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Exam Number/Code: CCD-410
Exam name: Cloudera Certified Developer for Apache Hadoop (CCDH)
n questions with full explanations
Certification: Cloudera Certification
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Exam Code: CCD-410 (Practice Exam Latest Test Questions VCE PDF)
Exam Name: Cloudera Certified Developer for Apache Hadoop (CCDH)
Certification Provider: Cloudera
Free Today! Guaranteed Training- Pass CCD-410 Exam.
Q11. You want to perform analysis on a large collection of images. You want to store this data in HDFS and process it with MapReduce but you also want to give your data analysts and data scientists the ability to process the data directly from HDFS with an interpreted high-level programming language like Python. Which format should you use to store this data in HDFS?
Q12. You need to perform statistical analysis in your MapReduce job and would like to call methods in the Apache Commons Math library, which is distributed as a 1.3 megabyte Java archive (JAR) file. Which is the best way to make this library available to your MapReducer job at runtime?
A. Have your system administrator copy the JAR to all nodes in the cluster and set its location in the HADOOP_CLASSPATH environment variable before you submit your job.
B. Have your system administrator place the JAR file on a Web server accessible to all cluster nodes and then set the HTTP_JAR_URL environment variable to its location.
C. When submitting the job on the command line, specify the –libjars option followed by the JAR file path.
D. Package your code and the Apache Commands Math library into a zip file named JobJar.zip
Q13. 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.
Q14. 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.
Q15. 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
Q16. 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?
Q17. You are developing a combiner that takes as input Text keys, IntWritable values, and emits Text keys, IntWritable values. Which interface should your class implement?
A. Combiner <Text, IntWritable, Text, IntWritable>
B. Mapper <Text, IntWritable, Text, IntWritable>
C. Reducer <Text, Text, IntWritable, IntWritable>
D. Reducer <Text, IntWritable, Text, IntWritable>
E. Combiner <Text, Text, IntWritable, IntWritable>
Q18. Which process describes the lifecycle of a Mapper?
A. The JobTracker calls the TaskTracker’s configure () method, then its map () method and finally its close () method.
B. The TaskTracker spawns a new Mapper to process all records in a single input split.
C. The TaskTracker spawns a new Mapper to process each key-value pair.
D. The JobTracker spawns a new Mapper to process all records in a single file.
Q19. The Hadoop framework provides a mechanism for coping with machine issues such as faulty configuration or impending hardware failure. MapReduce detects that one or a number of machines are performing poorly and starts more copies of a map or reduce task. All the tasks run simultaneously and the task finish first are used. This is called:
D. Default Partitioner
E. Speculative Execution
Q20. 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.