is a combination of web analytics with hadoop mcq
This tool tries to subsequently even out the block data distribution across the cluster. Developers are cautioned to rarely use map-side joins. It is defined as a language-independent schema (written in JSON). If it is read first then no. NAS is a high-end storage device which includes a high cost. A - It is lost for ever. Active NameNode works and runs in the cluster. This and other engines are outlined below. Join our subscribers list to get the latest news, updates and special offers delivered directly in your inbox. The best performance expectation one can have is measured in milliseconds. Developers should design Map-Reduce jobs without reducers only if no reduce slots are available on the cluster. Yes, Avro was specifically designed for data processing via Map-Reduce, B. The MapReduce reducer has three phases: Ans. A Sequence Filecontains a binary encoding of an arbitrary number of homo geneous writable objects. D. Yes, but the limit is currently capped at 10 input paths. ASWDC (App, Software & Website Development Center) Darshan Institute of Engineering & Technology (DIET) B. That will completely disable the reduce step. D. Avro specifies metadata that allows easier data access. Ans. Reducers always run in isolation and the Hadoop Mapreduce programming paradigm never allows them to communicate with each other. Identity Mapper is a default Mapper class which automatically works when no Mapper is specified in the MapReduce driver class. Q2) Explain Big data and its characteristics. Counters are useful for collecting statistics about MapReduce jobs for application-level or quality control. Apache Flume is a service/tool/data ingestion mechanism used to collect, aggregate, and transfer massive amounts of streaming data such as events, log files, etc., from various web sources to a centralized data store where they can be processed together. Dear Readers, Welcome to Hadoop Objective Questions and Answers have been designed specially to get you acquainted with the nature of questions you may encounter during your Job interview for the subject of Hadoop Multiple choice Questions.These Objective type Hadoop are very important for campus placement test and job … B. Map files are the files that show how the data is distributed in the Hadoop cluster. D. Write a custom FileInputFormat and override the method isSplittable to always return false. It allocates the resources (containers) to various running applications based on resource availability and configured shared policy. A line that crosses file splits is read by the RecordReader of the split that contains the end of the brokenline. Ans. Schema of the data is known in RDBMS and it always depends on the structured data. The Hadoop administrator has to set the number of the reducer slot to zero on all slave nodes. Ans. Consider the replication factor is 3 for data blocks on HDFS it means for every block of data two copies are stored on the same rack, while the third copy is stored on a different rack. Distributed filesystems must always be resident in memory, which is much faster than disk. Client applications associate the Hadoop HDFS API with the NameNode when it has to copy/move/add/locate/delete a file. C. Yes, developers can add any number of input paths. This data can be either structured or unstructured data. A line that crosses file splits is read by the RecordReader of the split that contains the beginning of thebroken line. A. RDD(Resilient Distributed Datasets) is a fundamental data structure of Spark. C. Only Java supported since Hadoop was written in Java. Mahout is on the way out so you should not use that. B - It can be replicated form its alternative locations to other live machines.. C - The namenode allows new client request to keep trying to read it.. D - The Mapreduce job process runs ignoring the block and the data stored in it. Ans. The most common problem with map-side joins is lack of the avaialble map slots since map-side joins require a lot of mappers. The new NameNode will start serving the client once it has completed loading the last checkpoint FsImage and enough block reports from the DataNodes. Question3: I was told by my web analytics vendor that tagging my pages is easy. The process of translating objects or data structures state into binary or textual form is called Avro Serialization. B. HDFS (Hadoop Distributed File System) is the storage unit of Hadoop. C. No, but sequence file input format can read map files. Ans. It is a distributed collection of objects, and each dataset in RDD is further distributed into logical partitions and computed on several nodes of the cluster. Q22) List the different types of Hadoop schedulers. Build a new class that extends Partitioner Class. Q28) What is the main purpose of the Hadoop fsck command? A. They show the task distribution during job execution. Q27) What is a rack-aware replica placement policy? C. The TaskTracker spawns a new Mapper to process each key-value pair. Q 1 - In a Hadoop cluster, what is true for a HDFS block that is no longer available due to disk corruption or machine failure?. Hadoop is open source. It is used during reduce step. In order to give a balance to a certain threshold among data nodes, use the Balancer tool. A. Often binary data is added to a sequence file. Hadoop fsck command is used for checking the HDFS file system. It cannot be used as a key for example. B. Sequences of MapReduce and Pig jobs. Hadoop Questions and Answers has been designed with a special intention of helping students and professionals preparing for various Certification Exams and Job Interviews.This section provides a useful collection of sample Interview Questions and Multiple Choice Questions (MCQs) and their answers with appropriate explanations. The reduce method is called as soon as the intermediate key-value pairs start to arrive. The index allows fast data look up. Aspirants can also find the benefits of practicing the Web Services MCQ Online question and answers. Ans. A. Iterative repetition of MapReduce jobs until a desired answer or state is reached. The input file is split exactly at the line breaks, so each Record Reader will read a series of complete lines. HDFS divides data into blocks, whereas MapReduce divides data into input split and empower them to mapper function. SequenceFileInputFormat is the input format used for reading in sequence files. Data Mine Lab - Developing solutions based on Hadoop, Mahout, HBase and Amazon Web Services. C. Set the number of mappers equal to the number of input files you want to process. Apache HBase is multidimensional and a column-oriented key datastore runs on top of HDFS (Hadoop Distributed File System). No. Hadoop is an open-source framework used for storing large data sets and runs applications across clusters of commodity hardware. But, before starting, I would like to draw your attention to the Hadoop revolution in the market. C. Avro is a java library that create splittable files, A. Each key must be the same type. This is because Hadoop executes in parallel across so many machines, C. The best performance expectation one can have is measured in minutes. So, check all the parts and learn the new concepts of the Hadoop. Apache Hive offers a database query interface to Apache Hadoop. In Apache Hadoop, if nodes do not fix or diagnose the slow-running tasks, the master node can redundantly perform another instance of the same task on another node as a backup (the backup task is called a Speculative task). SerDe is a combination of Serializer and Deserializer. A serializable object which executes a simple and efficient serialization protocol, based on DataInput and DataOutput. Map-side join is done in the map phase and done in memory, B . Without much complex Java implementations in MapReduce, programmers can perform the same implementations very easily using Pig Latin. Apache Pig decreases the length of the code by approx 20 times (according to Yahoo). The Purpose of Distributed Cache in the MapReduce framework is to cache files when needed by the applications. The programmer can configure in the job what percentage of the intermediate data should arrive before the reduce method begins. D. Input file splits may cross line breaks. Ans. It periodically creates the checkpoints of filesystem metadata by merging the edits log file with FsImage file. Hadoop is open source. The basic parameters of Mapper are listed below: Ans. Kudu is specifically designed for use cases that require fast analytics on fast (rapidly changing) data. As the Hadoop Questions are part of various kind of examinations and interviews. Now, configure DataNodes and clients, so that they can acknowledge the new NameNode, that is started. Practice Hadoop MCQs Online Quiz Mock Test For Objective Interview. C. The distributed cache is a component that caches java objects. These free quiz questions will test your knowledge of Hadoop. It is important for MapReduce as in the sorting phase the keys are compared with one another. D. The most common problem with map-side join is not clearly specifying primary index in the join. It receives inputs from the Map class and passes the output key-value pairs to the reducer class. Being a framework, Hadoop is made up of several modules that are supported by a large ecosystem of technologies. Faster Analytics. HDFS Federation enhances the present HDFS architecture through a clear separation of namespace and storage by enabling a generic block storage layer. The TaskTracker spawns a new Mapper to process all records in a single input split. There is no default input format. MapReduce Programming model is language independent, Distributed programming complexity is hidden, Manages all the inter-process communication, The application runs in one or more containers, Job’s input and output locations in the distributed file system, Class containing the map function and reduce function, JAR file containing the reducer, driver, and mapper classes. The default input format is xml. These Objective type Hadoop are very important for campus placement test and job interviews. A. These sequences can be combined with other actions including forks, decision points, and path joins. The input format always should be specified. Q4) What is YARN and explain its components? Q17) How to decommission (removing) the nodes in the Hadoop cluster? Hadoop works better for large amounts of data. Any programming language that can comply with Map Reduce concept can be supported. Q19) What is the difference between active and passive NameNodes? Q6) What are the Hadoop daemons and explain their roles in a Hadoop cluster? In Hadoop 1.x, NameNode is the single point of failure. Ans. It provides multiple namespaces in the cluster to improve scalability and isolation. Ans. A developer may decide to limit to one reducer for debugging purposes. The distributed cache is special component on datanode that will cache frequently used data for faster client response. D. A Sequence Filecontains a binary encoding of an arbitrary number key-value pairs. Input file splits may cross line breaks. Hadoop MCQs – Big Data Science “Hadoop MCQs – Big Data Science” is the set of frequently asked Multiple Choice questions and these MCQs are asked in different test in the past in different test. DAS Log File Aggregator is a plug-in to DAS that makes it easy to import large numbers of log files stored on disparate servers. Each value must be sametype. A. customizable courses, self paced videos, on-the-job support, and job assistance. Dear Readers, Welcome to Hadoop Objective Questions and Answers have been designed specially to get you acquainted with the nature of questions you may encounter during your Job interview for the subject of Hadoop Multiple choice Questions. Ex: replication factors, block location, etc. So your best options are to use Flink either with Hadoop or Flink tables or use Spark ML (machine language) library with data stored in Hadoop or elsewhere and then store the results either in Spark or Hadoop. Ans. Replication factor means the minimum number of times the file will replicate(copy) across the cluster. HDFS Block is the physical division of the disk which has the minimum amount of data that can be read/write, while MapReduce InputSplit is the logical division of data created by the InputFormat specified in the MapReduce job configuration. MapReduce is a programming model used for processing and generating large datasets on the clusters with parallel and distributed algorithms. The job configuration requires the following: Ans. Finally, job status and diagnostic information are provided to the client. B. It is designed to work for the MapReduce paradigm. C. No, because the Reducer and Combiner are separate interfaces. It implements mapping inputs directly into the output. C. An arbitrarily sized list of key/value pairs. Characteristics of Big Data: Volume - It represents the amount of data that is increasing at an exponential rate i.e. A. Ans. However, it is not possible to limit a cluster from becoming unbalanced. D. Sequences of MapReduce and Pig. It is mainly responsible for managing a collection of submitted applications. Here, we are presenting those MCQs in a different style. It is a distributed file system used for storing data by commodity hardware. Reads are fast in RDBMS because the schema of the data is already known. Data represented in a distributed filesystem is already sorted. ResourceManager then scheduling tasks and monitoring them. A Combiner is a semi-reducer that executes the local reduce task. This Hadoop Test contains around 20 questions of multiple choice with 4 options. It is used during map step. B. There are different arguments that can be passed with this command to emit different results. 1.1. Writables are interfaces in Hadoop. : Storage unit– HDFS (NameNode, DataNode) Processing framework– YARN (ResourceManager, NodeManager) 4. The concept of choosing closer data nodes based on racks information is called Rack Awareness. B. The most common problem with map-side joins is introducing a high level of code complexity. Ans. C. A developer can always set the number of the reducers to zero. It stores various types of data as blocks in a distributed environment and follows master and slave topology. Key Difference Between Hadoop and RDBMS. Hadoop Pig runs both atomic data types and complex data types. It includes commodity hardware which will be cost-effective. The most often used is the in-memory engine, where data is loaded completely into memory and is analyzed there. Generally, the daemon is nothing but a process that runs in the background. Q30) What is the purpose of dfsadmin tool? Increase the parameter that controls minimum split size in the job configuration. This complexity has several downsides: increased risk of bugs and performance degradation. It maintains configuration data, performs synchronization, naming, and grouping. C. The default input format is controlled by each individual mapper and each line needs to be parsed indivudually. ( B) a) ALWAYS True. B. This is because Hadoop can only be used for batch processing, B. Individuals can practice the Big Data Hadoop MCQ Online Test from the below sections. Q15) What are the limitations of Hadoop 1.0? 13. ASWDC (App, Software & Website Development Center) Darshan Institute of Engineering & Technology (DIET) Yes, we can build “Spark” for any specific Hadoop version. It can easily store and process a large amount of data compared to RDBMS. By default, the HDFS block size is 128MB for Hadoop 2.x. This set of Multiple Choice Questions & Answers (MCQs) focuses on “Big-Data”. It is a compressed binary file format optimized for passing the data between outputs of one MapReduce job to the input of some other MapReduce job. Ans. Ans. The client can talk directly to a DataNode after the NameNode has given the location of the data. A. Serialize the data file, insert in it the JobConf object, and read the data into memory in the configure method of the mapper. Q29) What is the purpose of a DataNode block scanner? The values are arbitrarily ordered, and the ordering may vary from run to run of the same MapReduce job. B. 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Hadoop will be a good choice in environments when there are needs for big data processing on which the data being processed does not have dependable relationships. Check out the Big Data Hadoop Certification Training course and get certified today. D. A DataNode is disconnectedfrom the cluster. Q21) What is a Checkpoint Node in Hadoop? Apache Hadoop is a programming framework written in Java, it uses simple programming paradigm in order to develop data processing applications which can run in parallel over a distributed computing environment. It reads, writes, and manages large datasets that are residing in distributed storage and queries through SQL syntax. It stores any kind of data. ResourceManager then distributes the software/configuration to the slaves. A. A. C. There is a CPU intensive step that occurs between the map and reduce steps. E. Input file splits may cross line breaks. The JobTracker calls the TaskTracker’s configure () method, then its map () method and finally its close () method. In Hadoop 2.x, we have both Active and passive NameNodes. 1. Apache Pig is a high-level scripting language used for creating programs to run on Apache Hadoop. The test aims to validate your knowledge in digital data analytics which allows you to deliver actionable business insights. www.gtu-mcq.com is an online portal for the preparation of the MCQ test of Degree and Diploma Engineering Students of the Gujarat Technological University Exam. b) Map Reduce. Yes, because the sum operation is both associative and commutative and the input and output types to the reduce method match. B. Reduce-side join because join operation is done on HDFS. RapidMiner offers flexible approaches to remove any limitations in data set size. www.gtu-mcq.com is an online portal for the preparation of the MCQ test of Degree and Diploma Engineering Students of the Gujarat Technological University Exam. Ans. The WAL ensures all the changes to the data can be replayed when a RegionServer crashes or becomes unavailable. C. Map-side join is faster because join operation is done in memory. A. Map or reduce tasks that are stuck in an infinite loop. .hdfs dfsadmin -point topology is used for printing the topology. In addition to this, the applicants can go through about the Instructions, how to check the Web Services Online test Results. Learn Hadoop Multiple Choice Questions and Answers with explanations. A. ASequenceFilecontains a binaryencoding ofan arbitrary numberof homogeneous writable objects. The MapReduce framework represents the RecordReader instance through InputFormat. The configuration settings in the configuration file takes precedence, B. C. Sequences of MapReduce jobs only; no Pig or Hive tasks or jobs. How can we make the most of our efforts? The syntax for running the MapReduce program is. This definitive list of top Hadoop Interview Questions will cover the concepts including Hadoop HDFS, MapReduce, Pig, Hive, HBase, Spark, Flume, and Sqoop. Checkpoint Node is the new implementation of secondary NameNode in Hadoop. Rack Awareness is the algorithm used for improving the network traffic while reading/writing HDFS files to Hadoop cluster by NameNode. d) ALWAYS False. Q35) What is the main functionality of NameNode? C. Map files are generated by Map-Reduce after the reduce step. Q8) How can you skip the bad records in Hadoop? www.gtu-mcq.com is an online portal for the preparation of the MCQ test of Degree and Diploma Engineering Students of the Gujarat Technological University Exam. Ans. Ans. D. ASequenceFilecontains a binary encoding of an arbitrary number key-value pairs. Hence, this reduces development time by almost 16 times. Introduction: Hadoop Ecosystem is a platform or a suite which provides various services to solve the big data problems. www.gtu-mcq.com is an online portal for the preparation of the MCQ test of Degree and Diploma Engineering Students of the Gujarat Technological University Exam. D. Map files are sorted sequence files that also have an index. ASWDC (App, Software & Website Development Center) Darshan Institute of Engineering & Technology (DIET) The distributed cache is special component on namenode that will cache frequently used data for faster client response. D. Pig provides the additional capability of allowing you to control the flow of multiple MapReduce jobs. Override the get partition method in the wrapper. The below-provided is a free online quiz related to the Hadoop topic. B. Binary data cannot be used by Hadoop fremework. Ans. The MapReduce Partitioner manages the partitioning of the key of the intermediate mapper output. Both techniques have about the the same performance expectations. A. One key and a list of all values associated with that key. In order to overwrite default input format, the Hadoop administrator has to change default settings in config file. In-memory analytics is always the fa… The reduce method is called only after all intermediate data has been copied and sorted. Q31) What is the command used for printing the topology? A. Which of the following are the core components of Hadoop? B. Pig programs are executed as MapReduce jobs via the Pig interpreter. This Google Analytics exam involves 15 MCQs that are similar to those expected in the real exam. It views the input data set as a set of pairs and processes the map tasks in a completely parallel manner. It is imposible to disable the reduce step since it is critical part of the Mep-Reduce abstraction. A. HADOOP MCQs. 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. In-Memory: The natural storage mechanism of RapidMiner is in-memory data storage, highly optimized for data access usually performed for analytical tasks. When you have cached a file for a job, the Hadoop framework will make it available to each and every data node where map/reduces tasks are operating. Regarding analytics packages that work natively with Hadoop – those are limited to Frink and Mahout. It uses MapReduce to effect its distribution, reporting, recovery, and error handling. This data cannot be used as part of mapreduce execution, rather input specification only. A line that crosses file splits is read by the RecordReaders of both splits containing the brokenline. Ans. Yes. D. Reduce-side join because it is executed on a the namenode which will have faster CPU and more memory. By providing us with your details, We wont spam your inbox. Ans. Write a custom MapRunner that iterates over all key-value pairs in the entire file. Each key must be the same type. The methods used for restarting the NameNodes are the following: These script files are stored in the sbin directory inside the Hadoop directory store. According to Forbes, 90% of global organizations report their investments in Big Data analytics, which clearly shows that the career for Hadoop professionals is very promising right now and the upward trend will keep progressing with time. D. Input file splits may cross line breaks. D. A distributed filesystem makes random access faster because of the presence of a dedicated node serving file metadata. Apache Sqoop is a tool particularly used for transferring massive data between Apache Hadoop and external datastores such as relational database management, enterprise data warehouses, etc. It is a file-level computer data storage server connected to a computer network, provides network access to a heterogeneous group of clients. B. C. Data storage and processing can be co-located on the same node, so that most input data relevant to Map or Reduce will be present on local disks or cache. B. No, Hadoop does not provide techniques for custom datatypes. There are only a very few job parameters that can be set using Java API. This process is called Speculative Execution in Hadoop. It provides AvroMapper and AvroReducer for running MapReduce programs. So, it is not possible for multiple users or processes to access it at the same time. C. It depends when the developer reads the configuration file. Hadoop MCQs – Big Data Science. Remove the Nodes from include file and then run: Hadoop dfsadmin-refreshNodes, Hadoop mradmin -refreshNodes. Q23) How to keep an HDFS cluster balanced? HDFS (Hadoop Distributed File System) is the primary data storage unit of Hadoop. IdentityMapper.class is used as a default value when JobConf.setMapperClass is not set. Question2: Should I use a free analytics program for my website? Pig is a part of the Apache Hadoop project that provides C-like scripting languge interface for data processing, C. Pig is a part of the Apache Hadoop project. Developer can specify other input formats as appropriate if xml is not the correct input. Q16) How to commission (adding) the nodes in the Hadoop cluster? D. Place the data file in the DistributedCache and read the data into memory in the configure method of the mapper. NameNode chooses the Datanode which is closer to the same rack or nearby rack for reading/Write request. Apache Oozie is a scheduler which controls the workflow of Hadoop jobs. Big Data refers to a large amount of data that exceeds the processing capacity of conventional database systems and requires a special parallel processing mechanism. We fulfill your skill based career aspirations and needs with wide range of B. ASequenceFilecontains a binary encoding of an arbitrary number of heterogeneous writable objects. It displays the tree of racks and DataNodes attached to the tracks. I hope these questions will be helpful for your Hadoop job and in case if you come across any difficult question in an interview and unable to find the best answer please mention it in the comments section below. 1. B. Pig provides no additional capabilities to MapReduce. A line thatcrosses tile splits is ignored. They are often used in high-performance map-reduce jobs, B. Sequence files are a type of the file in the Hadoop framework that allow data to be sorted, C. Sequence files are intermediate files that are created by Hadoop after the map step. Grab the opportunity to test your skills of Apache Hadoop.These Hadoop multiple choice questions will help you to revise the concepts of Apache Hadoop and will build up your confidence in Hadoop. We make learning - easy, affordable, and value generating. Q2) Explain Big data and its characteristics. Below is some multiple choice Questions corresponding to them are the choice of answers. The Various HDFS Commands are listed bellow. Q14) Compare HDFS (Hadoop Distributed File System) and NAS (Network Attached Storage)? Streaming data is gathered from multiple sources into Hadoop for analysis. B. Reducers start copying intermediate key-value pairs from each Mapper as soon as it has completed. For a Comparison of types, the WritableComparable interface is implemented.
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