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CCA Spark and Hadoop Developer (CCA-175) Interview Questions

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CCA Spark and Hadoop Developer (CCA-175) Interview Questions

The CCA Spark and Hadoop Developer (CCA-175) exam are designed to test the knowledge of a CCA Spark and Hadoop Developer who has demonstrated their core skills in ingesting, transforming, and processing data using Apache Spark and core Cloudera enterprise tools. This certification will also help you advance in your career. As the need for understanding cloud concepts has grown, so has the demand for professionals in this field.

Big Data jobs are currently in high demand. One out of every five large corporations is shifting to Big Data Analytics, so now is the time to start applying for jobs in this field.
Big data has grown exponentially in the last decade. Big Data necessitates the widespread use of Hadoop to address major Big Data challenges. Hadoop is one of the most widely use frameworks for storing, processing, and analyzing Big Data. As a result, there is always a need for professionals in this field. But how do you get a job in the Hadoop industry? We’ve got answers for that!

So, without further ado, here are the Top Spark and Hadoop Developer Interview Questions and Answers that will assist you in acing the interview.

1. What concepts are used in the Hadoop Framework?

The Hadoop Framework is based on two fundamental concepts:

  • HDFS (Hadoop Distributed File System) is a Java-based file system for scalable and reliable storage of large datasets. HDFS operates on a Master-Slave architecture and stores all of its data in the form of blocks.
  • MapReduce is a programming model and implementation for processing and generating large data sets. The map job divides the data set into key-value pairs, also known as tuples. The reduce job then takes the map job’s output and combines the data tuples into a smaller set of tuples.

2. What exactly is Hadoop? Identify the Key Components of a Hadoop Application.

Hadoop emerged as a solution to the “Big Data” problem. Hadoop is defined as a framework that provides a variety of tools and services for storing and processing Big Data. It also plays an important role in the analysis of big data and in making efficient business decisions when making the decision using the traditional method is difficult. Hadoop offers a vast toolset that makes it possible to store and process data very easily. Here are all the main components of Hadoop:

  • Hadoop Common
  • HDFS
  • Hadoop MapReduce
  • YARN
  • PIG and HIVE – The Data Access Components.
  • HBase – For Data Storage
  • Apache Flume, Sqoop, Chukwa – The Data Integration Components
  • Ambari, Oozie and ZooKeeper – Data Management and Monitoring Component
  • Thrift and Avro – Data Serialization components
  • Apache Mahout and Drill – Data Intelligence Components

3. How many different input formats does Hadoop support? Explain.

The Hadoop framework’s following features allow a Hadoop administrator to add (commission) and remove (decommission) Data Nodes in Hadoop clusters –

  • One of the key features of the Hadoop framework is that it makes use of commodity hardware. It causes DataNode crashes in a Hadoop cluster on a regular basis.
  • Another important feature of the Hadoop framework that is performed in response to the rapid growth of data volume is its scalability.

4. What exactly do you mean by “Rack Awareness”?

Rack Awareness is defined in Hadoop as the algorithm by which NameNode determines how blocks and their replicas are store in the Hadoop cluster. This is accomplished through rack definitions, which reduce traffic between DataNodes within the same rack. As an example, we know that the replication factor’s default value is 3. The “Replica Placement Policy” states that two copies of replicas for each block of data will be stored in a single rack, while the third copy will be stored in a different rack.

5. What are your thoughts on the Speculative Execution?

Speculative Execution in Hadoop is a process that occurs during the slower execution of a task at a node. The master node begins executing another instance of the same task on the other node during this process. And the task that is completed first is accepted, and the execution of the others is halted by killing that.

6. Describe some of Hadoop’s key features.

Hadoop’s key features are as follows:

  • Hadoop is based on Google MapReduce, which is based on Google’s Big Data File Systems.
  • For Big Data analysis, the Hadoop framework can efficiently answer any questions.

7. How do you tell the difference between RDBMS and Hadoop?

The key differences between RDBMS and Hadoop are as follows:

  • Firstly, RDBMS are design to store structured data, whereas Hadoop can store any type of data, including unstructure, structure, and semi-structured data.
  • Secondly, RDBMS adheres to the “Schema on Write” policy, whereas Hadoop adheres to the “Schema on reading” policy.
  • Further, RDBMS reads are faster because the schema of the data is already known, whereas HDFS writes are faster because no schema validation occurs during HDFS writing.
  • RDBMS is license software, so it must be purchased, whereas Hadoop is open-source software, so it is free.
  • RDBMS is use for OLTP systems, whereas Hadoop is use for data analytics, data discovery, and OLAP systems.

8. What are your thoughts on active and passive NameNodes?

Two NameNodes are present in a high-availability Hadoop architecture.

Active NameNode – The Active NameNode is the NameNode that is running in the Hadoop cluster.

Passive NameNode – The Passive NameNode is the standby NameNode that stores the same data as the Active NameNode.

When an active NameNode fails, the passive NameNode replaces it and assumes control. As a result, there is always a running NameNode in the cluster, ensuring that it never fails.

9. What are the Apache HBase Components?

Apache HBase is made up of the following major components:

  • A Table can be divide into several regions using the Region Server. A Region Server serves a collection of these regions to clients.
  • HMaster: This is the server that coordinates and manages the Region server.
  • ZooKeeper: A coordinator within the HBase distributed environment. It works by communicating in sessions to keep the server state within the cluster.

10. How does NameNode handle DataNode failure?

NameNode receives a signal from all DataNodes in the Hadoop cluster that specifies the DataNode’s proper function on a continuous basis. A block report contains a list of all the blocks on a DataNode. If a DataNode fails to send the signal to the NameNode within a certain time period, it is marked dead. The NameNode then replicates/copies the blocks of the deceased node to another DataNode using the previously created replicas.

11. Describe the NameNode recovery procedure.

The NameNode recovery process helps to keep the Hadoop cluster running and is explained in the following steps –

Step1: To begin a new NameNode, use the file system metadata replica (FsImage).

Step 2: Set up the clients and DataNodes to recognize the new NameNode.

Step 3: Once the new NameNode has finished loading the last checkpoint FsImage and has received block reports from the DataNodes, it will begin serving the client.

12. What are the various schedulers that are available in Hadoop?

The various schedulers available in Hadoop are –

  • COSHH – It schedules decisions by taking into account cluster, workload, and heterogeneity.
  • FIFO Scheduler – It orders jobs in a queue based on their arrival time without using heterogeneity.
  • Fair Sharing – It creates a pool for each user that contains a number of maps and reduces the number of slots available on a resource. Each user is permit to use his or her own pool for job execution.

13. Is it possible for DataNode and NameNode to be commodity hardware?

DataNodes are commodity hardware because they can store data like laptops and personal computers, and they are in high demand. Instead, NameNode serves as the master node, storing metadata about all HDFS blocks. It requires a large amount of memory and thus functions as a high-end machine with a large amount of memory.

14. Define Hadoop daemons.

NameNode, Secondary NameNode, DataNode, NodeManager, ResourceManager, and JobHistoryServer are the Hadoop daemons.

15. What are the roles of Hadoop daemons?

The function of various Hadoop daemons is –

  • NameNode – The NameNode is the master node that is in charge of storing metadata for all directories and files. It also includes metadata about each block of the file and its placement in the Hadoop cluster.
  • Secondary NameNode – This daemon is in charge of merging and storing the modified Filesystem Image in long-term storage. It is use in the event that the NameNode fails.
  • DataNode – The DataNode is the slave node that contains the actual data.
  • NodeManager – The NodeManager, which runs on slave machines, is in charge of launching application containers, monitoring resource usage, and reporting it to the ResourceManager.
  • ResourceManager – This is the primary authority in charge of managing resources and scheduling applications that run on top of YARN.
  • JobHistoryServer – It is in charge of storing all information about MapReduce jobs when the Application Master stops working (terminates).

16. Explain Checkpointing.

Checkpointing is a procedure for combining a FsImage and an Edit log into a new FsImage. Instead of replaying an edit log, the NameNode handles the loading of the final in-memory state from the FsImage directly. The secondary NameNode is in charge of the checkpointing process.

17. What are the most important hardware considerations when deploying Hadoop in a production environment?

The memory requirements of the Memory System are as follows: Depending on the application, this will differ between worker services and management services.

  • Operating system: A 64-bit operating system is preferred because it eliminates any restrictions on the amount of memory that can be use on worker nodes.
  • Storage: To achieve scalability and high performance, a Hadoop Platform should be design by moving computing activities to data.
  • Capacity: Large Form Factor discs are less expensive and provide more storage space.
  • Network: Two TOR switches per rack are ideal to eliminate the possibility of redundancy.

18. What should you keep in mind when deploying a secondary NameNode?

Always deploy a secondary NameNode on a separate Standalone system. This prevents it from interfering with the primary node’s operations.

19. Describe the various modes in which Hadoop code can be executed.

There are several ways to run Hadoop code –

  • Mode of fully-distributed distribution
  • Mode of pseudo-distribution
  • Self-contained mode

20. What are the most important characteristics of hdfs-site.xml?

The hdfs-site.xml file has three important properties:

  • data.dr – Identify the location of data storage.
  • name.dr – identifies the location of metadata storage and specifies whether DFS is on disc or at a remote location.
  • checkpoint.dir – Secondary NameNode directory.

21. What are the essential Hadoop tools for improving Big Data performance?

Some of the most important Hadoop tools for improving Big Data performance are –

Hive, HDFS, HBase, Avro, SQL, NoSQL, Oozie, Clouds, Flume, SolrSee/Lucene, and ZooKeeper

22. Identify the operating systems that are supported by Hadoop deployment.

The primary operating system for Hadoop is Linux. It can, however, be installed on the Windows operating system with the help of some additional software.

23. Why is HDFS used for applications that require large data sets rather than multiple small files?

When storing a large number of data sets in a single file, HDFS is more efficient than storing small chunks of data in multiple files. Because the NameNode stores metadata for the file system in RAM, the number of files in the HDFS file system is limited. Simply put, more files will generate more metadata, which will necessitate more memory (RAM). It is recommended that the metadata of a block, file, or directory be 150 bytes in size.

24. Define Apache Yarn.

YARN is an acronym that stands for Yet Another Resource Negotiator. It is a resource management system for Hadoop Cluster. It was introduced in Hadoop 2 to aid MapReduce and is Hadoop’s next-generation computation and resource management framework.

25. Explain Node Manager.

The YARN equivalent of the Tasktracker is Node Manager. It receives instructions from the ResourceManager and manages the resources on a single node. It is in charge of containers, as well as monitoring and reporting their resource usage to the ResourceManager. Every container process that runs on a slave node is initially provisioned, monitored, and tracked by the slave node’s Node Manager daemon.

26. What are the distinctions between a regular FileSystem and an HDFS?

Regular FileSystem: Data in a regular FileSystem is kept in a single system. Due to the machine’s low fault tolerance, data recovery is difficult. Since seek time is longer, it takes longer to process the data.
Data is distributed and maintained across multiple systems using HDFS. Data can still be recovered from other nodes in the cluster if a DataNode fails. The time required to read data is comparatively longer because there is local data read to the disc and data coordination from multiple systems.

27. What makes HDFS fault-tolerant?

Since it replicates data across DataNodes, HDFS is fault-tolerant. A block of data is replicated on three DataNodes by default. The data blocks are saved in various DataNodes. If one of the DataNodes fails, the data can still be retrieved from the other DataNodes.

28. Is it possible to modify the number of mappers generated by a MapReduce job?

The number of mappers cannot be changed by default because it is equal to the number of input splits. However, there are several ways to change the number of mappers, including setting a property or customizing the code.

For example, if you have a 1GB file that is divided into eight blocks of 128MB each, the cluster will only have eight mappers running. However, there are several ways to change the number of mappers, including setting a property or customizing the code.

29. What happens if you store too many small files in an HDFS cluster?

Storing a large number of small files on HDFS generates a large number of metadata files. It is difficult to store this metadata in RAM because each file, block, or directory requires 150 bytes for metadata. As a result, the total size of all the metadata will be too large.

30. In a YARN-based cluster, can we have more than one ResourceManager?

Yes, we can have multiple ResourceManagers in Hadoop v2. A high availability YARN cluster can have an active ResourceManager and a standby ResourceManager, with ZooKeeper handling coordination.

At any given time, only one ResourceManager can be active. If an active ResourceManager fails, the standby ResourceManager steps in.

Conclusion for CCA Spark and Hadoop Developer (CCA-175) Interview Questions

Spark and Hadoop is a rapidly expanding field that creates a large number of jobs for both new and experienced workers each year. The best way to prepare for a Hadoop job is to answer all of the Spark and Hadoop Interview Questions that you come across. We put together this list of Hadoop interview questions for you, which we will keep up to date.

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