CCA-500 Practice Exam Questions with Answers Cloudera Certified Administrator for Apache Hadoop (CCAH) Certification

Cached by the NodeManager managing the job containers, then written to a log directory on the NameNode

Cached in the YARN container running the task, then copied into HDFS on job completion

In HDFS, in the directory of the user who generates the job

On the local disk of the slave mode running the task

You must modify the configuration files on the NameNode only. DataNodes read their configuration from the master nodes

You must modify the configuration files on each of the DataNodes machines

You don’t need to restart any daemon, as they will pick up changes automatically

You must restart the NameNode daemon to apply the changes to the cluster

You must restart all six DatNode daemon to apply the changes to the cluster

CSV

XML

HTML

Avro

SequenceFiles

JSON

Sample the web server logs web servers and copy them into HDFS using curl

Ingest the server web logs into HDFS using Flume

Channel these clickstreams into Hadoop using Hadoop Streaming

Import all user clicks from your OLTP databases into Hadoop using Sqoop

Write a MapReeeduce job with the web servers for mappers and the Hadoop cluster nodes for reducers

fstime

VERSION

Fsimage_N (where N reflects transactions up to transaction ID N)

Edits_N-M (where N-M transactions between transaction ID N and transaction ID N)

ApplicationManager

JobTracker

ApplicationMaster

JobHistoryServer

ResoureManager

NodeManager

We cannot say; the number of Mappers is determined by the ResourceManager

We cannot say; the number of Mappers is determined by the developer

30

3

10

We cannot say; the number of mappers is determined by the ApplicationMaster

SampleJar.Jar is sent to the ApplicationMaster which allocates a container for SampleJar.Jar

Sample.jar is placed in a temporary directory in HDFS

SampleJar.jar is sent directly to the ResourceManager

SampleJar.jar is serialized into an XML file which is submitted to the ApplicatoionMaster

The client queries the NameNode for the locations of the block, and reads all three copies. The first copy to complete transfer to the client is the one the client reads as part of hadoop’s speculative execution framework.

The client queries the NameNode for the locations of the block, and reads from the first location in the list it receives.

The client queries the NameNode for the locations of the block, and reads from a random location in the list it receives to eliminate network I/O loads by balancing which nodes it retrieves data from any given time.

The client queries the NameNode which retrieves the block from the nearest DataNode to the client then passes that block back to the client.

HMaster

ResourceManager

TaskManager

JobTracker

NameNode

DataNode

When your workload generates a large amount of output data, significantly larger than the amount of intermediate data

When your workload consumes a large amount of input data, relative to the entire capacity if HDFS

When your workload consists of processor-intensive tasks

When your workload generates a large amount of intermediate data, on the order of the input data itself

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.

In YARN, resource allocations is a function of megabytes of memory in multiples of 1024mb. Thus, they should specify the amount of memory resource they need by executing –D mapreduce-reduces.memory-mb-2048

In YARN, the ApplicationMaster is responsible for requesting the resource required for a specific launch. Thus, executing –D yarn.applicationmaster.reduce.tasks=2 will specify that the ApplicationMaster launch two task contains on the worker nodes.

Developers specify reduce tasks in the exact same way for both MapReduce version 1 (MRv1) and MapReduce version 2 (MRv2) on YARN. Thus, executing –D mapreduce.job.reduces-2 will specify reduce tasks.

In YARN, resource allocation is function of virtual cores specified by the ApplicationManager making requests to the NodeManager where a reduce task is handeled by a single container (and thus a single virtual core). Thus, the developer needs to specify the number of virtual cores to the NodeManager by executing –p yarn.nodemanager.cpu-vcores=2

The file will remain under-replicated until the administrator brings that node back online

The cluster will re-replicate the file the next time the system administrator reboots the NameNode daemon (as long as the file’s replication factor doesn’t fall below)

This will be immediately re-replicated and all other HDFS operations on the cluster will halt until the cluster’s replication values are resorted

The file will be re-replicated automatically after the NameNode determines it is under-replicated based on the block reports it receives from the NameNodes