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Cloudera Certified Administrator for Apache Hadoop CDH4 Sample Questions:

1. You have a cluster running with the fair scheduler enabled and configured. You submit multiple jobs to the cluster. Each job is assigned to a pool. What are the two key points to remember about how jobs are scheduled with the fair scheduler?

A) Each pool gets 1/M of the total available tasks slots, where M is the no. of nodes in the cluster
B) Pools get a dynamically-allocated share of the available task slots (subject to additional constraints)
C) Each pools share of the tasks slots remains static within the execution of any individual job
D) Pools are assigned priorites.pools with higher priorities an executed b4 pools with lower priorities
E) Each pools share of task slots may change throughout the course of job execution
F) Each pool gets 1/N of the total available tasks slots, where N is the no of jobs running on the cluster

2. What occurs when you run a hadoop job specifying output directory for job output which already exists in HDFS?

A) the job will run successfully. Output from the reducers will override the contents of existing directory
B) An error will occur immediately because the output directory must not already exist.
C) an error will occur after the mappers have completed but before any reducers begin to run because the output path must not exist during the shuffle and sort.
D) the job will run successfully output from the reducers will be placed in a directory called job output -1

3. You set mapred.tasktracker.reduce.tasks.maximum to a value of 4 on a cluster running mapreduce v1 (MRv1). How many reducers will run for any given job?

A) Four reducer's will run. Once set by the cluster administrator, this parameter can't be overridden
B) a maximum of 4 reducer's, but the actual number of reducer's that run for any given job is based on the volume intermediate data.
C) The number of reducer's for any given job is set by the developer
D) Maximum of 4 reducers, but the actual number of reducers that run for any given job is based on the volume of input data

4. For a MapReduce job, on a cluster running MapReduce v1 (MRv1), what's the relationship between tasks and task templates?

A) The developer sets the number of task attempts on job submission.
B) There are always exactly as many task attempts as there are tasks.
C) There are always at least as many task attempts as there are tasks.
D) There are always at most as many tasks attempts as there are tasks.

5. When planning a hadoop cluster, what general rule governs the hardware requirements between master nodes and slave nodes?

A) The master nodes require more memory and greater disk capacity then the slave nodes
B) The master nodes requires more memory and no disk drives
C) the master nodes requires less memory and fewer number disk drives than the slave nodes
D) The master and slave nodes should have the same hardware configuration
E) The master nodes requires more memory but less disk capacity

Solutions: