新版CDP-3002考古題：CDP Data Engineer - Certification Exam100％通過考試，Cloudera CDP-3002 認證為什麼Fast2test Cloudera的CDP-3002考試培訓資料與別的培訓資料相比，它更受廣大考生的歡迎呢，第一，這是共鳴的問題，我們必須真正瞭解考生的需求，而且要比任何網站都要全面到位。第二，專注，為了做好我們決定完成的事情，必須放棄所有不重要的機會。第三，人們的確會用表面來判斷一個東西的好壞，我們或許擁有最優秀最高品質的產品，但如果以粗製濫造的方式展示出來，自然會被列為粗製濫造的產品，如果以既有創意又很專業的方式呈現，那麼我們將得到最高的效果。Fast2test Cloudera的CDP-3002考試培訓資料就是這樣成功的培訓資料，舍它其誰? 最新的 Cloudera Certification CDP-3002 免費考試真題 (Q110-Q115):問題 #110
In a PySpark application running on Kubernetes, you want to enable dynamic allocation of Executors. Which configuration setting is essential to turn on this feature?
A. 'spark.kubernetes.executor.dynamicAllocation'
B. 'spark.dynamicAllocation.enabled'
C. 'spark.executor.instances'
D. 'spark.kubernetes.dynamicAllocation.enabled'
答案：B
解題說明：
The configuration 'spark.dynamicAllocation.enabled' is used to enable the dynamic allocation feature in Spark applications. This feature allows Spark to dynamically adjust the number of Executor pods in Kubernetes based on the current workload.
問題 #111
When persisting an RDD in Spark, what factors should you consider when choosing the storage level (e.g., MEMORY ONLY, MEMORY AND_DISK)?
A. Only consider the size of the RDD
B. Balance memory usage with fault tolerance needs
C. All of the above
D. Prioritize keeping data in memory for faster access
答案：C
解題說明：
Choosing the storage level involves considering various factors. You should consider the size of the RDD A, as large datasets might not fit entirely in memory. Prioritizing memory usage B improves access speed but might not be suitable for fault-tolerance. Balancing memory usage with disk persistence C ensures data availability even in case of node failures, offering a compromise between performance and fault tolerance.
問題 #112
A data engineer needs to query a table stored in Apache Hive using SparkSQL. Which of the following commands correctly retrieves data from a Hive table named 'sales data'?
A.
B.
C.
D.
答案：B
解題說明：
Option C is the correct choice because it uses the 'spark.sql' method to execute a SQL query on a Hive table.
問題 #113
In Apache Spark, which storage level is recommended for caching data that is accessed frequently but is too large to fit in memory?
A. OFF HEAP
B. MEMORY AND DISK
C. DISK ONLY
D. MEMORY ONLY
答案：B
解題說明：
The MEMORY_AND_DISK storage level is recommended for caching data that is frequently accessed but too large to fit entirely in memory. This level stores the data in memory as much as possible and spills the remainder to disk, providing a good balance between performance and resource utilization.
問題 #114
You want to write the results of a Spark DataFrame back to a Hive table. How can you achieve this efficiently?
A. Use the DataFrame.write.saveAsTable("table_name") method
B. Use the hiveContext.sql("lNSERT INTO table_name SELECT FROM df') method
C. Implement a custom function to write individual rows to the Hive table
D. Convert the DataFrame to a temporary table and then use HiveQL operations
答案：A
解題說明：
Option B provides a concise and efficient way to write Spark DataFrame content to a Hive table. This method automatically handles schema conversion and data partitioning, offering a seamless integration between the two frameworks.
