CCD-470 V8.02_formatted

Transcription

1 CCD-470 V8.02_formatted Number: Passing Score: 800 Time Limit: 120 min File Version: Exam : CCD-470 Title : Cloudera Certified Developer for Apache Hadoop CDH4 Upgrade Exam (CCDH) Version : V8.02

2 Exam A QUESTION 1 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. /Reference: QUESTION 2 Which 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 directory 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. /Reference: QUESTION 3 You are developing a combiner that takes as input Text keys, IntWritable values, and emits Text keys, IntWritable values. Which interface should your class implement? A. Combiner <Text, IntWritable, Text, IntWritable> B. Mapper <Text, IntWritable, Text, IntWritable> C. Reducer <Text, Text, IntWritable, IntWritable> D. Reducer <Text, IntWritable, Text, IntWritable> E. Combiner <Text, Text, IntWritable, IntWritable> /Reference:

3 QUESTION 4 Indentify the utility that allows you to create and run MapReduce jobs with any executable or script as the mapper and/or the reducer? A. Oozie B. Sqoop C. Flume D. Hadoop Streaming E. mapred /Reference: QUESTION 5 How are keys and values presented and passed to the reducers during a standard sort and shuffle phase of MapReduce? A. Keys are presented to reducer in sorted order; values for a given key are not sorted. B. Keys are presented to reducer in sorted order; values for a given key are sorted in ascending order. C. Keys are presented to a reducer in random order; values for a given key are not sorted. D. Keys are presented to a reducer in random order; values for a given key are sorted in ascending order. /Reference: QUESTION 6 Assuming default settings, which best describes the order of data provided to a reducer s reduce method: A. The keys given to a reducer aren t in a predictable order, but the values associated with those keys always are. B. Both the keys and values passed to a reducer always appear in sorted order. C. Neither keys nor values are in any predictable order. D. The keys given to a reducer are in sorted order but the values associated with each key are in no predictable order

4 /Reference: QUESTION 7 You wrote a map function that throws a runtime exception when it encounters a control character in input data. The input supplied to your mapper contains twelve such characters totals, spread across five file splits. The first four file splits each have two control characters and the last split has four control characters. Indentify the number of failed task attempts you can expect when you run the job with mapred.max.map.attempts set to 4: A. You will have forty-eight failed task attempts B. You will have seventeen failed task attempts C. You will have five failed task attempts D. You will have twelve failed task attempts E. You will have twenty failed task attempts Correct Answer: E /Reference: QUESTION 8 You want to populate an associative array in order to perform a map-side join. You?v decided to put this information in a text file, place that file into the Distributed Cache and read it in your Mapper before any records are processed. Indentify which method in the Mapper you should use to implement code for reading the file and populating the associative array? A. combine B. map C. init D. configure /Reference: QUESTION 9 You ve written a MapReduce job that will process 500 million input records and generated 500 million key-value pairs. The data is not uniformly distributed. Your MapReduce job will create a significant amount of intermediate data that it needs to transfer between mappers and reduces which is a potential bottleneck. A custom implementation of which interface is most likely to reduce the amount of intermediate data transferred across the network? A. Partitioner B. OutputFormat C. WritableComparable D. Writable E. InputFormat F. Combiner

5 Correct Answer: F /Reference: QUESTION 10 Can you use MapReduce to perform a relational join on two large tables sharing a key? Assume that the two tables are formatted as comma-separated files in HDFS. A. Yes. B. Yes, but only if one of the tables fits into memory C. Yes, so long as both tables fit into memory. D. No, MapReduce cannot perform relational operations. E. No, but it can be done with either Pig or Hive. /Reference: QUESTION 11 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 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. /Reference: QUESTION 12 You want to understand more about how users browse your public website, such as which pages they visit prior to placing an order. You have a farm of 200 web servers hosting your website. How will you gather this data for your analysis? A. Ingest the server web logs into HDFS using Flume. B. Write a MapReduce job, with the web servers for mappers, and the Hadoop cluster nodes for reduces. C. Import all users clicks from your OLTP databases into Hadoop, using Sqoop. D. Channel these clickstreams inot Hadoop using Hadoop Streaming. E. Sample the weblogs from the web servers, copying them into Hadoop using curl.

6 Correct Answer: B /Reference: QUESTION 13 MapReduce v2 (MRv2/YARN) is designed to address which two issues? A. Single point of failure in the NameNode. B. Resource pressure on the JobTracker. C. HDFS latency. D. Ability to run frameworks other than MapReduce, such as MPI. E. Reduce complexity of the MapReduce APIs. F. Standardize on a single MapReduce API. Correct Answer: BD /Reference: QUESTION 14 You need to run the same job many times with minor variations. Rather than hardcoding all job configuration options in your drive code, you ve decided to have your Driver subclass org.apache.hadoop.conf.configured and implement the org.apache.hadoop.util.tool interface. Indentify which invocation correctly passes.mapred.job.name with a value of Example to Hadoop? A. hadoop mapred.job.name=example MyDriver input output B. hadoop MyDriver mapred.job.name=example input output C. hadoop MyDrive D mapred.job.name=example input output D. hadoop setproperty mapred.job.name=example MyDriver input output E. hadoop setproperty ( mapred.job.name=example ) MyDriver input output /Reference: QUESTION 15 You are developing a MapReduce job for sales reporting. The mapper will process input keys representing the year (IntWritable) and input values representing product indentifies (Text). Indentify what determines the data types used by the Mapper for a given job. A. The key and value types specified in the JobConf.setMapInputKeyClass and JobConf.setMapInputValuesClass methods B. The data types specified in HADOOP_MAP_DATATYPES environment variable C. The mapper-specification.xml file submitted with the job determine the mapper s input key and value types. D. The InputFormat used by the job determines the mapper s input key and value types.

7 /Reference: QUESTION 16 Identify the MapReduce v2 (MRv2 / YARN) daemon responsible for launching application containers and monitoring application resource usage? A. ResourceManager B. NodeManager C. ApplicationMaster D. ApplicationMasterService E. TaskTracker F. JobTracker /Reference: QUESTION 17 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 Record Reader 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 Record Readers of both splits containing the broken line. C. The input file is split exactly at the line breaks, so each Record Reader 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. Correct Answer: E /Reference: QUESTION 18 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 keyvalue 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 keyvalue 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.

8 Correct Answer: E /Reference: QUESTION 19 You have the following key-value pairs as output from your Map task: (the, 1) (fox, 1) (faster, 1) (than, 1) (the, 1) (dog, 1) How many keys will be passed to the Reducer s reduce method? A. Six B. Five C. Four D. Two E. One F. Three /Reference: QUESTION 20 You have user profile records in your OLPT database, that you want to join with web logs you have already ingested into the Hadoop file system. How will you obtain these user records? A. HDFS command B. Pig LOAD command C. Sqoop import D. Hive LOAD DATA command E. Ingest with Flume agents F. Ingest with Hadoop Streaming Correct Answer: B /Reference: QUESTION 21 What is the disadvantage of using multiple reducers with the default HashPartitioner and distributing your workload across you cluster? A. You will not be able to compress the intermediate data. B. You will longer be able to take advantage of a Combiner.

9 C. By using multiple reducers with the default HashPartitioner, output files may not be in globally sorted order. D. There are no concerns with this approach.it is always advisable to use multiple reduces. /Reference: QUESTION 22 Given a directory of files with the following structure: line number, tab character, string: Example: A. B. C. D. Correct Answer: /Reference: QUESTION 23 abialkjfjkaoasdfjksdlkjhqweroij A. B. C. D. Correct Answer: /Reference: QUESTION 24 kadfjhuwqounahagtnbvaswslmnbfgy A. B. C. D. Correct Answer: /Reference:

10 QUESTION 25 kjfteiomndscxeqalkzhtopedkfsikj You want to send each line as one record to your Mapper. Which InputFormat should you use to complete the line: conf.setinputformat (.class) ;? A. SequenceFileAsTextInputFormat B. SequenceFileInputFormat C. KeyValueFileInputFormat D. BDBInputFormat Correct Answer: B /Reference: QUESTION 26 You need to perform statistical analysis in your MapReduce job and would like to call methods in the Apache Commons Math library, which is distributed as a 1.3 megabyte Java archive (JAR) file. Which is the best way to make this library available to your MapReducer job at runtime? A. Have your system administrator copy the JAR to all nodes in the cluster and set its location in the HADOOP_CLASSPATH environment variable before you submit your job. B. Have your system administrator place the JAR file on a Web server accessible to all cluster nodes and then set the HTTP_JAR_URL environment variable to its location. C. When submitting the job on the command line, specify the libjars option followed by the JAR file path. D. Package your code and the Apache Commands Math library into a zip file named JobJar.zip /Reference: QUESTION 27 The Hadoop framework provides a mechanism for coping with machine issues such as faulty configuration or impending hardware failure. MapReduce detects that one or a number of machines are performing poorly and starts more copies of a map or reduce task. All the tasks run simultaneously and the task finish first are used. This is called: A. Combine B. IdentityMapper C. IdentityReducer D. Default Partitioner E. Speculative Execution Correct Answer: E /Reference:

11 QUESTION 28 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. Correct Answer: E /Reference: QUESTION 29 What data does a Reducer reduce method process? A. All the data in a single input file. B. All data produced by a single mapper. C. All data for a given key, regardless of which mapper(s) produced it. D. All data for a given value, regardless of which mapper(s) produced it. /Reference: QUESTION 30 All keys used for intermediate output from mappers must: A. Implement a splittable compression algorithm. B. Be a subclass of FileInputFormat. C. Implement WritableComparable. D. Override issplitable. E. Implement a comparator for speedy sorting. /Reference: QUESTION 31 On a cluster running MapReduce v1 (MRv1), a TaskTracker heartbeats into the JobTracker on your cluster, and alerts the JobTracker it has an open map task slot. What determines how the JobTracker assigns each map task to a TaskTracker?

12 A. The amount of RAM installed on the TaskTracker node. B. The amount of free disk space on the TaskTracker node. C. The number and speed of CPU cores on the TaskTracker node. D. The average system load on the TaskTracker node over the past fifteen (15) minutes. E. The location of the InsputSplit to be processed in relation to the location of the node. Correct Answer: E /Reference: QUESTION 32 What is a SequenceFile? A. A SequenceFile contains a binary encoding of an arbitrary number of homogeneous Writable objects B. A SequenceFile contains a binary encoding of an arbitrary number of heterogeneous Writable objects C. A SequenceFile contains a binary encoding of an arbitrary number of WritableComparable objects, in sorted order. D. A SequenceFile contains a binary encoding of an arbitrary number key-value pairs.each key must be the same type.each value must be the same type. /Reference: QUESTION 33 A client application creates an HDFS file named foo.txt with a replication factor of 3. Identify which best describes the file access rules in HDFS if the file has a single block that is stored on data nodes A, B and C? A. The file will be marked as corrupted if data node B fails during the creation of the file. B. Each data node locks the local file to prohibit concurrent readers and writers of the file. C. Each data node stores a copy of the file in the local file system with the same name as the HDFS file. D. The file can be accessed if at least one of the data nodes storing the file is available. /Reference: QUESTION 34 In a MapReduce job, you want each of your input files processed by a single map task. How do you configure a MapReduce job so that a single map task processes each input file regardless of how many blocks the input file occupies? A. Increase the parameter that controls minimum split size in the job configuration. B. Write a custom MapRunner that iterates over all key-value pairs in the entire file. C. Set the number of mappers equal to the number of input files you want to process. D. Write a custom FileInputFormat and override the method issplitable to always return false.

13 /Reference: QUESTION 35 Which process describes the lifecycle of a Mapper? A. The JobTracker calls the TaskTracker s configure () method, then its map () method and finally its close () method. B. The TaskTracker spawns a new Mapper to process all records in a single input split. C. The TaskTracker spawns a new Mapper to process each key-value pair. D. The JobTracker spawns a new Mapper to process all records in a single file. /Reference: QUESTION 36 Determine which best describes when the reduce method is first called in a MapReduce job? A. Reducers start copying intermediate key-value pairs from each Mapper as soon as it has completed.the programmer can configure in the job what percentage of the intermediate data should arrive before the reduce method begins. B. Reducers start copying intermediate key-value pairs from each Mapper as soon as it has completed.the reduce method is called only after all intermediate data has been copied and sorted. C. Reduce methods and map methods all start at the beginning of a job, in order to provide optimal performance for map-only or reduce-only jobs. D. Reducers start copying intermediate key-value pairs from each Mapper as soon as it has completed.the reduce method is called as soon as the intermediate key-value pairs start to arrive. /Reference: QUESTION 37 You have written a Mapper which invokes the following five calls to the OutputColletor.collect method: output.collect (new Text ( Apple ), new Text ( Red ) ) ; output.collect (new Text ( Banana ), new Text ( Yellow ) ) ; output.collect (new Text ( Apple ), new Text ( Yellow ) ) ; output.collect (new Text ( Cherry ), new Text ( Red ) ) ; output.collect (new Text ( Apple ), new Text ( Green ) ) ; How many times will the Reducer s reduce method be invoked? A. 6 B. 3 C. 1 D. 0 E. 5

14 Correct Answer: B /Reference: QUESTION 38 To process input key-value pairs, your mapper needs to lead a 512 MB data file in memory. What is the best way to accomplish this? A. Serialize the data file, insert in it the JobConf object, and read the data into memory in the configure method of the mapper. B. Place the data file in the DistributedCache and read the data into memory in the map method of the mapper. C. Place the data file in the DataCache and read the data into memory in the configure method of the mapper. D. Place the data file in the DistributedCache and read the data into memory in the configure method of the mapper. Correct Answer: B /Reference: QUESTION 39 In a MapReduce job, the reducer receives all values associated with same key. Which statement best describes the ordering of these values? A. The values are in sorted order. B. The values are arbitrarily ordered, and the ordering may vary from run to run of the same MapReduce job. C. The values are arbitrary ordered, but multiple runs of the same MapReduce job will always have the same ordering. D. Since the values come from mapper outputs, the reducers will receive contiguous sections of sorted values. Correct Answer: B /Reference: QUESTION 40 You need to create a job that does frequency analysis on input data. You will do this by writing a Mapper that uses TextInputFormat and splits each value (a line of text from an input file) into individual characters. For each one of these characters, you will emit the character as a key and an InputWritable as the value. As this will produce proportionally more intermediate data than input data, which two resources should you expect to be bottlenecks? A. Processor and network I/O B. Disk I/O and network I/O C. Processor and RAM D. Processor and disk I/O

15 Correct Answer: B /Reference: QUESTION 41 You want to count the number of occurrences for each unique word in the supplied input data. You?v decided to implement this by having your mapper tokenize each word and emit a literal value 1, and then have your reducer increment a counter for each literal 1 it receives. After successful implementing this, it occurs to you that you could optimize this by specifying a combiner. Will you be able to reuse your existing Reduces as your combiner in this case and why or why not? A. Yes, because the sum operation is both associative and commutative and the input and output types to the reduce method match. B. No, because the sum operation in the reducer is incompatible with the operation of a Combiner. C. No, because the Reducer and Combiner are separate interfaces. D. No, because the Combiner is incompatible with a mapper which doesn t use the same data type for both the key and value. E. Yes, because Java is a polymorphic object-oriented language and thus reducer code can be reused as a combiner. /Reference: QUESTION 42 Your client application submits a MapReduce job to your Hadoop cluster. Identify the Hadoop daemon on which the Hadoop framework will look for an available slot schedule a MapReduce operation. A. TaskTracker B. NameNode C. DataNode D. JobTracker E. Secondary NameNode /Reference: QUESTION 43 Which project gives you a distributed, Scalable, data store that allows you random, realtime read/write access to hundreds of terabytes of data? A. HBase B. Hue C. Pig D. Hive

16 E. Oozie F. Flume G. Sqoop /Reference: QUESTION 44 You use the hadoop fs put command to write a 300 MB file using and HDFS block size of 64 MB. Just after this command has finished writing 200 MB of this file, what would another user see when trying to access this life? A. They would see Hadoop throw an ConcurrentFileAccessException when they try to access this file. B. They would see the current state of the file, up to the last bit written by the command. C. They would see the current of the file through the last completed block. D. They would see no content until the whole file written and closed. /Reference: QUESTION 45 Identify the tool best suited to import a portion of a relational database every day as files into HDFS, and generate Java classes to interact with that imported data? A. Oozie B. Flume C. Pig D. Hue E. Hive F. Sqoop G. fuse-dfs Correct Answer: F /Reference: QUESTION 46 You have a directory named jobdata in HDFS that contains four files: _first.txt, second.txt,.third.txt and #data.txt. How many files will be processed by the FileInputFormat.setInputPaths () command when it's given a path object representing this directory? A. Four, all files will be processed B. Three, the pound sign is an invalid character for HDFS file names C. Two, file names with a leading period or underscore are ignored

17 D. None, the directory cannot be named jobdata E. One, no special characters can prefix the name of an input file /Reference: QUESTION 47 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. /Reference: QUESTION 48 A combiner reduces: A. The number of values across different keys in the iterator supplied to a single reduce method call. B. The amount of intermediate data that must be transferred between the mapper and reducer. C. The number of input files a mapper must process. D. The number of output files a reducer must produce. Correct Answer: B /Reference: QUESTION 49 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.

18 /Reference: QUESTION 50 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 Correct Answer: BD /Reference: QUESTION 51 What types of algorithms are difficult to express in MapReduce v1 (MRv1)? A. Algorithms that require applying the same mathematical function to large numbers of individual binary records. B. Relational operations on large amounts of structured and semi-structured data. C. Algorithms that require global, sharing states. D. Large-scale graph algorithms that require one-step link traversal. E. Text analysis algorithms on large collections of unstructured text (e.g, Web crawls). /Reference: QUESTION 52 In the reducer, the MapReduce API provides you with an iterator over Writable values. What does calling the next () method return? A. It returns a reference to a different Writable object time. B. It returns a reference to a Writable object from an object pool. C. It returns a reference to the same Writable object each time, but populated with different data. D. It returns a reference to a Writable object. The API leaves unspecified whether this is a reused object or a new object. E. It returns a reference to the same Writable object if the next value is the same as the previous value, or a new Writable object otherwise.

19 /Reference: QUESTION 53 Table metadata in Hive is: A. Stored as metadata on the NameNode. B. Stored along with the data in HDFS. C. Stored in the Metastore. D. Stored in ZooKeeper. /Reference: QUESTION 54 Analyze each scenario below and indentify which best describes the behavior of the default partitioner? A. The default partitioner assigns key-values pairs to reduces based on an internal random number generator. B. The default partitioner implements a round-robin strategy, shuffling the key-value pairs to each reducer in turn. This ensures an event partition of the key space. C. The default partitioner computers the hash of the key. Hash values between specific ranges are associated with different buckets, and each bucket is assigned to a specific reducer. D. The default partitioner computers the hash of the key and divides that valule modulo the number of reducers. The result determines the reducer assigned to process the key-value pair. E. The default partitioner computers the hash of the value and takes the mod of that value with the number of reducers. The result determines the reducer assigned to process the key-value pair. /Reference: QUESTION 55 You need to move a file titled weblogs into HDFS. When you try to copy the file, you can t. You know you have ample space on your DataNodes. Which action should you take to relieve this situation and store more files in HDFS? A. Increase the block size on all current files in HDFS. B. Increase the block size on your remaining files. C. Decrease the block size on your remaining files. D. Increase the amount of memory for the NameNode. E. Increase the number of disks (or size) for the NameNode. F. Decrease the block size on all current files in HDFS.

20 /Reference: QUESTION 56 In a large MapReduce job with m mappers and n reducers, how many distinct copy operations will there be in the sort/shuffle phase? A. mxn (i.e., m multiplied by n) B. n C. m D. m+n (i.e., m plus n) E. m+n (i.e., m to the power of n) /Reference: QUESTION 57 Workflows expressed in Oozie can contain: A. Sequences of MapReduce and Pig. These sequences can be combined with other actions including forks, decision points, and path joins. B. Sequences of MapReduce job only; on Pig on Hive tasks or jobs. These MapReduce sequences can be combined with forks and path joins. C. Sequences of MapReduce and Pig jobs. These are limited to linear sequences of actions with exception handlers but no forks. D. Iterntive repetition of MapReduce jobs until a desired answer or state is reached. /Reference: QUESTION 58 Which best describes what the map method accepts and emits? A. It accepts a single key-value pair as input and emits a single key and list of corresponding values as output. B. It accepts a single key-value pairs as input and can emit only one key-value pair as output. C. It accepts a list key-value pairs as input and can emit only one key-value pair as output.

21 D. It accepts a single key-value pairs as input and can emit any number of key-value pair as output, including zero. /Reference: QUESTION 59 When can a reduce class also serve as a combiner without affecting the output of a MapReduce program? A. When the types of the reduce operation s input key and input value match the types of the reducer s output key and output value and when the reduce operation is both communicative and associative. B. When the signature of the reduce method matches the signature of the combine method. C. Always. Code can be reused in Java since it is a polymorphic object-oriented programming language. D. Always. The point of a combiner is to serve as a mini-reducer directly after the map phase to increase performance. E. Never. Combiners and reducers must be implemented separately because they serve different purposes. /Reference: QUESTION 60 You want to perform analysis on a large collection of images. You want to store this data in HDFS and process it with MapReduce but you also want to give your data analysts and data scientists the ability to process the data directly from HDFS with an interpreted high-level programming language like Python. Which format should you use to store this data in HDFS? A. SequenceFiles B. Avro C. JSON D. HTML E. XML F. CSV /Reference: QUESTION 61 You want to run Hadoop jobs on your development workstation for testing before you submit them to your production cluster. Which mode of operation in Hadoop allows you to most closely simulate a production cluster while using a single machine? A. Run all the nodes in your production cluster as virtual machines on your development workstation. B. Run the hadoop command with the jt local and the fs file:///options.

22 C. Run the DataNode, TaskTracker, NameNode and JobTracker daemons on a single machine. D. Run simldooop, the Apache open-source software for simulating Hadoop clusters. /Reference: QUESTION 62 Your cluster s HDFS block size in 64MB. You have directory containing 100 plain text files, each of which is 100MB in size. The InputFormat for your job is TextInputFormat.Determine how many Mappers will run? A. 64 B. 100 C. 200 D. 640 /Reference: QUESTION 63 Which of the following best describes the workings of TextInputFormat? A. Input file splits may cross line breaks. A line that crosses tile splits is ignored. B. The input file is split exactly at the line breaks, so each Record Reader will read a series of complete lines. C. 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. D. 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. 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 beginning of the broken line. /Reference: QUESTION 64 Which of the following statements most accurately describes the relationship between MapReduce and Pig? A. Pig provides additional capabilities that allow certain types of data manipulation not possible with MapReduce. B. Pig provides no additional capabilities to MapReduce.Pig programs are executed as MapReduce jobs via the Pig interpreter. C. Pig programs rely on MapReduce but are extensible, allowing developers to do special-purpose processing not provided by MapReduce.

23 D. Pig provides the additional capability of allowing you to control the flow of multiple MapReduce jobs. /Reference: QUESTION 65 You need to import a portion of a relational database every day as files to HDFS, and generate Java classes to Interact with your imported data. Which of the following tools should you use to accomplish this? A. Pig B. Hue C. Hive D. Flume E. Sqoop F. Oozie G. fuse-dfs Correct Answer: E /Reference: QUESTION 66 You have an employee who is a Date Analyst and is very comfortable with SQL.He would like to run ad-hoc analysis on data in your HDFS duster. Which of the following is a data warehousing software built on top of Apache Hadoop that defines a simple SQL-like query language well-suited for this kind of user? A. Pig B. Hue C. Hive D. Sqoop E. Oozie F. Flume G. Hadoop Streaming /Reference: QUESTION 67 What is the preferred way to pass a small number of configuration parameters to a mapper or reducer? A. As key-value pairs in the jobconf object. B. As a custom input key-value pair passed to each mapper or reducer.

24 C. Using a plain text file via the Distributedcache, which each mapper or reducer reads. D. Through a static variable in the MapReduce driver class (i.e., the class that submits the MapReduce job). /Reference: QUESTION 68 Given a Mapper, Reducer, and Driver class packaged into a jar, which is the correct way of submitting the job to the cluster? A. jar MyJar.jar B. jar MyJar.jar MyDriverClass inputdir outputdir C. hadoop jar MyJar.jar MyDriverClass inputdir outputdir D. hadoop jar class MyJar.jar MyDriverClass inputdir outputdir /Reference: QUESTION 69 What is the difference between a failed task attempt and a killed task attempt? A. A failed task attempt is a task attempt that threw an unhandled exception. A killed task attempt is one that was terminated by the JobTracker. B. A failed task attempt is a task attempt that did not generate any key value pairs. A killed task attempt is a task attempt that threw an exception, and thus killed by the execution framework. C. A failed task attempt is a task attempt that completed, but with an unexpected status value. A killed task attempt is a duplicate copy of a task attempt that was started as part of speculative execution. D. A failed task attempt is a task attempt that threw a Runtime Exception (i.e., the task fails).a killed task attempt is a task attempt that threw any other type of exception (e.g., IOException); the execution framework catches these exceptions and reports them as killed. /Reference: QUESTION 70 Custom programmer-defined counters in MapReduce are: A. Lightweight devices for bookkeeping within MapReduce programs. B. Lightweight devices for ensuring the correctness of a MapReduce program.mappers Increment counters, and reducers decrement counters. If at the end of the program the counters read zero, then you are sure that the job completed correctly. C. Lightweight devices for synchronization within MapReduce programs. You can use counters to coordinate execution between a mapper and a reducer.

25 /Reference: QUESTION 71 MapReduce is well-suited for all of the following applications EXCEPT? (Choose one): A. Text mining on a large collections of unstructured documents. B. Analysis of large amounts of Web logs (queries, clicks, etc.). C. Online transaction processing (OLTP) for an e-commerce Website. D. Graph mining on a large social network (e.g., Facebook friends network). /Reference: QUESTION 72 Your Custer s HOFS block size is 64MB. You have a directory containing 100 plain text files, each of which Is 100MB in size. The InputFormat for your job is TextInputFormat. How many Mappers will run? A. 64 B. 100 C. 200 D. 640 /Reference: QUESTION 73 Does the MapReduce programming model provide a way for reducers to communicate with each other? A. Yes, all reducers can communicate with each other by passing information through the jobconf object. B. Yes, reducers can communicate with each other by dispatching intermediate key value pairs that get shuffled to another reduce C. Yes, reducers running on the same machine can communicate with each other through shared memory, but not reducers on different machines. D. No, each reducer runs independently and in isolation. /Reference:

26 QUESTION 74 Which of the following best describes the map method input and output? A. It accepts a single key-value pair as input and can emit only one key-value pair as output. B. It accepts a list of key-value pairs as input hut run emit only one key value pair as output. C. It accepts a single key-value pair as input and emits a single key and list of corresponding values as output D. It accepts a single key-value pair as input and can emit any number of key-value pairs as output, including zero. /Reference: QUESTION 75 Your client application submits a MapReduce job to your Hadoop cluster. The Hadoop framework looks for an available slot to schedule the MapReduce operations on which of the following Hadoop computing daemons? A. DataNode B. NameNode C. JobTracker D. TaskTracker E. Secondary NameNode /Reference: QUESTION 76 Which MapReduce daemon runs on each slave node and participates in job execution? A. TaskTracker B. JobTracker C. NameNode D. Secondary NameNode /Reference: QUESTION 77 What is the standard configuration of slave nodes in a Hadoop cluster? A. Each slave node runs a JobTracker and a DataNode daemon. B. Each slave node runs a TaskTracker and a DataNode daemon. C. Each slave node either runs a TaskTracker or a DataNode daemon, but not both. D. Each slave node runs a DataNode daemon, but only a fraction of the slave nodes run TaskTrackers.

27 E. Each slave node runs a TaskTracker, but only a fraction of the slave nodes run DataNode daemons. Correct Answer: B /Reference: QUESTION 78 Which happens if the NameNode crashes? A. HDFS becomes unavailable until the NameNode is restored. B. The Secondary NameNode seamlessly takes over and there is no service interruption. C. HDFS becomes unavailable to new MapReduce jobs, but running jobs will continue until completion. D. HDFS becomes temporarily unavailable until an administrator starts redirecting client requests to the Secondary NameNode. /Reference: QUESTION 79 You are running a job that will process a single InputSplit on a cluster which has no other jobs currently running. Each node has an equal number of open Map slots. On which node will Hadoop first attempt to run the Map task? A. The node with the most memory B. The node with the lowest system load C. The node on which this InputSplit is stored D. The node with the most free local disk space /Reference: QUESTION 80 How does the NameNode detect that a DataNode has failed? A. The NameNode does not need to know that a DataNode has failed. B. When the NameNode fails to receive periodic heartbeats from the DataNode, it considers the DataNode as failed. C. The NameNode periodically pings the datanode. If the DataNode does not respond, the NameNode considers the DataNode as failed. D. When HDFS starts up, the NameNode tries to communicate with the DataNode and considers the DataNode as failed if it does not respond. Correct Answer: B

28 /Reference: QUESTION 81 The NameNode uses RAM for the following purpose: A. To store the contents of files in HDFS. B. To store filenames, list of blocks and other meta information. C. To store the edits log that keeps track of changes in HDFS. D. To manage distributed read and write locks on files in HDFS. Correct Answer: B /Reference: QUESTION 82 What is a Writable? A. Writable is an interface that all keys and values in MapReduce must implement. Classes implementing this interface must implement methods for serializing and deserializing themselves. B. Writable is an abstract class that all keys and values in MapReduce must extend. Classes extending this abstract base class must implement methods for serializing and deserializing themselves C. Writable is an interface that all keys, but not values, in MapReduce must implement. Classes implementing this interface must implement methods for serializing and deserializing themselves. D. Writable is an abstract class that all keys, but not values, in MapReduce must extend. Classes extending this abstract base class must implement methods for serializing and deserializing themselves. /Reference: QUESTION 83 During the standard sort and shuffle phase of MapReduce, keys and values are passed to reducers. Which of the following is true? A. Keys are presented to a reducer in sorted order; values for a given key are not sorted. B. Keys are presented to a reducer in soiled order; values for a given key are sorted in ascending order. C. Keys are presented to a reducer in random order; values for a given key are not sorted. D. Keys are presented to a reducer in random order; values for a given key are sorted in ascending order. /Reference: QUESTION 84 What is the behavior of the default partitioner?

29 A. The default partitioner assigns key value pairs to reducers based on an internal random number generator. B. The default partitioner implements a round robin strategy, shuffling the key value pairs to each reducer in turn. This ensures an even partition of the key space. C. The default partitioner computes the hash of the key.hash values between specific ranges are associated with different buckets, and each bucket is assigned to a specific reducer. D. The default partitioner computes the hash of the key and divides that value modulo the number of reducers. The result determines the reducer assigned to process the key-value pair. E. The default partitioner computes the hash of the value and takes the mod of that value with the number of reducers. The result determines the reducer assigned to process the key value pair. /Reference: QUESTION 85 Which statement best describes the data path of intermediate key-value pairs (i.e., output of the mappers)? A. Intermediate key-value pairs are written to HDFS.Reducers read the intermediate data from HDFS. B. Intermediate key-value pairs are written to HDFS.Reducers copy the intermediate data to the local disks of the machines running the reduce tasks. C. Intermediate key-value pairs are written to the local disks of the machines running the map tasks, and then copied to the machine running the reduce tasks. D. Intermediate key-value pairs are written to the local disks of the machines running the map tasks, and are then copied to HDFS.Reducers read the intermediate data from HDFS. /Reference: QUESTION 86 If you run the word count MapReduce program with m mappers and r reducers, how many output files will you get at the end of the job? And how many key-value pairs will there be in each file? Assume k is the number of unique words in the input files. A. There will be r files, each with exactly k/r key-value pairs. B. There will be r files, each with approximately k/m key-value pairs. C. There will be r files, each with approximately k/r key-value pairs. D. There will be m files, each with exactly k/m key value pairs. E. There will be m files, each with approximately k/m key-value pairs. /Reference: QUESTION 87 You have a large dataset of key-value pairs, where the keys are strings, and the values are integers. For each

30 unique key, you want to identify the largest integer. In writing a MapReduce program to accomplish this, can you take advantage of a combiner? A. No, a combiner would not be useful in this case. B. Yes. C. Yes, but the number of unique keys must be known in advance. D. Yes, as long as all the keys fit into memory on each node. E. Yes, as long as all the integer values that share the same key fit into memory on each node. Correct Answer: B /Reference: QUESTION 88 What happens in a MapReduce job when you set the number of reducers to zero? A. No reducer executes, but the mappers generate no output. B. No reducer executes, and the output of each mapper is written to a separate file in HDFS. C. No reducer executes, but the outputs of all the mappers are gathered together and written to a single file in HDFS. D. Setting the number of reducers to zero is invalid, and an exception is thrown. Correct Answer: B /Reference: QUESTION 89 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. /Reference: QUESTION 90 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

31 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) /Reference: QUESTION 91 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. /Reference: QUESTION 92 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. /Reference: QUESTION 93 Which two of the following are valid statements? (Choose 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.

32 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. Correct Answer: BD /Reference: QUESTION 94 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. /Reference: QUESTION 95 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. /Reference: QUESTION 96 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.