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CCD-410 Cloudera Certified Developer for Apache Hadoop (CCDH) Cloudera
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What is ccD-410 Certification Exam..?? Cloudera CCD-410 practice exam test questions cover ALL the Exam Objectives you will be tested on in order to pass your CCD-410 exam on your First Try! With FirstTryCertify CCD-410 questions and answers, you will successfully pass your CCD-410 exam and feel confident in obtaining success on your First Try. http://www.examcollectionvce.com/vce-ccD-410.html
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Information of ccD-410 Certification Exam… Vendor Cloudera Certifications Cloudera Certified Developer for Apache Hadoop (CCDH) Exam Code ccD-410 Total Questions 60 Q&As http://www.examcollectionvce.com/vce-ccD-410.html
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Cloudera CCD-410 Practice Exam Features: VVery Detailed Questions and Answers CCCD-410 PDF Questions and Answers Updated Frequently CCCD-410 PDF Practice Questions Verified by Expert Senior Certified Staff CCCD-410 Most Realistic Questions that Guarantee you a Pass on Your First Try CCCD-410 Practice Test Questions in Multiple Choice Formats and Updates for 1 Year http://www.examcollectionvce.com/vce-ccD-410.html
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Question Answer of Cloudera CCD-410 Practice Exam.. QUESTION: 1 Combiners Increase the efficiency of a MapReduce program because: A. They provide a mechanism for different mappers to communicate with each Other, thereby reducing synchronization overhead. B. They provide an optimization and reduce the total number of computations that are needed to execute an algorithm by a factor of n, where is the number of reducer. C. They aggregate intermediate map output locally on each individual machine and therefore reduce the amount of data that needs to be shuffled across the network to the reducers. D. They aggregate intermediate map output horn a small number of nearby (i.e., rack-local) machines and therefore reduce the amount of data that needs to be shuffled across the network to the reducers. Answer: C http://www.examcollectionvce.com/vce-ccD-410.html
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Question Answer of Cloudera CCD-410 Practice Exam.. QUESTION: 2 In a large MapReduce job with m mappers and r reducers, how many distinct copy operations will there be in the sort/shuffle phase? A. m B. r C. m+r (i.e., m plus r) D. mxr (i.e., m multiplied by r) E. mr (i.e., m to the power of r) Answer: D http://www.examcollectionvce.com/vce-ccD-410.html
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Question Answer of Cloudera CCD-410 Practice Exam.. QUESTION: 3 What happens in a MapReduce job when you set the number of reducers to one? A. A single reducer gathers and processes all the output from all the mappers. The output is written in as many separate files as there are mappers. B. A single reducer gathers and processes all the output from all the mappers. The output is written to a single file in HDFS. C. Setting the number of reducers to one creates a processing bottleneck, and since the number of reducers as specified by the programmer is used as a reference value only, the MapReduce runtime provides a default setting for the number of reducers. D. Setting the number of reducers to one is invalid, and an exception is thrown. Answer: A http://www.examcollectionvce.com/vce-ccD-410.html
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Question Answer of Cloudera CCD-410 Practice Exam.. QUESTION: 4 In the standard word count MapReduce algorithm, why might using a combiner reduce the overall Job running time? A. Because combiners perform local aggregation of word counts, thereby allowing the mappers to process input data faster. B. Because combiners perform local aggregation of word counts, thereby reducing the number of mappers that need to run. C. Because combiners perform local aggregation of word counts, and then transfer that data to reducers without writing the intermediate data to disk. D. Because combiners perform local aggregation of word counts, thereby reducing the number of key-value pairs that need to be snuff let across the network to the reducers. Answer: A http://www.examcollectionvce.com/vce-ccD-410.html
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Question Answer of Cloudera CCD-410 Practice Exam.. QUESTION: 5 Which two of the following are valid statements? (Select two) A. HDFS is optimized for storing a large number of files smaller than the HDFS block size. B. HDFS has the Characteristic of supporting a "write once, read many" data access model. C. HDFS is a distributed file system that replaces ext3 or ext4 on Linux nodes in a Hadoop cluster. D. HDFS is a distributed file system that runs on top of native OS filesystems and is well suited to storage of very large data sets. Answer: B, D http://www.examcollectionvce.com/vce-ccD-410.html
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Question Answer of Cloudera CCD-410 Practice Exam.. QUESTION: 6 You need to create a GUI application to help your company's sales people add and edit customer information. Would HDFS be appropriate for this customer information file? A. Yes, because HDFS is optimized for random access writes. B. Yes, because HDFS is optimized for fast retrieval of relatively small amounts of data. C. No, because HDFS can only be accessed by MapReduce applications. D. No, because HDFS is optimized for write-once, streaming access for relatively large files. Answer: D http://www.examcollectionvce.com/vce-ccD-410.html
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Question Answer of Cloudera CCD-410 Practice Exam.. QUESTION: 7 Which of the following describes how a client reads a file from HDFS? A. The client queries the NameNode for the block location(s). The NameNode returns the block location(s) to the client. The client reads the data directly off the DataNode(s). B. The client queries all DataNodes in parallel. The DataNode that contains the requested data responds directly to the client. The client reads the data directly off the DataNode. C. The client contacts the NameNode for the block location(s). The NameNode then queries the DataNodes for block locations. The DataNodes respond to the NameNode, and the NameNode redirects the client to the DataNode that holds the requested data block(s). The client then reads the data directly off the DataNode. D. The client contacts the NameNode for the block location(s). The NameNode contacts the DataNode that holds the requested data block. Data is transferred from the DataNode to the NameNode, and then from the NameNode to the client. Answer: C http://www.examcollectionvce.com/vce-ccD-410.html
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Question Answer of Cloudera CCD-410 Practice Exam.. QUESTION: 8 Which of the following statements best describes how a large (100 GB) file is stored in HDFS? A. The file is divided into variable size blocks, which are stored on multiple data nodes. Each block is replicated three times by default. B. The file is replicated three times by default. Each copy of the file is stored on a separate datanodes. C. The master copy of the file is stored on a single datanode. The replica copies are divided into fixed-size blocks, which are stored on multiple datanodes. D. The file is divided into fixed-size blocks, which are stored on multiple datanodes. Each block is replicated three times by default. Multiple blocks from the same file might reside on the same datanode. E. The file is divided into fixed-size blocks, which are stored on multiple datanodes. Each block is replicated three times by default. HDFS guarantees that different blocks from the same file are never on the same datanode. Answer: E http://www.examcollectionvce.com/vce-ccD-410.html
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Question Answer of Cloudera CCD-410 Practice Exam.. QUESTION: 9 Your cluster has 10 DataNodes, each with a single 1 TB hard drive. You utilize all your disk capacity for HDFS, reserving none for MapReduce. You implement default replication settings. What is the storage capacity of your Hadoop cluster (assuming no compression)? A. about 3 TB B. about 5 TB C. about 10 TB D. about 11 TB Answer: A http://www.examcollectionvce.com/vce-ccD-410.html
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Question Answer of Cloudera CCD-410 Practice Exam.. Question: 10 When is the earliest point at which the reduce method of a given Reducer can be called? A. As soon as at least one mapper has finished processing its input split. B. As soon as a mapper has emitted at least one record. C. Not until all mappers have finished processing all records. D. It depends on the InputFormat used for the job. Answer: C http://www.examcollectionvce.com/vce-ccD-410.html
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