C1000-118 Antworten - C1000-118 Prüfungsfragen, C1000-118 Deutsche - Smart

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Exam Code: C1000-118

Exam Name: IBM Cloud Professional Architect v5

Version: V15.35

Q & A: 208 Questions and Answers

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NEW QUESTION: 1
You are a licensing specialist. Your customer is Consolidated Messenger, a messenger company.
Company Background
Consolidated Messenger has 120 employees who work in the companys office in Toronto.
The company recently acquired another messenger company that has 35 employees who work in an office in Montreal and five employees who are field sales representatives.
Consolidated Messenger anticipates a 75 percent increase in sales over the next two years.
Network Description
The Toronto office of Consolidated Messenger has 120 desktops that run Microsoft
Windows 2000 Professional. One hundred of the desktops run Microsoft Office 2000
Standard and 20 run Office XP Professional. The Toronto office also has a single server, which runs Windows 2000 Advanced Server.
The Montreal office of the newly acquired company has a single server, which runs
Microsoft Small Business Server 2000. The office also has 35 desktops that run Windows
98 and Office 97. The sales representatives connect to the server remotely by using
Terminal Server.
Current Licensing Solution
Consolidated Messenger acquired licenses through an Open Business agreement. The
Montreal company purchased software through retail channels and OEM.
Business Goals
The IT manager at Consolidated Messenger has the following goals:
Upgrade Windows 2000 Server to Windows Server 2003.
Standardize both offices on the same desktop software.
Deploy Windows Server 2003 at the Montreal office.
Manage and track all software licenses for both companies.
Deploy Microsoft Exchange Server in both offices within two years.
Due to budget constraints, Consolidated Messenger seeks a solution that can help the two companies stay current without making large software purchases.
End of repeated scenario
You need to recommend a payment structure for Consolidated Messenger.
What should you recommend?
A. Spread payments for the initial order throughout the term of the agreement. Pay for additional products as needed.
B. Pay for licenses monthly, as needed.
C. Pay up front for the initial order. Pay for additional products as needed.
Answer: A

NEW QUESTION: 2
All of the following tasks could be delegated to a nursing assistant or unlicensed assistive personnel (UAP) except:
A. monitoring/recording the amount of fluid taken.
B. assisting a client to the bathroom.
C. monitoring intravenous infusion.
D. offering fluid intake every 1-2 hours.
Answer: C
Explanation:
Explanation/Reference:
Explanation:
Monitoring an intravenous infusion must be performed by an RN or LPN. Assisting during activity, offering fluids and recording intake are in the job scope of the nursing assistant. Coordinated Care

NEW QUESTION: 3
A Security Engineer has been asked to create an automated process to disable IAM user access keys that are more than three months old.
Which of the following options should the Security Engineer use?
A. Write a script that uses the GenerateCredentialReport, GetCredentialReport, and UpdateAccessKey APIs.
B. In the AWS Console, choose the IAM service and select "Users". Review the "Access Key Age" column.
C. Define an IAM policy that denies access if the key age is more than three months and apply to all users.
D. Create an Amazon CloudWatch alarm to detect aged access keys and use an AWS Lambda function to disable the keys older than 90 days.
Answer: A

NEW QUESTION: 4
CORRECT TEXT
Problem Scenario 68 : You have given a file as below.
spark75/f ile1.txt
File contain some text. As given Below
spark75/file1.txt
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
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
For a slightly more complicated task, lets look into splitting up sentences from our documents into word bigrams. A bigram is pair of successive tokens in some sequence.
We will look at building bigrams from the sequences of words in each sentence, and then try to find the most frequently occuring ones.
The first problem is that values in each partition of our initial RDD describe lines from the file rather than sentences. Sentences may be split over multiple lines. The glom() RDD method is used to create a single entry for each document containing the list of all lines, we can then join the lines up, then resplit them into sentences using "." as the separator, using flatMap so that every object in our RDD is now a sentence.
A bigram is pair of successive tokens in some sequence. Please build bigrams from the sequences of words in each sentence, and then try to find the most frequently occuring ones.
Answer:
Explanation:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create all three tiles in hdfs (We will do using Hue}. However, you can first create in local filesystem and then upload it to hdfs.
Step 2 : The first problem is that values in each partition of our initial RDD describe lines from the file rather than sentences. Sentences may be split over multiple lines.
The glom() RDD method is used to create a single entry for each document containing the list of all lines, we can then join the lines up, then resplit them into sentences using "." as the separator, using flatMap so that every object in our RDD is now a sentence.
sentences = sc.textFile("spark75/file1.txt") \ .glom() \
map(lambda x: " ".join(x)) \ .flatMap(lambda x: x.spllt("."))
Step 3 : Now we have isolated each sentence we can split it into a list of words and extract the word bigrams from it. Our new RDD contains tuples containing the word bigram (itself a tuple containing the first and second word) as the first value and the number 1 as the second value. bigrams = sentences.map(lambda x:x.split())
\ .flatMap(lambda x: [((x[i],x[i+1]),1)for i in range(0,len(x)-1)])
Step 4 : Finally we can apply the same reduceByKey and sort steps that we used in the wordcount example, to count up the bigrams and sort them in order of descending frequency. In reduceByKey the key is not an individual word but a bigram.
freq_bigrams = bigrams.reduceByKey(lambda x,y:x+y)\
map(lambda x:(x[1],x[0])) \
sortByKey(False)
freq_bigrams.take(10)

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