Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Exam Dumps

Databricks Certified Associate Developer for Apache Spark 3.0 Exam

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Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Sample Questions

Question # 1

Which of the following options describes the responsibility of the executors in Spark? 

A. The executors accept jobs from the driver, analyze those jobs, and return results to the driver. 
B. The executors accept tasks from the driver, execute those tasks, and return results to the cluster manager.
 C. The executors accept tasks from the driver, execute those tasks, and return results to the driver. 
D. The executors accept tasks from the cluster manager, execute those tasks, and return results to the driver. E. The executors accept jobs from the driver, plan those jobs, and return results to the cluster manager.

Answer: C

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Question # 2

Which of the following describes the role of tasks in the Spark execution hierarchy? 

A. Tasks are the smallest element in the execution hierarchy. 
B. Within one task, the slots are the unit of work done for each partition of the data. 
C. Tasks are the second-smallest element in the execution hierarchy. 
D. Stages with narrow dependencies can be grouped into one task. 
E. Tasks with wide dependencies can be grouped into one stage. 

Answer: A

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Question # 3

Which of the following describes the role of the cluster manager?

A. The cluster manager schedules tasks on the cluster in client mode. 
B. The cluster manager schedules tasks on the cluster in local mode. 
C. The cluster manager allocates resources to Spark applications and maintains the executor processes in client mode. 
D. The cluster manager allocates resources to Spark applications and maintains the executor processes in remote mode. 
E. The cluster manager allocates resources to the DataFrame manager. 

Answer: C

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Question # 4

Which of the following is the idea behind dynamic partition pruning in Spark? 

A. Dynamic partition pruning is intended to skip over the data you do not need in the results of a query. 
B. Dynamic partition pruning concatenates columns of similar data types to optimize join performance. 
C. Dynamic partition pruning performs wide transformations on disk instead of in memory. 
D. Dynamic partition pruning reoptimizes physical plans based on data types and broadcast variables. 
E. Dynamic partition pruning reoptimizes query plans based on runtime statistics collected during query execution. 

Answer: A

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Question # 5

Which of the following is one of the big performance advantages that Spark has over Hadoop? 

A. Spark achieves great performance by storing data in the DAG format, whereas Hadoop can only use parquet files.
B. Spark achieves higher resiliency for queries since, different from Hadoop, it can be deployed on Kubernetes
C. Spark achieves great performance by storing data and performing computation in memory, whereas large jobs in Hadoop require a large amount of relatively slow disk I/O operations
D. Spark achieves great performance by storing data in the HDFS format, whereas Hadoop can only use parquet files.
E. Spark achieves performance gains for developers by extending Hadoop's DataFrames with a userfriendly API.

Answer: C

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Question # 6

Which of the following is the deepest level in Spark's execution hierarchy? 

A. Job 
B. Task 
C. Executor 
D. Slot E. Stage

Answer: B

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Question # 7

Which of the following statements about garbage collection in Spark is incorrect? 

A. Garbage collection information can be accessed in the Spark UI's stage detail view. 
B. Optimizing garbage collection performance in Spark may limit caching ability. 
C. Manually persisting RDDs in Spark prevents them from being garbage collected. 
D. In Spark, using the G1 garbage collector is an alternative to using the default Parallel garbage collector.
E. Serialized caching is a strategy to increase the performance of garbage collection. 

Answer: C

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Question # 8

The code block displayed below contains an error. The code block should return a DataFrame where all entries in column supplier contain the letter combination et in this order. Find the error. Code block: itemsDf.filter(Column('supplier').isin('et')) 

A. The Column operator should be replaced by the col operator and instead of isin, contains should be used. 
B. The expression inside the filter parenthesis is malformed and should be replaced by isin('et', 'supplier'). 
C. Instead of isin, it should be checked whether column supplier contains the letters et, so isin should be replaced with contains. In addition, the column should be accessed using col['supplier']. 
D. The expression only returns a single column and filter should be replaced by select. 

Answer: B

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Question # 9

Which of the following describes characteristics of the Dataset API? 

A. The Dataset API does not support unstructured data. 
B. In Python, the Dataset API mainly resembles Pandas' DataFrame API. 
C. In Python, the Dataset API's schema is constructed via type hints. 
D. The Dataset API is available in Scala, but it is not available in Python. 
E. The Dataset API does not provide compile-time type safety. 

Answer: D

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