Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Exam Dumps

Databricks Certified Associate Developer for Apache Spark 3.5 – Python

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Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Sample Questions

Question # 1

A data scientist of an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user. Before further processing the data, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns in this DataFrame. The PII columns in df_user are first_name, last_name, email, and birthdate. Which code snippet can be used to meet this requirement? 

A. df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate") 
B. df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
C. df_user_non_pii = df_user.dropfields("first_name", "last_name", "email", "birthdate")  
D. df_user_non_pii = df_user.dropfields("first_name, last_name, email, birthdate")  

Answer: A

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Question # 2

A Spark DataFrame df is cached using the MEMORY_AND_DISK storage level, but the DataFrame is too large to fit entirely in memory. What is the likely behavior when Spark runs out of memory to store the DataFrame?

A. Spark duplicates the DataFrame in both memory and disk. If it doesn't fit in memory, the DataFrame is stored and retrieved from the disk entirely
B. Spark splits the DataFrame evenly between memory and disk, ensuring balanced storage utilization. 
C. Spark will store as much data as possible in memory and spill the rest to disk when memory is full, continuing processing with performance overhead. 
D. Spark stores the frequently accessed rows in memory and less frequently accessed rows on disk, utilizing both resources to offer balanced performance. 

Answer: C

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Question # 3

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

Answer: D

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Question # 4

A data scientist is analyzing a large dataset and has written a PySpark script that includes several transformations and actions on a DataFrame. The script ends with a collect() action to retrieve the results. How does Apache Sparkâ„¢'s execution hierarchy process the operations when the data scientist runs this script? 

A. The script is first divided into multiple applications, then each application is split into jobs, stages, and finally tasks
B. The entire script is treated as a single job, which is then divided into multiple stages, and each stage is further divided into tasks based on data partitions.
C. The collect() action triggers a job, which is divided into stages at shuffle boundaries, and each stage is split into tasks that operate on individual data partitions. 
D. Spark creates a single task for each transformation and action in the script, and these tasks are grouped into stages and jobs based on their dependencies. 
Answer: C 

Answer: C

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Question # 5

A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately. Which two characteristics of Apache Spark's execution model explain this behavior? Choose 2 answers:

A. The Spark engine requires manual intervention to start executing transformations.  
B. Only actions trigger the execution of the transformation pipeline.  
C. Transformations are executed immediately to build the lineage graph.  
D. The Spark engine optimizes the execution plan during the transformations, causing delays.  
E. Transformations are evaluated lazily.  

Answer: B,E

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Question # 6

An engineer has a large ORC file located at /file/test_data.orc and wants to read only specific columns to reduce memory usage. Which code fragment will select the columns, i.e., col1, col2, during the reading process? 

A. spark.read.orc("/file/test_data.orc").filter("col1 = 'value' ").select("col2") 
B. spark.read.format("orc").select("col1", "col2").load("/file/test_data.orc") 
C. spark.read.orc("/file/test_data.orc").selected("col1", "col2") 
D. spark.read.format("orc").load("/file/test_data.orc").select("col1", "col2") 

Answer: D

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Question # 7

A Spark engineer is troubleshooting a Spark application that has been encountering out-of-memory errors during execution. By reviewing the Spark driver logs, the engineer notices multiple "GC overhead limit exceeded" messages. Which action should the engineer take to resolve this issue? 

A. Optimize the data processing logic by repartitioning the DataFrame.  
B. Modify the Spark configuration to disable garbage collection  
C. Increase the memory allocated to the Spark Driver.
D. Cache large DataFrames to persist them in memory. 

Answer: C

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