CCA-505練習材料不僅適用於學生，也適用於上班族;不僅適用於工作的退伍軍人，也適用於新招募的新人。我們的學習材料使用非常簡單易懂的語言，以確保所有人都能學習和理解。 CCA-505真正的考試也可以讓你避免課本閱讀的枯燥，但讓你掌握練習過程中的所有重要知識。選擇CCA-505測試引擎的原因如下。

下載最新試用版

保證100％通過

我們相信所有購買CCA-505練習材料的學生只要能夠按照我們的學習材料提供的內容，每天進行學習，並通過模擬考試定期自我檢驗，就能順利通過專業資格考試。一旦您不幸使用我們的CCA-505真實考試題庫未通過考試，我們將全額退款。退款流程非常簡單。只要您向員工提供您的成績單，您很快就會收到退款。當然，在您購買之前，我們的學習資料將為您提供免費試用服務，只要您登錄我們的網站，您就可以免費下載我們的試用版。我相信在您嘗試CCA-505測試引擎後，您會愛上它們。

最省時最高效的學習方法

我們的CCA-505練習材料有三種不同版本：PDF，軟件版本和APP在線版本。它們為不同的考生提供了選擇其研究方法的可能性。如果您是辦公室工作人員，您可以在地鐵或公交車上學習CCA-505真實考試的在線版本；如果你是一名學生，你可以在排隊吃飯時復習；如果你是家庭主婦，你可以在孩子睡覺時學習。同時，我們的學習資料支持離線學習，避免了沒有網絡就無法學習的情況。同時，使用CCA-505測試引擎進行審核，讓您從標題中就能查看知識點，不僅可以讓您更深刻地記住知識點，還可以讓您避免閱讀書籍的枯燥過程。

語言易於理解

作為一個行業新人，專業書籍中那些難以理解的單詞和表達常常讓你感到懊惱，但CCA-505練習材料將幫助你完美地解決這個問題。由學習材料聘請的行業專家，將通過示例，圖表等解釋所有難以理解的專業詞彙。 CCA-505實際測試中使用的所有語言都非常簡單易懂。使用我們的學習資料，您不必擔心您無法理解解專業書籍的內容。你也不需要花費昂貴的學費去輔導班。 CCA-505測試引擎可以幫助您解決學習中的所有問題。

最新的 CCAH CCA-505 免費考試真題:

1. Assume you have a file named foo.txt in your local directory. You issue the following three commands:
Hadoop fs -mkdir input
Hadoop fs -put foo.txt input/foo.txt
Hadoop fs -put foo.txt input
What happens when you issue that third command?

A) The file is uploaded and stored as a plain named input
B) You get a warning that foo.txt is being overwritten
C) The write silently fails
D) You get a error message telling you that foo.txt already exists. The file is not written to HDFS
E) You get an error message telling you that input is not a directory
F) You get an error message telling you that foo.txt already exists, and asking you if you would like to overwrite
G) The write succeeds, overwriting foo.txt in HDFS with no warning

2. ---
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.

3. 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.

4. Which two are Features of Hadoop's rack topology?

A) HDFS is rack aware but MapReduce daemons are not
B) Hadoop gives preference to Intra rack data transfer in order to conserve bandwidth
C) Even for small clusters on a single rack, configuring rack awareness will improve performance.
D) Configuration of rack awareness is accomplished using a configuration file. You cannot use a rack topology script.
E) Rack location is considered in the HDFS block placement policy

5. You are configuring a cluster running HDFS, MapReduce version 2 (MRv2) on YARN
running Linux. How must you format the underlying filesystem of each DataNode?

A) They may be formatted in any Linux filesystem
B) They must be formatted as HDFS
C) They must be formatted as either ext3 or ext4
D) They must not formatted - - HDFS will format the filesystem automatically

問題與答案：