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Cloudera CCD-333 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Hadoop Fundamentals | - HDFS architecture and data storage concepts - MapReduce programming model |
| Data Analysis and Optimization | - Performance tuning in MapReduce jobs - Query optimization in Hive |
| Data Processing with Hadoop Ecosystem | - Pig Latin scripting basics - Hive query language and data warehousing |
| Data Ingestion and Workflow | - Sqoop data import/export - Flume data ingestion concepts |
| Hadoop Cluster Operations | - Resource management with YARN - Cluster configuration and management basics |
Cloudera Certified Developer for Apache Hadoop Sample Questions:
Question #1
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 that finish first are used. This is called:
A. Combiner
B. Default Partitioner
C. Speculative Execution
D. IdentityReducer
E. IdentityMapper
Question #2
You are running a job that will process a single InputSplit on a cluster which has no other jobs currently running. Each node has an equal number of open Map slots. On which node will Hadoop first attempt to run the Map task?
A. The node on which this InputSplit is stored
B. The node with the most memory
C. The node with the lowest system load
D. The node with the most free local disk space
Question #3
In a MapReduce job, the reducer receives all values associated with the same key. Which statement is most accurate about the ordering of these values?
A. The values are arbitrarily ordered, and the ordering may vary from run to run of the same MapReduce job.
B. The values are arbitrarily ordered, but multiple runs of the same MapReduce job will always have the same ordering.
C. Since the values come from mapper outputs, the reducers will receive contiguous sections of sorted values.
D. The values are in sorted order.
Question #4
Which MapReduce daemon runs on each slave node and participates in job execution?
A. Secondary NameNode
B. TaskTracker
C. NameNode
D. JobTracker
Question #5
In a MapReduce job, you want each of you 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. Set the number of mappers equal to the number of input files you want to process.
B. Increase the parameter that controls minimum split size in the job configuration.
C. Write a custom FileInputFormat and override the method isSplittable to always return false.
D. Write a custom MapRunner that iterates over all key-value pairs in the entire file.
Solutions:
| Question #1 Answer: C | Question #2 Answer: A | Question #3 Answer: C | Question #4 Answer: B | Question #5 Answer: C |
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