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1. The block size is set to by default. a. 64MB b. 32MB c. 16MB d. 128MB
Answer: A
Explanation: The default block size is 64MB, although it can be extended if the HDFS configuration requires it.
2. Which of the parameters below describes the archive’s destination directory? a.Title of the archive b. origin c. place of travel d. None of the aforementioned options are available.
Answer: C
Explanation: destination
3. In the Hadoop Ecosystem, is the most popular high-level Java API. a. HCatalog b. Cascalog c. Scalding d. Cascading is a term that refers to a series of events that occur
Answer: D
Explanation: Many of the complexity of MapReduce programming are hidden behind more intuitive pipelines and data flow abstractions thanks to cascading.
4. HDFS files are intended for use with high-density storage systems. a. Only writing once into a file b. Data access with a low latency. c. Modifications at arbitrary offsets and multiple writers. d. Do not append anything to the file until the end.
Answer: D
Explanation: Append only at the end of the document.
5. The names of the parameters are changed during the execution of a streaming task. a. vmap b. mapvim c. Mapreduce d. Mapred
Answer: D
Explanation: Use the parameter names with underscores to get the values in a streaming job’s mapper/reducer.
6. In the Hadoop environment, what does commodity Hardware imply? a. Hardware that is industry standard b. Low specs Hardware that is used in the industry c. Hardware that has been discarded d. Low-cost hardware
Answer: D
Explanation: Low requirements Hardware that is used in the industry
7. Gzip (short for GNU zip) compresses files and gives them the extension. a. .g b. .gzp c. .gzip d. .gz
Answer: D
Explanation: The gunzip command can be used to uncompress files created by a variety of compression programmes, including Gzip.
8. In a disc balancer, the datanode chooses the disc for the block based on which volume is chosen. a. It’s a round-robin competition. b. The amount of room available c. Each and every one of the preceding options d. None of the aforementioned options are viable options.
Answer: C
Explanation: Available space, round-robin
9. The option allows you to copy jars to the current working directory of tasks and have them automatically unjarred. a. Documents b. mission c. archives d. None of the above
Answer: C
Explanation: A generic option is Archives.
10. To offer various outputs to Hadoop, which of the following is used? a. MultipleOutputs is a class that allows you to have several outputs. b. DBInputFormat is a type of DBInputFormat that is used to format data c. FileOutputFormat is a format for the output of a file. d. MultipleOutputFormat is a type of output format that has more than one output format.
Answer: A
Explanation: a class for many outputs
11. ——is a Hadoop Map/Reduce scheduler that allows huge clusters to be shared. a. A Flow Scheduler is a programme that allows you to schedule the flow b. Data Scheduler c. Capacity Scheduler d. None of the above
Answer: C
Explanation: Numerous queues are supported by the Capacity Scheduler, and a job can be sent to multiple queues.
12. Enable disk balancer in hdfs-site.xml by setting which of the following to true a. dfs.balancer.enabled is a value that is set in the dfs.balancer.enabled property. b. dfs.disk.balancer.disabled i c. dfs.disk.balancer.enabled d. diskbalancer.enabled
Answer: C
Explanation: dfs.disk.balancer.enabled
13. Hadoop is responsible for which of the following genres? a. The Relational Database Management System (RDBMS) is a type of database management system b. A file system that is shared across multiple computers. c. JAX-RS (Java Application XML Representational State Transfer) d. Java Message Service (JMS) is a service that lets you send and receive messages
Answer: B
Explanation: HDFS (Hadoop Distributed File System) is a file system that is designed to consistently store and stream very massive data sets to users.
14. How many partitioners are there in total? a. The quantity of reducers b. The quantity of combiners c. The amount of mappers d. None of the above
Answer: C
Explanation: The amount of reducers
15. Per terabyte compressed, the compression offset map expands to GB. a. 1-3 b. 10–16 c. 20-22 d. 0-1
Answer: A
Explanation: The larger the compression offset table is, the more compressed blocks you have.
16. Simply mentioning a specific directory isn’t enough for Some partitioning jobs. a. inanimate b. semi-cluster c. dynamic d. All of the above
Answer: C
Explanation: It requires a pattern specification instead of a directory specification because it writes to several destinations.
17. Data written by one system can be sorted efficiently by another system using . a. Data Type: Complex b. Establish a hierarchy c. Order of Sorting d. Each and every one of the above
Answer: C
Explanation: Without deserializing to objects, Avro binary-encoded data can be effectively organised.
18.All SSD Data replication is required in a variety of situations, including the following: a.The Replication Factor has been modified. b.The DataNode is no longer available. c.Corrupted Data Blocks d. Each and every one of the above
Answer: D
Explanation: To provide a high level of failure tolerance, data is replicated over several DataNodes.
19.Identify the following statement as being incorrect: a. In Hive, variables have four different namespaces. b. With the define command, you can create custom variables in a different namespace. c. Hivevar can also be used to define custom variables in their own namespace. d. None of the aforementioned options are available.
Answer: A
Explanation: Hiveconf, system, and env are three namespaces for variables.
20. A is a way to extend Ambari by allowing third parties to add additional resource types to the APIs. a. cause b. observe c. model d. None of the above
Answer: B
Explanation: An application that is deployed into the Ambari container is referred to as a view.
21.—- is a free and open source system for data analysis that is expressive, declarative, quick, and efficient. a. Flume b. flink c. Flexibility d. ESME
Answer: B
Explanation: Stratosphere blends distributed MapReduce-like platforms’ scalability and programming flexibility with out-of-core execution performance.
22. What programming language did Hadoop use? a. Java (software platform) b. Perl c. Java, version (programming language) d. Lua (programming language)
Answer: C
Explanation: PerlHadoop is primarily written in Java, with some native code written in C and command-line utilities written as shell scripts.
23. The function is called by the InputFormat class, which computes splits for each file and delivers them to the jobtracker. a. says b. is rewarded c. getSplits is a command that returns a list of splits. d. All of the above
Answer: C
Explanation: InputFormat schedules map jobs to be processed on tasktrackers based on their storage locations.
24. The can be used to report progress and set application-level status messages in applications. a. Partitioner b. OutputSplit c. Reporter d. Each and every one of the above
Answer: C
Explanation: Reporter can also be used to update Counters or simply to show that they are still active.
25. A MapReduce job is submitted by your client application to your Hadoop cluster. Identify the Hadoop daemon that the Hadoop framework will use to plan a MapReduce job. a. JobTracker b. DataNode c. JobTracker d. TaskTracker
Answer: B
Explanation: JobTracker
26. What should be the top limit for a Map Reduce job’s counters? a. 5 b. 15 c. 150 d. 50
Answer: D
Explanation: ~50
27. PIG data is read using which of the following functions? a. WRITING b. READING IN c. LOADING d. None of the aforementioned options are available.
Answer: C
Explanation: The standard load function is PigStorage.
28. Traditional Hadoop deployments have a single point of failure, whereas are highly resilient and eliminate that risk. a. EMR b. Solutions from Isilon c. AWS d. None of the aforementioned options are available.
Answer: B
Explanation: The Isilon solution also provides enterprise data protection and security features, such as file system audits and data-at-rest encryption, in order to meet compliance needs.
29. The Mapreduce architecture for Hadoop does not sort the output of the Program. a. Mapping b. Cascader c. Scalding d. None of the above
Answer: D
Explanation: Typically, the reduction task’s output is written to the FileSystem. The Reducer’s output isn’t sorted.
30. You’ll need a distributed, scalable data store that lets you access hundreds of terabytes of data at random and in real time. Which of the options do you think you’d go with? a. Hue b. Pig c. HBase d. Flume
Answer: C
Explanation: HBase
Hadoop Interview Questions and Answers Section 02
1. A framework for creating data flows for ETL (extract, transform, and load) processing and analysis of huge datasets has been developed. a. Oozie b. HIVE c. Pig d. Latin
Answer: C
Explanation: Pig
2. In HDFS, you can use the merge command to combine all the files in a directory. a. getmerge b. putmerge c. reappear d. mergeall
Answer: A
Explanation: getmerge
3. HBase is used for what? a. Quick Navigation, in Hadoop, reduce layer b. Hadoop’s MapReduce replacement c. In Hadoop, a tool for doing random and fast read/write operations d. More quickly In Hadoop, there is a read-only query engine.
Answer: C
Explanation: In Hadoop, there’s a tool for performing random and fast read/write operations.
4. Which attribute controls whether speculative execution is enabled or disabled? a. mapred.map.tasks.speculative.execution b. speculative.execution.mapred.reduce.tasks c. Both of the preceding d. None of the preceding
5. What should the namenode’s hardware be like? a. Simply increase the amount of RAM available to each of the data nodes. b. It makes no difference. c. Better than the commodity grade d. Commodity quality
Answer: C
Explanation: Superior than commodity grade
6. Which of the following statements most accurately defines TextInputFormat’s operation? a. Because the input file is divided precisely at line breaks, each Record Reader will read a series of entire lines. b. Line breaks in the input file might be crossed. The RecordReaders of both splits containing the brokenlin line read a line that crosses file splits. c. Line breaks in the input file might be crossed. The RecordReader of the file split that contains the beginning of the broken line reads a line that crosses file splits. d. Line breaks in the input file might be crossed. The RecordReader of the split that contains the end of the broken line is used to read a line that crosses file divides.
Answer: D
Explanation: Line breaks in input files may be crossed. The RecordReader of the split that contains the end of the broken line is used to read a line that crosses file divides.
7.EXCEPT FOR HADOOP, ALL OF THESE ARE ACCURATE DESCRIPTIONS. a. Instantaneous b. A method of computing that is distributed. c. Java-based system d. Source code
Answer: A
Explanation: real-time
8. In MapReduce, the input split signifies a. The average data block size used as programme input. b. The precise location of the block’s first and last full records. c. Splitting the data input to a MapReduce programme into a size specified in the mapred-site.xml file. d. None of these are correct.
Answer: C
Explanation: The start and finish locations of a block’s first and last entire records.
9. Which of the following is/are an example of Real-Time Big Data Processing? a. CEP platforms (Complex Event Processing) b. Data analysis for the stock market c. Detection of financial fraud transactions d. (A) and (B) are both true
Answer: D
Explanation: Bank fraud transaction detection using Complex Event Processing (CEP) platforms
10. When the active node fails in NameNode HA, which node assumes responsibility for the active node? a. Node with a secondary name b. Node that serves as a backup c. Node of the checkpoint d. Node in standby
Answer: D
Explanation: Standby node
11. On mapred-site.xml, which of the following properties is configured? a. Factor of replication b. hdfs file storage directory names c. The host and port where the MapReduce task is executed d. Variables in the Java environment.
Answer: C
Explanation: Host and port where MapReduce task runs
12. What should the namenode’s hardware be like? a. Simply increase the amount of RAM available to each of the data nodes. b. It makes no difference. c. Better than the commodity grade d. Commodity quality
Answer: C
Explanation: Superior than commodity grade
13. Which of the following statements most accurately defines TextInputFormat’s operation? a. Because the input file is divided precisely at line breaks, each Record Reader will read a series of entire lines. b. Line breaks in the input file might be crossed. The RecordReaders of both splits containing the brokenlin line read a line that crosses file splits. c. Line breaks in the input file might be crossed. The RecordReader of the file split that contains the beginning of the broken line reads a line that crosses file splits. d. Line breaks in the input file might be crossed. The RecordReader of the split that contains the end of the broken line is used to read a line that crosses file divides.
Answer: D
Explanation: Line breaks in input files may be crossed. The RecordReader of the split that contains the end of the broken line is used to read a line that crosses file divides.
14. EXCEPT FOR HADOOP, ALL OF THESE ARE ACCURATE DESCRIPTIONS. a. Instantaneous b. A method of computing that is distributed.br /> c. Java-based system d. Source code
Answer: A
Explanation: real-time
15. In MapReduce, the input split signifies a. The average data block size used as programme input. b. The precise location of the block’s first and last full records. c. Splitting the data input to a MapReduce programme into a size specified in the mapred-site.xml file. d. None of these are correct.
Answer: B
Explanation: The start and finish locations of a block’s first and last entire records.
16. Which of the following is/are an example of Real-Time Big Data Processing? a. CEP platforms (Complex Event Processing) b. Data analysis for the stock market c. Detection of financial fraud transactions d.(A) and (B) are both true
Answer: D
Explanation: Bank fraud transaction detection using Complex Event Processing (CEP) platforms
17. When the active node fails in NameNode HA, which node assumes the active node’s responsibility? a. NameNode (secondary) b. Node c. Node of the checkpoint d. The node that serves as a backup
Answer: D
Explanation: Standby node
18. What is Hadoop’s origin? a.A favourite circus act of creator Doug Cuttings b.Cuttings’ son’s toy elephant c. Cuttings, a rock band from high school d. A Hadoop’s development laptop with sound cuttings
Answer: B
Explanation: The toy elephant of Cuttings son
19. What Hadoop methods are used to keep the name node from failing? a. Back up the metadata of the filesystem to a local drive and a remote NFS mount. b. Cloud-store the metadata from the filesystem. c. Make sure you have at least 12 CPUs on your PC. d. Investing in high-quality, long-lasting equipment.
Answer: A
Explanation: Make a local disc and a remote NFS mount backup of the filesystem metadata.
20. Big Data was coined by: a. Domain of Stock Exchanges b. Domains of Genomics and Astronomy c. Domain of the Social Media d. Domain of Banking and Finance
Answer: B
Explanation: Genomics and Astronomy Domain
21. Which of the following statements concerning NameNode High Availability is correct? a. Identify and eliminate a single point of failure. b. In order to achieve a high degree of scalability c. Cut your storage costs in half. d. None of the aforementioned options are viable options.
Answer: A
Explanation: Solve Single point of failure.
22. HDFS federation is a system that allows users to share files across several servers. a. The metadata for the entire filesystem is managed by each name node. b. A piece of the filesystem’s metadata is managed by each name node. c. If a single name node fails, the entire filesystem loses access to some metadata. d. Each data node establishes a connection to each name node.
Answer: B
Explanation: A piece of the filesystem’s metadata is managed by each name node.
23. What is a SequenceFile, and what is it used for? a. A Flow Diagram b. An arbitrary number of homogenous readable objects are encoded in binary in a file. c. A SequenceFile is a binary encoding of an arbitrary number of WritableComparable objects, sorted in order. d. A Sequence is a term used to describe a series of events
Answer: C
Explanation: A SequenceFile holds a binary encoding of any number of key-value pairs. The same type of key must be used for each. Each value must have the same type as the previous one.
24. Hadoop is a framework that integrates with a number of other programmes. Is it true that there are common cohorts? a. MapReduce, Hive, and HBase are three of the most popular data processing frameworks. b. MapReduce, MySQL, and Google Apps are three of the most popular technologies. c. Iguana, Hummer, and MapReduce d. Heron, Trumpet, and MapReduce
Answer: A
Explanation: MapReduce, Hive, and HBase
25. Hadoop’s data locality feature entails a. Use many nodes to store the same data. b. Transfer data from one node to the next. c. Store the data in the same location as the compute nodes. d. Distribute the data over a number of different nodes.
Answer: C
Explanation: Data should be kept near the compute nodes.
26. In the Hadoop environment, what does commodity hardware imply? a. Low-cost equipment. b. Hardware that is common in the industry. c. Hardware that has been discarded d. Minimal requirements Hardware that is fit for a business
Answer: D
Explanation: Minimum requirements Hardware that is fit for a business
27.Which of the following options addresses the problem of small files? a. Hadoop archives, for example b. Files that include sequences c. HBase d. Each and every one of the preceding options
Answer:D
Explanation: All of the above
28. What happens if an HDFS block becomes unavailable due to disc corruption or machine failure in a Hadoop cluster? a. It is irretrievably lost. b. It can be copied to other live devices from its alternate places. c. The name node permits new client requests to attempt to read it in the future. d.The MapReduce task process skips over the block and its contents.
Answer: B
Explanation: It can be copied to other live devices from its other locations.
29. On mapred-site.xml, which of the following properties is set? a. Factor of reproduction b. hdfs file directory names c. The host and port on which the MapReduce task is executed. d. VARIABLES IN THE Java ENVIRONMENT
Answer: C
Explanation: MapReduce task’s host and port
30. Is a map input format available? a. Yes, but only in Hadoop 0.22 and above. b. Map files do have their own format. c. No, however map files can be read via the sequence file input format. d. A and B are both true.
Answer: C
Explanation: No, although map files can be read with the sequence file input format.
Hadoop Interview Questions Overview:
Here we included the scope for Hadoop for future and why ou choose this domain to make your career in this field. Hadoop is an open-source framework designed for distributed storage and processing of large datasets across clusters of commodity hardware. It was created by Doug Cutting and Mike Cafarella and is now maintained by the Apache Software Foundation. The Hadoop ecosystem includes various components that enable the storage, processing, and analysis of Big Data.
Key Components of the Hadoop Ecosystem
1. Hadoop Distributed File System (HDFS): This is the storage component of Hadoop, designed for high-throughput access to application data. HDFS uses a master-slave architecture, with the NameNode as the master and DataNodes as slaves.
2. MapReduce: A programming model and processing engine for distributed data processing. It allows parallel processing of large datasets by dividing them into smaller chunks.
3. YARN (Yet Another Resource Negotiator): YARN is a resource management layer that separates the resource management and job scheduling functions of MapReduce. It enables running various data processing engines on Hadoop, like Apache Spark and Apache Flink.
4. Hive: A data warehousing and SQL-like query language tool that simplifies data querying and analysis on Hadoop. It provides a familiar interface for users comfortable with SQL.
5. Pig: A high-level scripting language for data analysis and processing. It’s particularly useful for ETL (Extract, Transform, Load) operations.
6. HBase: A NoSQL database that provides real-time read/write access to large datasets. It’s suitable for applications requiring low-latency data access.
7. Spark: An in-memory data processing engine that is faster than traditional MapReduce. Spark supports real-time streaming, machine learning, and graph processing.
Future Scope of Hadoop
Hadoop has evolved significantly since its inception and continues to be a fundamental technology in the Big Data landscape. Here are some aspects of Hadoop’s future scope:
1. Advanced Analytics: Hadoop will play a crucial role in enabling advanced analytics, including machine learning, deep learning, and artificial intelligence. Tools like Spark, Mahout, and TensorFlow are integrated with Hadoop to perform these tasks.
2. Real-time Processing: Hadoop is moving toward real-time data processing capabilities. Frameworks like Apache Flink and Kafka Streams are integrated with Hadoop to support real-time data ingestion and processing.
3. Hybrid Cloud Deployments: As organizations increasingly adopt hybrid cloud infrastructures, Hadoop will continue to be a central component for managing and analyzing data across on-premises and cloud environments.
4. Security and Governance: Hadoop’s security features are continuously improving to address data privacy and regulatory compliance requirements. Technologies like Apache Ranger and Apache Sentry enhance data security and governance.
5. Edge Computing: With the growth of IoT (Internet of Things), Hadoop can be deployed at the edge to process data locally before sending it to central clusters. This reduces latency and bandwidth requirements.
6. Containerization: Hadoop is being containerized using technologies like Docker and Kubernetes, making it easier to manage and deploy Hadoop clusters.
7. Integration with Other Data Technologies: Hadoop is increasingly integrated with other data technologies, such as data lakes, data warehouses, and NoSQL databases, to provide a comprehensive data management and analysis solution.
Hadoop’s future remains bright, with continuous advancements in technology and integration with emerging data processing and analytics tools. So if you want to crack the Hadoop interview in any company the prepare above Top Hadoop Interview Questions. As organizations continue to grapple with massive datasets and the need for real-time insights, Hadoop will remain a critical player in the Big Data and analytics landscape. However, it’s essential for professionals in this field to stay updated with the latest trends and technologies to make the most of Hadoop’s potential.
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