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Cloudera CCA-505 試験シラバストピック:

Cloudera Certified Administrator for Apache Hadoop (CCAH) CDH5 Upgrade 認定 CCA-505 試験問題：

1. Assume you have a file named foo.txt in your local directory. You issue the following three commands:
Hadoop fs -mkdir input
Hadoop fs -put foo.txt input/foo.txt
Hadoop fs -put foo.txt input
What happens when you issue that third command?

A) You get a warning that foo.txt is being overwritten
B) The file is uploaded and stored as a plain named input
C) You get an error message telling you that input is not a directory
D) You get a error message telling you that foo.txt already exists. The file is not written to HDFS
E) The write succeeds, overwriting foo.txt in HDFS with no warning
F) You get an error message telling you that foo.txt already exists, and asking you if you would like to overwrite
G) The write silently fails

2. Your cluster's mapped-site.xml includes the following parameters
<name>mapreduce.map.memory.mb</name>
<value>4096<value/>
<name>mapreduce.reduce.memory,mb</name>
<value>8192</value>
And your cluster's yarn-site.xml includes the following parameters
<name>yarn.nodemanager/vmen-pmem-ratio</name>
<value>2.1</value>
What is the maximum amount of virtual memory allocated for each map before YARN will kill its Container?

A) 8.2 GB
B) 24.6 GB
C) 17.2 GB
D) 4 GB

3. You have converted your Hadoop cluster from a MapReduce 1 (MRv1) architecture to a MapReduce 2 (MRv2) on YARN architecture. Your developers are accustomed to specifying map and reduce tasks (resource allocation) tasks when they run jobs. A developer wants to know how specify to reduce tasks when a specific job runs. Which method should you tell that developer to implement?

A) MapReduce version 2 (MRv2) on YARN abstracts resource allocation away from the idea of "tasks" into memory and virtual cores, thus eliminating the need for a developer to specify the number of reduce tasks, and indeed preventing the developer from specifying the number of reduce tasks.
B) In YARN, the ApplicationMaster is responsible for requesting the resources required for a specific job. Thus, executing -p yarn.applicationmaster.reduce.tasks-2 will specify that the ApplicationMaster launch two task containers on the worker nodes.
C) In YARN, resource allocation is a function of megabytes of memory in multiple of 1024mb. Thus, they should specify the amount of memory resource they need by executing -D mapreduce.reduce.memory-mp-2040
D) In YARN, resource allocation is a function of virtual cores specified by the ApplicationMaster making requests to the NodeManager where a reduce task is handled by a single container (and this a single virtual core). Thus, the developer needs to specify the number of virtual cores to the NodeManager by executing -p yarn.nodemanager.cpuvcores=2
E) Developers specify reduce tasks in the exact same way for both MapReduce version 1 (MRv1) and MapReduce version 2 (MRv2) on YARN. Thus, executing -p mapreduce.job.reduce-2 will specify 2 reduce tasks.

4. You are migrating a cluster from MapReduce version 1 (MRv1) to MapReduce version2 (MRv2) on YARN. To want to maintain your MRv1 TaskTracker slot capacities when you migrate. What should you do?

A) Configure yarn.nodemanager.resource.memory-mb and yarn.nodemanager.resource.cpu-vcores to match the capacity you require under YARN for each NodeManager
B) You don't need to configure or balance these properties in YARN as YARN dynamically balances resource management capabilities on your cluster
C) Configure yarn.applicationmaster.resource.memory-mb and yarn.applicationmaster.cpuvcores so that ApplicationMaster container allocations match the capacity you require.
D) Configure mapred.tasktracker.map.tasks.maximum and mapred.tasktracker.reduce.tasks.maximum ub yarn.site.xml to match your cluster's configured capacity set by yarn.scheduler.minimum-allocation

5. Identify two features/issues that YARN is designed to address:

A) Resource pressures on the JobTracker
B) Reduce complexity of the MapReduce APIs
C) Ability to run frameworks other than MapReduce, such as MPI
D) Standardize on a single MapReduce API
E) HDFS latency
F) Single point of failure in the NameNode

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