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2 Exam : HDPCD Title : Hortonworks Data Platform Certified Developer Vendor : Hortonworks Version : DEMO Get Latest & Valid HDPCD Exam's Question and Answers 1 from Actual4dumps.com. 1

3 NO.1 Given the following Hive commands: Which one of the following statements Is true? A. The file mydata.txt is copied to a subfolder of /apps/hive/warehouse B. The file mydata.txt is moved to a subfolder of /apps/hive/warehouse C. The file mydata.txt is copied into Hive's underlying relational database 0. D. The file mydata.txt does not move from Its current location in HDFS Answer: A NO.2 For each intermediate key, each reducer task can emit: A. As many final key-value pairs as desired. There are no restrictions on the types of those key-value pairs (i.e., they can be heterogeneous). B. As many final key-value pairs as desired, but they must have the same type as the intermediate key-value pairs. C. As many final key-value pairs as desired, as long as all the keys have the same type and all the values have the same type. D. One final key-value pair per value associated with the key; no restrictions on the type. E. One final key-value pair per key; no restrictions on the type. Answer: C Reference: Hadoop Map-Reduce Tutorial; Yahoo! Hadoop Tutorial, Module 4: MapReduce NO.3 You write MapReduce job to process 100 files in HDFS. Your MapReduce algorithm uses TextInputFormat: the mapper applies a regular expression over input values and emits key-values pairs with the key consisting of the matching text, and the value containing the filename and byte offset. Determine the difference between setting the number of reduces to one and settings the number of reducers to zero. A. There is no difference in output between the two settings. B. With zero reducers, no reducer runs and the job throws an exception. With one reducer, instances of matching patterns are stored in a single file on HDFS. C. With zero reducers, all instances of matching patterns are gathered together in one file on HDFS. With one reducer, instances of matching patterns are stored in multiple files on HDFS. D. With zero reducers, instances of matching patterns are stored in multiple files on HDFS. With one reducer, all instances of matching patterns are gathered together in one file on HDFS. Answer: D * It is legal to set the number of reduce-tasks to zero if no reduction is desired. In this case the outputs of the map-tasks go directly to the FileSystem, into the output path set by setoutputpath(path). The framework does not sort the map-outputs before writing them out to the FileSystem. Get Latest & Valid HDPCD Exam's Question and Answers 2 from Actual4dumps.com. 2

4 * Often, you may want to process input data using a map function only. To do this, simply set mapreduce.job.reduces to zero. The MapReduce framework will not create any reducer tasks. Rather, the outputs of the mapper tasks will be the final output of the job. Note: Reduce In this phase the reduce(writablecomparable, Iterator, OutputCollector, Reporter) method is called for each <key, (list of values)> pair in the grouped inputs. The output of the reduce task is typically written to the FileSystem via OutputCollector.collect(WritableComparable, Writable). Applications can use the Reporter to report progress, set application-level status messages and update Counters, or just indicate that they are alive. The output of the Reducer is not sorted. NO.4 Which best describes how TextInputFormat processes input files and line breaks? A. Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReader of the split that contains the beginning of the broken line. B. Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReaders of both splits containing the broken line. C. The input file is split exactly at the line breaks, so each RecordReader will read a series of complete lines. D. Input file splits may cross line breaks. A line that crosses file splits is ignored. E. Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReader of the split that contains the end of the broken line. Answer: A Reference: How Map and Reduce operations are actually carried out NO.5 In a MapReduce job with 500 map tasks, how many map task attempts will there be? A. It depends on the number of reduces in the job. B. Between 500 and C. At most 500. D. At least 500. E. Exactly 500. Answer: D From Cloudera Training Course: Task attempt is a particular instance of an attempt to execute a task - There will be at least as many task attempts as there are tasks - If a task attempt fails, another will be started by the JobTracker - Speculative execution can also result in more task attempts than completed tasks NO.6 You have just executed a MapReduce job. Where is intermediate data written to after being emitted from the Mapper's map method? A. Intermediate data in streamed across the network from Mapper to the Reduce and is never Get Latest & Valid HDPCD Exam's Question and Answers 3 from Actual4dumps.com. 3

5 written to disk. B. Into in-memory buffers on the TaskTracker node running the Mapper that spill over and are written into HDFS. C. Into in-memory buffers that spill over to the local file system of the TaskTracker node running the Mapper. D. Into in-memory buffers that spill over to the local file system (outside HDFS) of the TaskTracker node running the Reducer E. Into in-memory buffers on the TaskTracker node running the Reducer that spill over and are written into HDFS. Answer: C The mapper output (intermediate data) is stored on the Local file system (NOT HDFS) of each individual mapper nodes. This is typically a temporary directory location which can be setup in config by the hadoop administrator. The intermediate data is cleaned up after the Hadoop Job completes. Reference: 24 Interview Questions & Answers for Hadoop MapReduce developers, Where is the Mapper Output (intermediate kay-value data) stored? NO.7 Which one of the following classes would a Pig command use to store data in a table defined in HCatalog? A. org.apache.hcatalog.pig.hcatoutputformat B. org.apache.hcatalog.pig.hcatstorer C. No special class is needed for a Pig script to store data in an HCatalog table D. Pig scripts cannot use an HCatalog table Answer: B NO.8 For each input key-value pair, mappers can emit: A. As many intermediate key-value pairs as designed. There are no restrictions on the types of those key-value pairs (i.e., they can be heterogeneous). B. As many intermediate key-value pairs as designed, but they cannot be of the same type as the input key-value pair. C. One intermediate key-value pair, of a different type. D. One intermediate key-value pair, but of the same type. E. As many intermediate key-value pairs as designed, as long as all the keys have the same types and all the values have the same type. Answer: E Mapper maps input key/value pairs to a set of intermediate key/value pairs. Maps are the individual tasks that transform input records into intermediate records. The transformed intermediate records do not need to be of the same type as the input records. A given input pair may map to zero or many output pairs. Reference: Hadoop Map-Reduce Tutorial NO.9 Which one of the following statements describes the relationship between the NodeManager Get Latest & Valid HDPCD Exam's Question and Answers 4 from Actual4dumps.com. 4

6 and the ApplicationMaster? A. The ApplicationMaster starts the NodeManager in a Container B. The NodeManager requests resources from the ApplicationMaster C. The ApplicationMaster starts the NodeManager outside of a Container D. The NodeManager creates an instance of the ApplicationMaster Answer: D NO.10 MapReduce v2 (MRv2/YARN) splits which major functions of the JobTracker into separate daemons? Select two. A. Heath states checks (heartbeats) B. Resource management C. Job scheduling/monitoring D. Job coordination between the ResourceManager and NodeManager E. Launching tasks F. Managing file system metadata G. MapReduce metric reporting H. Managing tasks Answer: B,C The fundamental idea of MRv2 is to split up the two major functionalities of the JobTracker, resource management and job scheduling/monitoring, into separate daemons. The idea is to have a global ResourceManager (RM) and per-application ApplicationMaster (AM). An application is either a single job in the classical sense of Map- Reduce jobs or a DAG of jobs. Note: The central goal of YARN is to clearly separate two things that are unfortunately smushed together in current Hadoop, specifically in (mainly) JobTracker: / Monitoring the status of the cluster with respect to which nodes have which resources available. Under YARN, this will be global. / Managing the parallelization execution of any specific job. Under YARN, this will be done separately for each job. Reference: Apache Hadoop YARN - Concepts & Applications Get Latest & Valid HDPCD Exam's Question and Answers 5 from Actual4dumps.com. 5