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2021 Sep CCD-410 exam guide
Q21. Workflows expressed in Oozie can contain:
A. Sequences of MapReduce and Pig. These sequences can be combined with other actions including forks, decision points, and path joins.
B. Sequences of MapReduce job only; on Pig on Hive tasks or jobs. These MapReduce sequences can be combined with forks and path joins.
C. Sequences of MapReduce and Pig jobs. These are limited to linear sequences of actions with exception handlers but no forks.
D. Iterntive repetition of MapReduce jobs until a desired answer or state is reached.
Answer: A
Q22. In a MapReduce job with 500 map tasks, how many map task attempts will there be?
A. It depends on the number of reduces in the job.
B. Between 500 and 1000.
C. At most 500.
D. At least 500.
E. Exactly 500.
Answer: D
Q23. You wrote a map function that throws a runtime exception when it encounters a control character in input data. The input supplied to your mapper contains twelve such characters totals, spread across five file splits. The first four file splits each have two control characters and the last split has four control characters.
Indentify the number of failed task attempts you can expect when you run the job with mapred.max.map.attempts set to 4:
A. You will have forty-eight failed task attempts
B. You will have seventeen failed task attempts
C. You will have five failed task attempts
D. You will have twelve failed task attempts
E. You will have twenty failed task attempts
Answer: E
Q24. You need to run the same job many times with minor variations. Rather than hardcoding all job configuration options in your drive code, you’ve decided to have your Driver subclass org.apache.hadoop.conf.Configured and implement the org.apache.hadoop.util.Tool interface.
Indentify which invocation correctly passes.mapred.job.name with a value of Example to Hadoop?
A. hadoop “mapred.job.name=Example” MyDriver input output
B. hadoop MyDriver mapred.job.name=Example input output
C. hadoop MyDrive –D mapred.job.name=Example input output
D. hadoop setproperty mapred.job.name=Example MyDriver input output
E. hadoop setproperty (“mapred.job.name=Example”) MyDriver input output
Answer: C
Q25. On a cluster running MapReduce v1 (MRv1), a TaskTracker heartbeats into the JobTracker on your cluster, and alerts the JobTracker it has an open map task slot.
What determines how the JobTracker assigns each map task to a TaskTracker?
A. The amount of RAM installed on the TaskTracker node.
B. The amount of free disk space on the TaskTracker node.
C. The number and speed of CPU cores on the TaskTracker node.
D. The average system load on the TaskTracker node over the past fifteen (15) minutes.
E. The location of the InsputSplit to be processed in relation to the location of the node.
Answer: E
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Q26. 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.
Answer: D
Q27. 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.
Answer: BD
Q28. What types of algorithms are difficult to express in MapReduce v1 (MRv1)?
A. Algorithms that require applying the same mathematical function to large numbers of individual binary records.
B. Relational operations on large amounts of structured and semi-structured data.
C. Algorithms that require global, sharing states.
D. Large-scale graph algorithms that require one-step link traversal.
E. Text analysis algorithms on large collections of unstructured text (e.g, Web crawls).
Answer: C
Q29. For each input key-value pair, mappers can emit:
A. As many intermediate key-value pairs as designed. There are no restrictions on the types of those key-value pairs (i.e., they can be heterogeneous).
B. As many intermediate key-value pairs as designed, but they cannot be of the same type as the input key-value pair.
C. One intermediate key-value pair, of a different type.
D. One intermediate key-value pair, but of the same type.
E. As many intermediate key-value pairs as designed, as long as all the keys have the same types and all the values have the same type.
Answer: E
Q30. In a MapReduce job, you want each of your input files processed by a single map task. How do you configure a MapReduce job so that a single map task processes each input file regardless of how many blocks the input file occupies?
A. Increase the parameter that controls minimum split size in the job configuration.
B. Write a custom MapRunner that iterates over all key-value pairs in the entire file.
C. Set the number of mappers equal to the number of input files you want to process.
D. Write a custom FileInputFormat and override the method isSplitable to always return false.
Answer: D