Cloudera CCA-500 Exam Questions Free Practice Test

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Questions # 1:

You are running a Hadoop cluster with MapReduce version 2 (MRv2) on YARN. You consistently see that MapReduce map tasks on your cluster are running slowly because of excessive garbage collection of JVM, how do you increase JVM heap size property to 3GB to optimize performance?

Options:

yarn.application.child.java.opts=-Xsx3072m

yarn.application.child.java.opts=-Xmx3072m

mapreduce.map.java.opts=-Xms3072m

mapreduce.map.java.opts=-Xmx3072m

Expert Solution

Questions # 2:

Which two features does Kerberos security add to a Hadoop cluster? (Choose two)

Options:

User authentication on all remote procedure calls (RPCs)

Encryption for data during transfer between the Mappers and Reducers

Encryption for data on disk (“at rest”)

Authentication for user access to the cluster against a central server

Root access to the cluster for users hdfs and mapred but non-root access for clients

Expert Solution

Questions # 3:

On a cluster running MapReduce v2 (MRv2) on YARN, a MapReduce job is given a directory of 10 plain text files as its input directory. Each file is made up of 3 HDFS blocks. How many Mappers will run?

Options:

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

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

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

Expert Solution

Questions # 4:

Given:

Question # 4

You want to clean up this list by removing jobs where the State is KILLED. What command you enter?

Options:

Yarn application –refreshJobHistory

Yarn application –kill application_1374638600275_0109

Yarn rmadmin –refreshQueue

Yarn rmadmin –kill application_1374638600275_0109

Expert Solution

Questions # 5:

You are planning a Hadoop cluster and considering implementing 10 Gigabit Ethernet as the network fabric. Which workloads benefit the most from faster network fabric?

Options:

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

Expert Solution

Questions # 6:

You are running Hadoop cluster with all monitoring facilities properly configured.

Which scenario will go undeselected?

Options:

HDFS is almost full

The NameNode goes down

A DataNode is disconnected from the cluster

Map or reduce tasks that are stuck in an infinite loop

MapReduce jobs are causing excessive memory swaps

Expert Solution

Questions # 7:

You need to analyze 60,000,000 images stored in JPEG format, each of which is approximately 25 KB. Because you Hadoop cluster isn’t optimized for storing and processing many small files, you decide to do the following actions:

1. Group the individual images into a set of larger files

2. Use the set of larger files as input for a MapReduce job that processes them directly with python using Hadoop streaming.

Which data serialization system gives the flexibility to do this?

Options:

CSV

XML

HTML

Avro

SequenceFiles

JSON

Expert Solution

Questions # 8:

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)

Options:

When Job B gets submitted, it will get assigned tasks, while job A continues to run with fewer tasks.

When Job B gets submitted, Job A has to finish first, before job B can gets scheduled.

When Job A gets submitted, it doesn’t consumes all the task slots.

When Job A gets submitted, it consumes all the task slots.

Expert Solution

Questions # 9:

Each node in your Hadoop cluster, running YARN, has 64GB memory and 24 cores. Your yarn.site.xml has the following configuration:

yarn.nodemanager.resource.memory-mb

32768

yarn.nodemanager.resource.cpu-vcores

You want YARN to launch no more than 16 containers per node. What should you do?

Options:

Modify yarn-site.xml with the following property:

yarn.scheduler.minimum-allocation-mb

2048

Modify yarn-sites.xml with the following property:

yarn.scheduler.minimum-allocation-mb

4096

Modify yarn-site.xml with the following property:

yarn.nodemanager.resource.cpu-vccores

No action is needed: YARN’s dynamic resource allocation automatically optimizes the node memory and cores

Expert Solution

Questions # 10:

Which is the default scheduler in YARN?

Options:

YARN doesn’t configure a default scheduler, you must first assign an appropriate scheduler class in yarn-site.xml

Capacity Scheduler

Fair Scheduler

FIFO Scheduler

Expert Solution

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