[2017 New Version] High Quality Cloudera CCD-410 Dumps PDF Practice Questions And Answers Update Youtube Demo

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Vendor: Cloudera
Certifications: CCDH
Exam Name: Cloudera Certified Developer for Apache Hadoop (CCDH)
Exam Code: CCD-410
Total Questions: 60 Q&As
CCD-410 dumps
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

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
Correct Answer: C

Can you use MapReduce to perform a relational join on two large tables sharing a key? Assume that the two tables are formatted as comma-separated files in HDFS.
A. Yes.
B. Yes, but only if one of the tables fits into memory
C. Yes, so long as both tables fit into memory.
D. No, MapReduce cannot perform relational operations.
E. No, but it can be done with either Pig or Hive.
Correct Answer: A

When is the earliest point at which the reduce method of a given Reducer can be called? CCD-410 dumps
A. As soon as at least one mapper has finished processing its input split.
B. As soon as a mapper has emitted at least one record.
C. Not until all mappers have finished processing all records.
D. It depends on the InputFormat used for the job.
Correct Answer: C

You’ve written a MapReduce job that will process 500 million input records and generated 500 million key-value pairs. The data is not uniformly distributed. Your MapReduce job will create a significant amount of intermediate data that it needs to transfer between mappers and reduces which is a potential bottleneck. A custom implementation of which interface is most likely to reduce the amount of intermediate data transferred across the network?
A. Partitioner
B. OutputFormat
C. WritableComparable
D. Writable
E. InputFormat
F. Combiner
Correct Answer: F

You want to populate an associative array in order to perform a map-side join. You’ve decided to put this information in a text file, place that file into the DistributedCache and read it in your Mapper before any records are processed.
Indentify which method in the Mapper you should use to implement code for reading the file and populating the associative array?
A. combine
B. map
C. init
D. configure
Correct Answer: B

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
Correct Answer: E

Assuming default settings, which best describes the order of data provided to a reducer’s reduce method:
A. The keys given to a reducer aren’t in a predictable order, but the values associated with those keys always are.
B. Both the keys and values passed to a reducer always appear in sorted order.
C. Neither keys nor values are in any predictable order.
D. The keys given to a reducer are in sorted order but the values associated with each key are in no predictable order
Correct Answer: D

How are keys and values presented and passed to the reducers during a standard sort and shuffle phase of MapReduce?
A. Keys are presented to reducer in sorted order; values for a given key are not sorted.
B. Keys are presented to reducer in sorted order; values for a given key are sorted in ascending order.
C. Keys are presented to a reducer in random order; values for a given key are not sorted.
D. Keys are presented to a reducer in random order; values for a given key are sorted in ascending order.
Correct Answer: A

MapReduce v2 (MRv2/YARN) is designed to address which two issues? CCD-410 dumps
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: BD

You want to understand more about how users browse your public website, such as which pages they visit prior to placing an order. You have a farm of 200 web servers hosting your website.
How will you gather this data for your analysis?
A. Ingest the server web logs into HDFS using Flume.
B. Write a MapReduce job, with the web servers for mappers, and the Hadoop cluster nodes for reduces.
C. Import all users’ clicks from your OLTP databases into Hadoop, using Sqoop.
D. Channel these clickstreams inot Hadoop using Hadoop Streaming.
E. Sample the weblogs from the web servers, copying them into Hadoop using curl.
Correct Answer: A

You have just executed a MapReduce job. Where is intermediate data written to after being emitted from the Mapper’s map method?
A. Intermediate data in streamed across the network from Mapper to the Reduce and is never written to disk.
B. Into in-memory buffers on the TaskTracker node running the Mapper that spill over and are written into HDFS.
C. Into in-memory buffers that spill over to the local file system of the TaskTracker node running the Mapper.
D. Into in-memory buffers that spill over to the local file system (outside HDFS) of the TaskTracker node running the Reducer
E. Into in-memory buffers on the TaskTracker node running the Reducer that spill over and are written into HDFS.
Correct Answer: C

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Last Updated on: October 26th, 2017 at 8:18 am, by admin

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