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NEW QUESTION: 1
A customer has their headquarters in London and two additional branch offices in Amsterdam and Barcelona. They want to replicate all data from their EMC Data Domain systems at the branch offices to the London headquarters' system.
What is the most efficient type of replication to achieve this goal?
A. Cascaded one-to-many
NEW QUESTION: 2
MJTelco Case Study
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.
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
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.
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
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.
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.
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
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately
Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems
both in telemetry flows and in production learning cycles.
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.
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.
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 BigQuery tables, write Google Apps Script that queries the data, calculates
the metric, and shows only suboptimal rows in a table in Google Sheets.
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 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.
D. 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
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