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NEW QUESTION: 1
How can you configure the transfer price solution in SAP S4/HANA? 2 Answers
A. Use separate accounts for each valuation
B. Use separate ledgers for each valuation
C. Use separate currency types within the same ledger for each valuation
D. Use separate segments for each valuation
Answer: A,B

NEW QUESTION: 2
Business Continuity and Disaster Recovery Planning (Primarily) addresses the:
A. Availability, Confidentiality and Integrity of the CIA triad
B. Integrity of the CIA triad
C. Availability of the CIA triad
D. Confidentiality of the CIA triad
Answer: C
Explanation:
Explanation/Reference:
Explanation:
Availability is one of the main themes behind business continuity planning, in that it ensures that the resources required to keep the business going will continue to be available to the people and systems that rely upon them.
Note: The CIA Triad, primary goals and objectives of security, is the three essential security principles of confidentiality, integrity, and availability. Vulnerabilities and risks are also evaluated based on the threat they pose against one or more of the CIA Triad principles.
Incorrect Answers:
B: Business Continuity and Disaster Recovery Planning primarily addresses availability, Confidentiality.
C: Business Continuity and Disaster Recovery Planning primarily addresses availability, not integrity.
D: Business Continuity and Disaster Recovery Planning primarily addresses availability, , Confidentiality or integrity.
References:
Harris, Shon, All In One CISSP Exam Guide, 6th Edition, McGraw-Hill, 2013, p. 888

NEW QUESTION: 3
MJTelco Case Study
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the
world. The company has patents for innovative optical communications hardware. Based on these patents,
they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to
overcome communications challenges in space. Fundamental to their operation, they need to create a
distributed data infrastructure that drives real-time analysis and incorporates machine learning to
continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the
network allowing them to account for the impact of dynamic regional politics on location availability and
cost.
Their management and operations teams are situated all around the globe creating many-to-many
relationship between data consumers and provides in their system. After careful consideration, they
decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
Scale and harden their PoC to support significantly more data flows generated when they ramp to more

than 50,000 installations.
Refine their machine-learning cycles to verify and improve the dynamic models they use to control

topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production
- to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
Scale up their production environment with minimal cost, instantiating resources when and where

needed in an unpredictable, distributed telecom user community.
Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.

Provide reliable and timely access to data for analysis from distributed research workers

Maintain isolated environments that support rapid iteration of their machine-learning models without

affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data

Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows

each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately

100m records/day
Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems

both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive
hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize
our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data
secure. We also need environments in which our data scientists can carefully study and quickly adapt our
models. Because we rely on automation to process our data, we also need our development and test
environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis.
Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on
automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to
work on our high-value problems instead of problems with our data pipelines.
You need to compose visualizations for operations teams with the following requirements:
The report must include telemetry data from all 50,000 installations for the most resent 6 weeks

(sampling once every minute).
The report must not be more than 3 hours delayed from live data.

The actionable report should only show suboptimal links.

Most suboptimal links should be sorted to the top.

Suboptimal links can be grouped and filtered by regional geography.

User response time to load the report must be <5 seconds.

Which approach meets the requirements?
A. Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries
all rows, applies a function to derive the metric, and then renders results in a table using the Google
charts and visualization API.
B. Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show
only suboptimal links in a table.
C. Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to
your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a
table.
D. Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates
the metric, and shows only suboptimal rows in a table in Google Sheets.
Answer: A

NEW QUESTION: 4
ソリューションアーキテクトは、カスタムAMIを実行するAmazon EC2インスタンスのAuto Scalingグループを含む3層アプリケーション用のAWS CloudFormationテンプレートを作成しました。
ソリューションアーキテクトは、最初にテンプレートを更新して新しいAMIを参照し、次にUpdateStackを呼び出してEC2インスタンスを新しいAMIから起動されたインスタンスに置き換えることにより、カスタムAMIの今後の更新を実行中のスタックにデプロイできるようにしたいと考えています。
これらの要件を満たすために、AMIの更新をどのように展開できますか？
A. Edit the AWS::AutoScaling:: AutoScalingGroup resource in the template, inserting an UpdatePolicy attribute.
B. Create a new stack from the updated template. Once it is successfully deployed, modify the DNS records to point to the new stack and delete the old stack.
C. Edit the AWS::AutoScaling::LaunchConfiguration resource in the template, changing its DeletionPolicy to Replace.
D. Create a change set for a new version of the template, view the changes to the running EC2 instances to ensure that the AMI is correctly updated, and then execute the change set.
Answer: D