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NEW QUESTION: 1
What is an advantage of using range partitioned tables?
A. Block indexes are much smaller than RID indexes, providing better performance
B. Increased query performance through data partition elimination
C. Ability to run utilities against the partitions in parallel
D. Table data is automatically and continuously clustered, requiring minimal reorganization
NEW QUESTION: 2
Why is HP well positioned for retail?
A. HP is the first in the industry to offer retail solutions.
B. HP is the world's largest technology company.
C. HP operates in over 170 countries.
D. HP offers a total end-to-end portfolio.
NEW QUESTION: 3
The implementations group has been using the test bed to do a 'proof-of-concept' that requires both Client 1 and Client 2 to access the WEB Server at 184.108.40.206. After several changes to the network addressing, routing scheme, DHCP services, NTP services, layer 2 connectivity, FHRP services, and device security, a trouble ticket has been opened indicating that Client 1 cannot ping the 220.127.116.11 address.
Use the supported commands to isolated the cause of this fault and answer the following questions.
What is the solution to the fault condition?
A. In Configuration mode, using the interface range Fa 1/0/1 - 2, then no switchport port-security interface configuration commands.
B. In Configuration mode, using the interface range Fa 1/0/1 - 2, then no switchport port-security, followed by shutdown, no shutdown interface configuration commands.
C. In Configuration mode, using the interface range Fa 1/0/1 - 2, then no switchport port-security interface configuration commands. Then in exec mode clear errdisable interface fa 1/0/1, then clear errdisable interface fa 1/0/2 commands.
D. In Configuration mode, using the interface range Fa 1/0/1 - 2, then no switchport port-security interface configuration commands. Then in exec mode clear errdisable interface fa 1/01 - 2 vlan 10 command
On ASW1, we need to remove port-security under interface fa1/0/1 & fa1/0/2.
Topology Overview (Actual Troubleshooting lab design is for below network design) Client Should have IP 10.2.1.3
EIGRP 100 is running between switch DSW1 & DSW2
OSPF (Process ID 1) is running between R1, R2, R3, R4
Network of OSPF is redistributed in EIGRP
BGP 65001 is configured on R1 with Webserver cloud AS 65002
HSRP is running between DSW1 & DSW2 Switches
The company has created the test bed shown in the layer 2 and layer 3 topology exhibits.
This network consists of four routers, two layer 3 switches and two layer 2 switches.
In the IPv4 layer 3 topology, R1, R2, R3, and R4 are running OSPF with an OSPF process number 1.
DSW1, DSW2 and R4 are running EIGRP with an AS of 10. Redistribution is enabled where necessary.
R1 is running a BGP AS with a number of 65001. This AS has an eBGP connection to AS 65002 in the ISP's network. Because the company's address space is in the private range.
R1 is also providing NAT translations between the inside (10.1.0.0/16 & 10.2.0.0/16) networks and outside (18.104.22.168/24) network.
ASW1 and ASW2 are layer 2 switches.
NTP is enabled on all devices with 22.214.171.124 serving as the master clock source.
The client workstations receive their IP address and default gateway via R4's DHCP server.
The default gateway address of 10.2.1.254 is the IP address of HSRP group 10 which is running on DSW1 and DSW2.
In the IPv6 layer 3 topology R1, R2, and R3 are running OSPFv3 with an OSPF process number 6.
DSW1, DSW2 and R4 are running RIPng process name RIP_ZONE.
The two IPv6 routing domains, OSPF 6 and RIPng are connected via GRE tunnel running over the underlying IPv4 OSPF domain. Redistrution is enabled where necessary.
Recently the implementation group has been using the test bed to do a 'proof-of-concept' on several implementations. This involved changing the configuration on one or more of the devices. You will be presented with a series of trouble tickets related to issues introduced during these configurations.
Note: Although trouble tickets have many similar fault indications, each ticket has its own issue and solution.
Each ticket has 3 sub questions that need to be answered & topology remains same.
Question-1 Fault is found on which device,
Question-2 Fault condition is related to,
Question-3 What exact problem is seen & what needs to be done for solution
Client is unable to ping IP 126.96.36.199
Steps need to follow as below:-
1. When we check on client 1 & Client 2 desktop we are not receiving DHCP address from R4 ipconfig ----- Client will be receiving IP address 10.2.1.3
2. IP 10.2.1.3 will be able to ping from R4 , but cannot ping from R3, R2, R1
3. This clearly shows problem at R4 since EIGRP is between DSW1, DSW2 & R4 and OSPF protocol is running between R4, R3, R2, R1 so routes from R4 are not propagated to R3, R2, R1
4. Since R4 is able to ping 10.2.1.3 it means that routes are received in EIGRP & same needs to be advertised in OSPF to ping from R3, R2, R1.
5. Need to check the routes are being advertised properly or not in OSPF & EIGRP vice-versa.
6. From above snap shot it clearly indicates that redistribution done in EIGRP is having problem & by default all routes are denied from ospf to EIGRP... so need to change route-map name.
7. Change required: On R4, in the redistribution of EIGRP routing protocol, we need to change name of route-map to resolve the issue. It references route-map OSPF_to_EIGRP but the actual route map is called OSPF->EIGRP.
NEW QUESTION: 4
Problem Scenario 32 : You have given three files as below.
Each file contain some text.
Apache Hadoop is an open-source software framework written in Java for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common and should be automatically handled by the framework spark3/sparkdir2/file2.txt
The core of Apache Hadoop consists of a storage part known as Hadoop Distributed File
System (HDFS) and a processing part called MapReduce. Hadoop splits files into large blocks and distributes them across nodes in a cluster. To process data, Hadoop transfers packaged code for nodes to process in parallel based on the data that needs to be processed.
his approach takes advantage of data locality nodes manipulating the data they have access to to allow the dataset to be processed faster and more efficiently than it would be in a more conventional supercomputer architecture that relies on a parallel file system where computation and data are distributed via high-speed networking
Now write a Spark code in scala which will load all these three files from hdfs and do the word count by filtering following words. And result should be sorted by word count in reverse order.
Filter words ("a","the","an", "as", "a","with","this","these","is","are","in", "for",
Also please make sure you load all three files as a Single RDD (All three files must be loaded using single API call).
You have also been given following codec
Please use above codec to compress file, while saving in hdfs.
See the explanation for Step by Step Solution and configuration.
Step 1 : Create all three files in hdfs (We will do using Hue). However, you can first create in local filesystem and then upload it to hdfs.
Step 2 : Load content from all files.
val content =
txt") //Load the text file
Step 3 : Now create split each line and create RDD of words.
val flatContent = content.flatMap(word=>word.split(" "))
step 4 : Remove space after each word (trim it)
val trimmedContent = f1atContent.map(word=>word.trim)
Step 5 : Create an RDD from remove, all the words that needs to be removed.
val removeRDD = sc.parallelize(List("a","theM,ManM, "as",
"a","with","this","these","is","are'\"in'\ "for", "to","and","The","of"))
Step 6 : Filter the RDD, so it can have only content which are not present in removeRDD.
val filtered = trimmedContent.subtract(removeRDD}
Step 7 : Create a PairRDD, so we can have (word,1) tuple or PairRDD. val pairRDD = filtered.map(word => (word,1))
Step 8 : Now do the word count on PairRDD. val wordCount = pairRDD.reduceByKey(_ +
Step 9 : Now swap PairRDD.
val swapped = wordCount.map(item => item.swap)
Step 10 : Now revers order the content. val sortedOutput = swapped.sortByKey(false)
Step 11 : Save the output as a Text file. sortedOutput.saveAsTextFile("spark3/result")
Step 12 : Save compressed output.
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