CCA-500 Questions And Answers
$68
Exam Name: Cloudera Certified Administrator for Apache Hadoop (CCAH)
Updated: 2022-01-20
Q & A: 60
- Reviews
Why Choose PassQuestion Cloudera CCA-500 Exam Questions
Passquestion team uses professional knowledge and experience to provide CCAH CCA-500 Questions and Answers for people ready to participate in Cloudera Certified Administrator for Apache Hadoop (CCAH) exam. The accuracy rate of CCA-500 exam questions provided by Passquestion are very high and they can 100% guarantee you pass the Cloudera CCA-500 exam successfully in the first attempt. Everyone can get CCA-500 pdf with free test engine to study. PassQuestion can promise you always have the latest version for your Cloudera CCA-500 test preparation and get your CCAH certification easily.
CCA-500 Frequently Asked Questions
Q1: Can I use CCA-500 exam Q&As in my phone?
Yes, PassQuestion provides CCAH CCA-500 pdf Q&As which you can download to study on your computer or mobile device, we also provide CCA-500 pdf free demo which from the full version to check its quality before purchasing.
Q2: What are the formats of your Cloudera CCA-500 exam questions?
PassQuestion provides Cloudera CCA-500 exam questions with pdf format and software format, pdf version can be downloaded directly from your member center.Software will be sent to your email.
Q3: How can I download my CCA-500 practice test questions after purchasing?
You can download the CCA-500 pdf directly from your member center, our colleague will send the software to your email in attachment or a download link.You need to download the link in a week, it will be automatically invalid after a week.
Q4: How long can I get my CCAH CCA-500 questions and answers after purchasing?
You can download the CCA-500 pdf directly after your purchase and we will send the software to your email in 10 minutes in our working time and no less than 12 hours in our off time.
Working Time:
GMT+8: Monday- Saturday 8:00 AM-18:00 PM
GMT: Monday- Saturday 0:00 AM-10:00 AM
Q5: Can I pass my test with your CCAH CCA-500 practice questions only?
Sure! All of PassQuestion CCAH CCA-500 practice questions come from real test. If you can practice well and get a good score in our practice Q&As, we ensure you can pass your Cloudera Certified Administrator for Apache Hadoop (CCAH) exam easily.
Q6: How can I know my CCA-500 updated?
You can check the number of questions, if it is changed,that means we have updated this exam ,you can contact us anytime to ask for an free update. our sales email : [email protected]
Q7: What is your refund process if I fail Cloudera CCA-500 test?
If you fail your CCA-500 test in 60 days by studying our study material, just scan your score report and send to us in attchment,when we check, we will give you full refund.
Q8. What other payment menthod can I use except Paypal?
If your country don't support Paypal, we offer another Payment method Western Union,it is also safe and fast. Pls contact us for the details, we will send it to your email.
Question No : 1
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?
A. HMaster
B. ResourceManager
C. TaskManager
D. JobTracker
E. NameNode
F. DataNode
A. HMaster
B. ResourceManager
C. TaskManager
D. JobTracker
E. NameNode
F. DataNode
Answer: B,E
Question No : 2
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?
A. For a 1GB child heap size an io.sort.mb of 128 MB will always maximize memory to disk I/O
B. Increase the io.sort.mb to 1GB
C. Decrease the io.sort.mb value to 0
D. Tune the io.sort.mb value until you observe that the number of spilled records equals (or is as close to equals) the number of map output records.
A. For a 1GB child heap size an io.sort.mb of 128 MB will always maximize memory to disk I/O
B. Increase the io.sort.mb to 1GB
C. Decrease the io.sort.mb value to 0
D. Tune the io.sort.mb value until you observe that the number of spilled records equals (or is as close to equals) the number of map output records.
Answer: D
Question No : 3
You want to node to only swap Hadoop daemon data from RAM to disk when absolutely necessary. What should you do?
A. Delete the /dev/vmswap file on the node
B. Delete the /etc/swap file on the node
C. Set the ram.swap parameter to 0 in core-site.xml
D. Set vm.swapfile file on the node
E. Delete the /swapfile file on the node
A. Delete the /dev/vmswap file on the node
B. Delete the /etc/swap file on the node
C. Set the ram.swap parameter to 0 in core-site.xml
D. Set vm.swapfile file on the node
E. Delete the /swapfile file on the node
Answer: D
Question No : 4
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?
A. Execute hdfs namenode ¨Creport on the command line and look at the Last Checkpoint information
B. Execute hdfs dfsadmin ¨CsaveNamespace on the command line which returns to you the last checkpoint value in fstime file
C. Connect to the web UI of the Secondary NameNode (http://mysecondary:50090/) and look at the ¡°Last Checkpoint¡± information
D. Connect to the web UI of the NameNode (http://mynamenode:50070) and look at the ¡°Last Checkpoint¡± information
Which best describes how you determine when the last checkpoint happened?
A. Execute hdfs namenode ¨Creport on the command line and look at the Last Checkpoint information
B. Execute hdfs dfsadmin ¨CsaveNamespace on the command line which returns to you the last checkpoint value in fstime file
C. Connect to the web UI of the Secondary NameNode (http://mysecondary:50090/) and look at the ¡°Last Checkpoint¡± information
D. Connect to the web UI of the NameNode (http://mynamenode:50070) and look at the ¡°Last Checkpoint¡± information
Answer: C
Question No : 5
For each YARN job, the Hadoop framework generates task log file. Where are Hadoop task log files stored?
A. Cached by the NodeManager managing the job containers, then written to a log directory on the NameNode
B. Cached in the YARN container running the task, then copied into HDFS on job completion
C. In HDFS, in the directory of the user who generates the job
D. On the local disk of the slave mode running the task
A. Cached by the NodeManager managing the job containers, then written to a log directory on the NameNode
B. Cached in the YARN container running the task, then copied into HDFS on job completion
C. In HDFS, in the directory of the user who generates the job
D. On the local disk of the slave mode running the task
Answer: D
Question No : 6
Table schemas in Hive are:
A. Stored as metadata on the NameNode
B. Stored along with the data in HDFS
C. Stored in the Metadata
D. Stored in ZooKeeper
A. Stored as metadata on the NameNode
B. Stored along with the data in HDFS
C. Stored in the Metadata
D. Stored in ZooKeeper
Answer: B
Question No : 7
What does CDH packaging do on install to facilitate Kerberos security setup?
A. Automatically configures permissions for log files at &MAPRED_LOG_DIR/userlogs
B. Creates users for hdfs and mapreduce to facilitate role assignment
C. Creates directories for temp, hdfs, and mapreduce with the correct permissions
D. Creates a set of pre-configured Kerberos keytab files and their permissions
E. Creates and configures your kdc with default cluster values
A. Automatically configures permissions for log files at &MAPRED_LOG_DIR/userlogs
B. Creates users for hdfs and mapreduce to facilitate role assignment
C. Creates directories for temp, hdfs, and mapreduce with the correct permissions
D. Creates a set of pre-configured Kerberos keytab files and their permissions
E. Creates and configures your kdc with default cluster values
Answer: B
Question No : 8
You want to understand more about how users browse your public website. For example, you want to know which pages they visit prior to placing an order. You have a server farm of 200 web servers hosting your website. Which is the most efficient process to gather these web server across logs into your Hadoop cluster analysis?
A. Sample the web server logs web servers and copy them into HDFS using curl
B. Ingest the server web logs into HDFS using Flume
C. Channel these clickstreams into Hadoop using Hadoop Streaming
D. Import all user clicks from your OLTP databases into Hadoop using Sqoop
E. Write a MapReeeduce job with the web servers for mappers and the Hadoop cluster nodes for reducers
A. Sample the web server logs web servers and copy them into HDFS using curl
B. Ingest the server web logs into HDFS using Flume
C. Channel these clickstreams into Hadoop using Hadoop Streaming
D. Import all user clicks from your OLTP databases into Hadoop using Sqoop
E. Write a MapReeeduce job with the web servers for mappers and the Hadoop cluster nodes for reducers
Answer: B
Question No : 9
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?
A. Two active NameNodes and two Standby NameNodes
B. One active NameNode and one Standby NameNode
C. Two active NameNodes and on Standby NameNode
D. Unlimited. HDFS High Availability (HA) is designed to overcome limitations on the number of NameNodes you can deploy
A. Two active NameNodes and two Standby NameNodes
B. One active NameNode and one Standby NameNode
C. Two active NameNodes and on Standby NameNode
D. Unlimited. HDFS High Availability (HA) is designed to overcome limitations on the number of NameNodes you can deploy
Answer: B
Question No : 10
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?
A. When Job B gets submitted, it will get assigned tasks, while job A continues to run with fewer tasks.
B. When Job B gets submitted, Job A has to finish first, before job B can gets scheduled.
C. When Job A gets submitted, it doesn¡¯t consumes all the task slots.
D. When Job A gets submitted, it consumes all the task slots.
A. When Job B gets submitted, it will get assigned tasks, while job A continues to run with fewer tasks.
B. When Job B gets submitted, Job A has to finish first, before job B can gets scheduled.
C. When Job A gets submitted, it doesn¡¯t consumes all the task slots.
D. When Job A gets submitted, it consumes all the task slots.
Answer: B
Question No : 11
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?
A. 4 GB
B. 17.2 GB
C. 8.9GB
D. 8.2 GB
E. 24.6 GB
<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?
A. 4 GB
B. 17.2 GB
C. 8.9GB
D. 8.2 GB
E. 24.6 GB
Answer: D
Question No : 12
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?
A. Modify yarn-site.xml with the following property:
<name>yarn.scheduler.minimum-allocation-mb</name>
<value>2048</value>
B. Modify yarn-sites.xml with the following property:
<name>yarn.scheduler.minimum-allocation-mb</name>
<value>4096</value>
C. Modify yarn-site.xml with the following property:
<name>yarn.nodemanager.resource.cpu-vccores</name>
D. No action is needed: YARN¡¯s dynamic resource allocation automatically optimizes the node memory and cores
<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?
A. Modify yarn-site.xml with the following property:
<name>yarn.scheduler.minimum-allocation-mb</name>
<value>2048</value>
B. Modify yarn-sites.xml with the following property:
<name>yarn.scheduler.minimum-allocation-mb</name>
<value>4096</value>
C. Modify yarn-site.xml with the following property:
<name>yarn.nodemanager.resource.cpu-vccores</name>
D. No action is needed: YARN¡¯s dynamic resource allocation automatically optimizes the node memory and cores
Answer: A