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Online AZ-204 Lab Simulation, AZ-204 Valid Dumps Pdf | Developing Solutions for Microsoft Azure Reliable Exam Price - Sugakumaster
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Exam Code: AZ-204
Exam Name: Developing Solutions for Microsoft AzureCertification
Version: V16.75
Q & A: 400 Questions and Answers
AZ-204 Free Demo download
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NEW QUESTION: 1
Azure virtual for Server2の適切なサイズが必要です。
あなたは何をするべきか?回答するには、回答領域で適切なオプションを選択します。
注:それぞれの正しい選択は1ポイントの価値があります。
Answer:
Explanation:
Explanation
Box 1: Create a Recovery Services vault
Create a Recovery Services vault on the Azure Portal.
Box 2: Install the Azure Site Recovery Provider
Azure Site Recovery can be used to manage migration of on-premises machines to Azure.
Scenario: Migrate the virtual machines hosted on Server1 and Server2 to Azure.
Server2 has the Hyper-V host role.
References:
https://docs.microsoft.com/en-us/azure/site-recovery/migrate-tutorial-on-premises-azure
NEW QUESTION: 2
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.
MJTelco's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000
installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud
Dataflow pipeline configuration setting should you update?
A. The maximum number of workers
B. The zone
C. The number of workers
D. The disk size per worker
Answer: B
NEW QUESTION: 3
Which of the following attacks is dependent upon the compromise of a secondary target in order to reach the primary target?
A. Watering hole
B. Address Resolution Protocol (ARP) poisoning
C. Brute force
D. Spear phishing
Answer: B
NEW QUESTION: 4
The ethical principle of keeping professional promises or obligations is:
A. beneficence.
B. veracity.
C. fidelity.
D. autonomy.
Answer: C
Explanation:
Explanation/Reference:
Explanation:
The ethical principle of veracity is truth-telling. Autonomy is client self-determination (that is, clients making their own decisions). Beneficence is the principle of doing good, which is a foundation of nursing care.
Coordinated Care
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