Cloudera CCA-500 - Questions & Answers
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Your cluster's mapred-start.xml includes the following parameters
<name>mapreduce.map.memory.mb</name> <value>4096</value> <name>mapreduce.reduce.memory.mb</name> <value>8192</value>
And any cluster's yarn-site.xml includes the following parameters
<name>yarn.nodemanager.vmen-pmen-ration</name> <value>2.1</value>
What is the maximum amount of virtual memory allocated for each map task before YARN will kill its Container?
Assuming you're not running HDFS Federation, what is the maximum number of NameNode daemons you should run on your cluster in order to avoid a "split-brain" scenario with your NameNode when running HDFS High Availability (HA) using Quorum-based storage?
Table schemas in Hive are:
For each YARN job, the Hadoop framework generates task log file.
Where are Hadoop task log files stored?
You have a cluster running with the fair Scheduler enabled. There are currently no jobs running on the cluster, and you submit a job A, so that only job A is running on the cluster. A while later, you submit Job B. now Job A and Job B are running on the cluster at the same time.
How will the Fair Scheduler handle these two jobs? (Choose two)
Each node in your Hadoop cluster, running YARN, has 64GB memory and 24 cores. Your yarn.site.xml has the following configuration:
<property> <name>yarn.nodemanager.resource.memory-mb</name> <value>32768</value> </property> <property> <name>yarn.nodemanager.resource.cpu-vcores</name> <value>12</value> </property>
You want YARN to launch no more than 16 containers per node.
What should you do?
You want to node to only swap Hadoop daemon data from RAM to disk when absolutely necessary.
What should you do?
You are configuring your cluster to run HDFS and MapReducer v2 (MRv2) on YARN.
Which two daemons needs to be installed on your cluster's master nodes? (Choose two)
You observed that the number of spilled records from Map tasks far exceeds the number of map output records. Your child heap size is 1GB and your io.sort.mb value is set to 1000MB.
How would you tune your io.sort.mb value to achieve maximum memory to disk I/O ratio?
You are running a Hadoop cluster with a NameNode on host mynamenode, a secondary NameNode on host mysecondarynamenode and several DataNodes.
Which best describes how you determine when the last checkpoint happened?