Technology

Hiring talent on demand is the process where employers can recruit trained fresher’s whenever they require. “Talent-on-Demand is an initiative to bridge this gap. It complements college education by way of industry-oriented, focused programs that enhance the employability quotient of students.

It is also the newest style of hiring skilled talent off campus today, wherein companies get access to a trained talent-pool that has hands-on experience in the technologies that they work with.

Talent-on-Demand is IIHT’s initiative to bridge the gap between the academia and the IT industry. We at IIHT supplement and complement college education by way of industry-oriented, focused programmes that enhance the employability quotient of students. We accomplish this by equipping them with project/ technology specific technical and domain knowhow through customised programs that ensure talent is billable immediately after being on-boarded.

Java, derived from C and C++, is an object-oriented programming language having a simple object model. Java has helped programmers in simplifying the coding and developing applications and installing them on cross-platform systems. 

Java is the most important development that took place in IT after C++. It has created a revolution in the digital world we live in. It has led to several innovations and development of very handy products and services.

Classroom training at workplaces is usually challenging when it comes to coordinating multiple schedules of learners and justifying the outcomes and ROI of L&D efforts. On the other hand, online videos as the only training resource leave employees disconnected from their peers or are left with incomplete understanding in the absence of other resources like virtual mentors, labs and illustrations.

Blended learning is a balanced mix of classroom and online learning sessions and is great at taking a headache out of such scenarios. The method brings in options and flexibility to training programs.

IIHT Launchpad is nation-wide placement drives to facilitate skilling and employment opportunities for tech students across India. Launchpad has been conducted in over 500 colleges in 19 locations across India for the initial assessments of candidates and scheduling more colleges in the coming weeks of September.

The wide disconnect between education and acquiring skills has resulted in only 7% of today’s engineering graduates deemed employable. Recognizing the dire need for transformation in the academic learning ecosystem, IIHT aims to tackle these challenges through our placement drives followed by in-house corporate training.

In the IT industry today, recruiting enterprises are adopting numerous ways to determine if aspiring graduates are ready for real-world challenges. This entices students to acquire additional technical credentials, along with their regular curriculum, that enables their suitability for a particular role.

When most institutions enrolled students to award a degree, these institutions are providing resources during the course with the help of third party talent transformation partners working closely with the industry.

Implementing the Integrated Learning Program at Campuses

Implementing such courses at universities varies from campus to campus and even batch to batch. With short term courses like Finishing School becoming mandatory for institutions before graduation, some institutions agree to include these programs in the existing schedule. A whole lot of other institutions offer these supplementary courses as integrated programs right after the regular lectures or during weekends while some institutions go for independent study for students in campus to complete a minimum number of hours of instruction.

Ream more : – https://www.iiht.com/blog/opportunities-to-bridge-the-campus-to-corporate-readiness-gap/

Hadoop is one of the most opted for courses at IIHT.  We are listing the most frequently asked Hadoop interview questions and should be of great help to you in your next interview. Keeping aside few interviewer specific twist during interviews, you’ll get a firm hold of basics of Hadoop and its framework in this blog.

Hadoop is the infrastructure that provides tools and services for processing and storing huge data sets. Hadoop is highly regarded as the ‘solution’ to most Big Data challenges faced by organizations and make optimized business decisions. 

Q1.) Explain in detail about Kafka Producer in context to Hadoop?
Q2.) Explain Monad class?
Q3.) Explain the reliability of Flume-NG data?
Q4.) What is Interceptor?
Q5.) What are the different Flume-NG Channel types?
Q6.) What is Base class in java?
Q7.) What is Base class in scala?
Q8.) What is Resilient Distributed Dataset(RDD)?
Q9.) Give a brief description of Fault tolerance in Hadoop?
Q10.) What is Immutable data with respect to Hadoop?
Q11.) Which are the nodes that hadoop can b executed?
Q12.) How is formatting done in HDFS?
Q13.) What are the contents found in masterfile of hadoop?
Q14.) Describe the main hdfs-site.xml properties?
Q15.)Explain about spill factor with respect to the RAM?
Q16.) Why do we require a password-less SSH in Fully Distributed environment?
Q17.) Does this reqirement lead to security issues?
Q18.) What will happen to a NameNode, when ResourceManager is down?
Q19.) 1 Tell about features of Fully Distributed mode?
Q20.) Explain about fsck?
Q21.) how to copy file from local hard disk to hdfs
Q22.) is it possible to set the reducer to zero???
Q23.) map-side join / hive join
Q24.) Managed Table Vs External Table
Q25.) Difference between bucketing and partitioning
Q26.) Syntax to create hive table with partitioning
Q27.) SQOOP split by:
Q28.) file formats available in SQOOP Import
Q29.) Default number of mappers in a sqoop command
Q30.) Maximum number of mappers used a sqoop import command
Q31.) Flume Architecture
Q32.) In Unix, command to show all processes
Q33.) partitions in hive
Q34.) File formats in hive
Q35.) Syntax to create bucketed table
Q36.) Custom Partitioning
Q37.) Difference between order by and sort by
Q38.) Purpose of Zoo Keeper
Q39.) Sqoop Incremental last modified
Q40.) Difference MR1 vs MR2
Q41.) select * from table – give what results for normal table and partitioned table
Q42.) Explode and implode in hive
Q43.) Interceptors in Flume:
Q44.) Different types of distributed file systems:
Q45.) Write a pig script to extract hive table
Q46.) predefined value in sqoop to extract data from any database current date minus one
Q47.) UNION, UNIONALL, MINUS and INTERSECT available in hive ?
Q48.) Difference between Distribute by, cluster by, order by, sort by
Q49.) Describe the main hdfs-site.xml properties?
Q50.) What is Hadoop? Name the Main Parts of a Hadoop Application
Q51.) How many Data Formats are there in Hadoop?
Q52.)What do you k now about YARN?
Q53.) Why do nodes are extracted and added regularly in Hadoop cluster?
Q54.)What do you understand by “Rack Awareness”?
Q55.)What do you understand about the Speculative Execution?
Q56.)State any of the main features of Hadoop
Q57.) Do you know some organizations that are using Hadoop?
Q58.) How can you distinguish RDBMS RDBMS and Hadoop?
Q59.) What do you know about active and passive NameNodes?
Q60.)What are the Parts from Apache HBase?
Q61.) How is the DataNode failure managed by NameNode?
Q62.) Define the NameNode recovery process.
Q63.) What are the various programs available in Hadoop?
Q64.) Can DataNode and NameNode be specialty hardware?
Q65.)Whatever is that Hadoop daemon? Explain their roles.
Q66.) Define “Checkpointing”. What is its benefit?
Q67.) Name the methods in which Hadoop code can be run.
Q68.) What is the hoop’s map reduction?
Q69.) How does the Hoodoo map work?
Q70.) Explain what happens in MapReduce
Q71.) Explain the Cache distributed in MapReduce structure?
Q72.) What is the name of Hope’s name?
Q73.) What is JobTracker in Hupa? What does hatio continue to do?
Q74.) Explain what is heart rate in HDFS?
Q75.) Explain what the connectors do, and explain that you should use a partner in the mopredos work.
Q76.) What happens when a data node fails?
Q77.) What is Special Execution?
Q78.) What are the basic parameters of the modeler?
Q79.) What is the MapReduce partition function?
Q80.) What is the difference between input separation and HDFS block?
Q81.) What happens in text form?
Q82.) Do you specify the main configuration parameters that the user should be prompted to work with?
Q83.) What would you explain to WebDAV at Hadoop?
Q84.) Explain how JobTracker should schedule a task?
Q85.) Explain what is Sequencefile?
Q86.) explain what the conf.setMapper class does?
Q88.) What is the difference between RDBMS and Hadoop?
Q89.) Are you familiar with the Hatoyo Gore elements?
Q90.) What is the name of the hottest?
Q92.) What is the data storage component used by Hadus?
Q94.) What is InputSplit in Hupa?
Q95.) How to Write a Custom Partition for a Hadoop Work?
Q96.) Can I change the number of producers for a job in the hoate?
Q97.) What is a visual file in the hottie?
Q98.) What will happen to the worker?
Q99.) How is it being indexed in HDFS?
Q100.) Can search files using wilddo?
Q101.) Do you want to list three configuration files of hidopo?
Q102.) Can you verify that the Yaminot uses the JPS command?
Q103.) ‘map’ and ‘defect’ in the hottie?
Q104.) In the hottest, what file controls do you report into the hoopoe?
Q105.) To use the network needs of Hadoop?
Q106.) What is rack awareness?
Q107.) What is a task tracker in the hatton?
Q108.) What is Demons running at the edge of the master and slavery?
Q109.) How can you fix the Hope code?
Q110.) What is the calculation of storage and nodes?
Q111.) Do you mean environmental use?
Q112.) Do you refer to the next step of Mapper or Methus Dogs?
Q113.) What is the Number of Normal Participation in Hatoyobil?
Q114.) What is the purpose of recordReader in Hutcho?
Q115.) How is RDBMS different from HDFS?
Q116.) What is meant by Big Data and its five V’s?
Q117.) Explain Hadoop with components?
Q118.) Explain HDFS as well as YARN?
Q119.) What are Hadoop daemons? Explain their roles.
Q120.) How is HDFS different from NAS?
Q121.) Explain Hadoop 1 vs. Hadoop 2.
Q122.) Explain active/passive “NameNodes”?
Q123.) What is the reason for adding/removing Hadoop cluster nodes frequently?
Q124.) Can same files be accessed by two clients in HDFS?
Q125.) How are DataNode failures managed by NameNode?
Q126.) How is a down NameNode handled?
Q127.) Explain a checkpoint?
Q128.) Explain fault tolerance of HDFS?
Q129.) Explain commodity hardware in terms of NameNode or DataNode?
Q130.) Would you use HDFS for large or small data sets?
Q131.) Explain HDFS block?
Q132.) Explain jps command?
Q133.) Explain “speculative execution”?
Q134.) Can you restart “NameNode”?
Q135.) How is HDFS Block different from Input Split?
Q136.) What are three modes for Hadoop running?
Q137.) Explain MapReduce?
Q138.) Explain MapReduce configuration parameters?
Q139.) Can you perform addition in mapper?
Q140.) Explain RecordReader?
Q141.) Explain “Distributed Cache”.
Q142.) Explain communication process of reducer?
Q143.) What is “MapReduce Partitioner”?
Q144.) Explain writing process of custom partitioner?
Q145) Explain “Combiner”?
Q146.) Explain “SequenceFileInputFormat”?
Q147.) What is Apache Pig?
Q148.) Explain Pig Latin data types?
Q150.) Explain UDF?
Q151.) Explain “SerDe”?
Q152.) Can many users use the default “Hive Metastore”?
Q153.) Name the default location for “Hive” to store table data?
Q154.) Explain Apache HBase?
Q155.) Name some components in Apache HBase?
Q156.) Name Region Server components?
Q157.) Explain “WAL”?
Q114.) What is HBase properties?
Q158.)Explain Apache Spark?
Q159.) Is it possible to build “Spark” using Hadoop version?
Q160.) What is RDD.
Q161.) Explain Apache ZooKeeper?
Q162.)Explain the configuration of “Oozie” job?
Q163.) Explain Rack Awareness?