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CCA-505學習資料為消費者提供免費試用服務。如果您對我們的學習資料感興趣，您只需要進入我們的官方網站，您就可以免費下載並體驗我們的試用問題庫。通過試用，您將在CCA-505考試指南中獲得不同的學習經歷，您會發現我們所說的不是謊言，您將立即愛上我們的產品。作為您成功的關鍵，我們的學習材料可以為您帶來的好處不是靠金錢衡量的。 CCA-505測試題庫：Cloudera Certified Administrator for Apache Hadoop (CCAH) CDH5 Upgrade Exam不僅可以幫助您通過考試，還可以幫助您掌握一套新的學習方法，並教您如何高效學習，我們的學習材料將引領您走向成功。

無論您是新人還是具有更多經驗老手，CCA-505學習材料都將是你們的最佳選擇，因為這是我們的專業人士根據多年來的考試大綱和行業趨勢的變化進行編輯的。 CCA-505測試題庫：Cloudera Certified Administrator for Apache Hadoop (CCAH) CDH5 Upgrade Exam不僅可以幫助您提高學習效率，還可以幫助您將復習時間從長達幾個月縮短到一個月甚至兩三週，這樣您就可以使用最少的時間和精力獲得最大提升。

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考試前只需20-30小時的學習時間

在此之前，您可能需要數月甚至一年的時間來準備專業考試，但使用CCA-505考試指南，您只需要在考試前花費20-30小時進行複習即可。並且使用我們的學習材料，您將不再需要任何其他復習材料，因為我們的學習材料已包含所有重要的測試點。與此同時，CCA-505學習材料將為您提供全新的學習方法 - 讓您練習過程中的掌握知識。有許多人因閱讀書籍而感到頭疼，因為裡面有很多難以理解的知識。與此同時，教科書中那些無聊的描述常常讓人感到困倦。但是使用CCA-505測試題庫：Cloudera Certified Administrator for Apache Hadoop (CCAH) CDH5 Upgrade Exam，你將不再有這些煩惱。

模擬考試功能

CCA-505學習資料的內容全部由行業專家根據多年來的考試大綱和行業發展趨勢編制而成。它與市場上問題庫的內容不重疊，避免了反复練習引起的疲勞。 CCA-505考試指南不是一個拼湊的測試題，而是有自己的系統和層次結構，可以使用戶有效地提高效率。我們的學習材料包含由考試專家根據不同科目的特點和範圍編寫的試題。模擬真實的Cloudera Certified Administrator for Apache Hadoop (CCAH) CDH5 Upgrade Exam測試環境。測試結束後，系統還會給出總分和正確率。

最新的 CCAH CCA-505 免費考試真題:

1. You are upgrading a Hadoop cluster from HDFS and MapReduce version 1 (MRv1) to one running HDFS and MapReduce version 2 (MRv2) on YARN. You want to set and enforce a block of 128MB for all new files written to the cluster after the upgrade. What should you do?

A) Set dfs.block.size to 134217728 on all the worker nodes and client machines, and set the parameter to final. You do need to set this value on the NameNode.
B) Set dfs.block.size to 128M on all the worker nodes and client machines, and set the parameter to final. You do need to set this value on the NameNode.
C) Set dfs.block.size to 128M on all the worker nodes, on all client machines, and on the NameNode, and set the parameter to final.
D) Set dfs.block.size to 134217728 on all the worker nodes, on all client machines, and on the NameNode, and set the parameter to final.
E) You cannot enforce this, since client code can always override this value.

2. You have converted your Hadoop cluster from a MapReduce 1 (MRv1) architecture to a MapReduce 2 (MRv2) on YARN architecture. Your developers are accustomed to specifying map and reduce tasks (resource allocation) tasks when they run jobs. A developer wants to know how specify to reduce tasks when a specific job runs. Which method should you tell that developer to implement?

A) In YARN, resource allocation is a function of virtual cores specified by the ApplicationMaster making requests to the NodeManager where a reduce task is handled by a single container (and this a single virtual core). Thus, the developer needs to specify the number of virtual cores to the NodeManager by executing -p yarn.nodemanager.cpuvcores=2
B) In YARN, resource allocation is a function of megabytes of memory in multiple of 1024mb. Thus, they should specify the amount of memory resource they need by executing -D mapreduce.reduce.memory-mp-2040
C) In YARN, the ApplicationMaster is responsible for requesting the resources required for a specific job. Thus, executing -p yarn.applicationmaster.reduce.tasks-2 will specify that the ApplicationMaster launch two task containers on the worker nodes.
D) Developers specify reduce tasks in the exact same way for both MapReduce version 1 (MRv1) and MapReduce version 2 (MRv2) on YARN. Thus, executing -p mapreduce.job.reduce-2 will specify 2 reduce tasks.
E) MapReduce version 2 (MRv2) on YARN abstracts resource allocation away from the idea of "tasks" into memory and virtual cores, thus eliminating the need for a developer to specify the number of reduce tasks, and indeed preventing the developer from specifying the number of reduce tasks.

3. ---
Your cluster has the following characteristics:
A rack aware topology is configured and on
Replication is not set to 3
Cluster block size is set to 64 MB
Which describes the file read process when a client application connects into the cluster and requests a 50MB file?

A) The client queries the NameNode for the locations of the block, and reads all three copies. The first copy to complete transfer to the client is the one the client reads as part of Hadoop's speculative execution framework.
B) The client queries the NameNode which retrieves the block from the nearest DataNode to the client and then passes that block back to the client.
C) The client queries the NameNode for the locations of the block, and reads from a random location in the list it retrieves to eliminate network I/O leads by balancing which nodes it retrieves data from at any given time.
D) The client queries the NameNode for the locations of the block, and reads from the first location in the list it receives.

4. During the execution of a MapReduce v2 (MRv2) job on YARN, where does the Mapper place the intermediate data each Map task?

A) The Mapper stores the intermediate data on the mode running the job's ApplicationMaster so that is available to YARN's ShuffleService before the data is presented to the Reducer
B) The Mapper stores the intermediate data on the underlying filesystem of the local disk in the directories yarn.nodemanager.local-dirs
C) YARN holds the intermediate data in the NodeManager's memory (a container) until it is transferred to the Reducers
D) The Mapper transfers the intermediate data immediately to the Reducers as it generated by the Map task
E) The Mapper stores the intermediate data in HDFS on the node where the MAP tasks ran in the HDFS /usercache/&[user]sppcache/application_&(appid) directory for the user who ran the job

5. Your Hadoop cluster contains nodes in three racks. You have NOT configured the dfs.hosts property in the NameNode's configuration file. What results?

A) The NameNode will update the dfs.hosts property to include machine running DataNode daemon on the next NameNode reboot or with the command dfsadmin -refreshNodes
B) Presented with a blank dfs.hosts property, the NameNode will permit DatNode specified in mapred.hosts to join the cluster
C) Any machine running the DataNode daemon can immediately join the cluster
D) No new nodes can be added to the cluster until you specify them in the dfs.hosts file

問題與答案：