D).Spark Connect does not have built-in authentication but is designed to work seamlessly with existing authentication infrastructures.

NEW QUESTION # 90
......

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Latest Associate-Developer-Apache-Spark-3.5 Exam Notes, Associate-Developer-Apache-Spark-3.5 Valid Test Test

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q85-Q90):

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