Hortonworks HDP Certified Developer HDPCD Questions & Answers

• Question 31:

  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: AB

  Reference: Apache Hadoop YARN -Conceptsand; Applications

• Question 32:

  You want to Ingest log files Into HDFS, which tool would you use?

  A. HCatalog

  B. Flume

  C. Sqoop

  D. Ambari

  Correct Answer: B

• Question 33:

  Which one of the following statements is true about a Hive-managed table?

  A. Records can only be added to the table using the Hive INSERT command.

  B. When the table is dropped, the underlying folder in HDFS is deleted.

  C. Hive dynamically defines the schema of the table based on the FROM clause of a SELECT query.

  D. Hive dynamically defines the schema of the table based on the format of the underlying data.

  Correct Answer: B

• Question 34:

  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

  Correct Answer: C

  Explanation: Each file would be split into two as the block size (64 MB) is less than the file size (100 MB),

  so 200 mappers would be running.

  Note:

  If you're not compressing the files then hadoop will process your large files (say 10G), with a number of

  mappers related to the block size of the file.

  Say your block size is 64M, then you will have ~160 mappers processing this 10G file (160*64 ~= 10G).

  Depending on how CPU intensive your mapper logic is, this might be an

  acceptable blocks size, but if you find that your mappers are executing in sub minute times, then you might

  want to increase the work done by each mapper (by increasing the block size to 128, 256, 512m - the

  actual size depends on how you intend to process the data). Reference: http://stackoverflow.com/

• Question 35:

  Which one of the following statements describes a Hive user-defined aggregate function?

  A. Operates on multiple input rows and creates a single row as output

  B. Operates on a single input row and produces a single row as output

  C. Operates on a single input row and produces a table as output

  D. Operates on multiple input rows and produces a table as output

  Correct Answer: A

• Question 36:

  Which of the following tool was designed to import data from a relational database into HDFS?

  A. HCatalog

  B. Sqoop

  C. Flume

  D. Ambari

  Correct Answer: B

• Question 37:

  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.

  Correct Answer: D

  Explanation: The input types fed to the mapper are controlled by the InputFormat used. The default input

  format, "TextInputFormat," will load data in as (LongWritable, Text) pairs. The long value is the byte offset

  of the line in the file. The Text object holds the string contents of the line of the file.

  Note: The data types emitted by the reducer are identified by setOutputKeyClass()

  andsetOutputValueClass(). The data types emitted by the reducer are identified by setOutputKeyClass()

  and setOutputValueClass().

  By default, it is assumed that these are the output types of the mapper as well. If this is not the case, the

  methods setMapOutputKeyClass() and setMapOutputValueClass() methods of the JobConf class will

  override these.

  Reference: Yahoo! Hadoop Tutorial, THE DRIVER METHOD

• Question 38:

  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.

  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.

  Correct Answer: C

• Question 39:

  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.

  Correct Answer: A

  Explanation: You can use your reducer code as a combiner if the operation performed is commutative and associative.

  Reference: 24 Interview Questions and Answers for Hadoop MapReduce developers, What are combiners? When should I use a combiner in my MapReduce Job?

• Question 40:

  Given the following Pig commands:

  

  Which one of the following statements is true?

  A. The $1 variable represents the first column of data in 'my.log'

  B. The $1 variable represents the second column of data in 'my.log'

  C. The severe relation is not valid

  D. The grouped relation is not valid

  Correct Answer: B