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NEW QUESTION: 1
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.
Given the record streams MJTelco is interested in ingesting per day, they are concerned about the cost of Google BigQuery increasing. MJTelco asks you to provide a design solution. They require a single large data table called tracking_table. Additionally, they want to minimize the cost of daily queries while performing fine-grained analysis of each day's events. They also want to use streaming ingestion. What should you do?
A. Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.
B. Create a partitioned table called tracking_table and include a TIMESTAMP column.
C. Create a table called tracking_table with a TIMESTAMP column to represent the day.
D. Create a table called tracking_table and include a DATE column.
Answer: B

NEW QUESTION: 2
Why should the data store be based on a clustered configuration in the New York datacenter?
A. This solution maintains manageability of the server farm at all times.
B. Explanation:
QUESTIONNO: 142 Why should the data store be based in New York, with a replicated copy of the data store in San Francisco?
A. This solution maintains manageability of the XenApp farm if the primary datacenter fails.
B. The data store must be located in the primary datacenter.
C. A replicated configuration is the only supported method for redundant data stores.
D. This solution maintains manageability of the farm at all times.
C. The data store must be located in the primary datacenter.
D. This solution reduceslogon times.
E. A clustered configuration is the only supported method for redundant data stores.
Answer: A,B

NEW QUESTION: 3



A. Option B
B. Option C
C. Option A
D. Option D
Answer: B
Explanation:
CNAME record A CNAME record is an abbreviation for Canonical Name record and is a type of resource record in the Domain Name System (DNS) that specifies that the domain name is an alias of another, canonical domain name. Here "canonical" usually means: a more generally accepted or standard name.
For example, if there is a DNS zone as follows: NAME TYPE VALUE
bar.example.com. CNAME foo.example.com. foo.example.com. A 192.0.2.23
MX record A mail exchanger record (MX record) is a type of resource record in the Domain Name System that specifies a mail server responsible for accepting email messages on behalf of a recipient's domain, and a preference value used to prioritize mail delivery if multiple mail servers are available. The set of MX records of a domain name specifies how email should be routed with the Simple Mail Transfer Protocol (SMTP).
A Record An A (address) record is a DNS record that can be used to point your domain name and host names to a static IP address.
NOT A B D Need to increase the cost associated with the MX record of Server2 so that Server1 is preferred over Server2 but Server2 will be used if Server1 is not available.
Need to increase the cost associated with the MX record of Server2 so that Server1 is preferred over Server2 but Server2 will be used if Server1 is not available. There is already a CNAME (Alias) associated with mail.contoso.com which points to Server1.contoso.com
Record Name Record Type Data Contoso.com Mail Exchanger (MX) [20] mail2.contoso.com Mail2 Alias CNAME Server2.contoso.com

NEW QUESTION: 4
What is a circular dependency in AWS CloudFormation?
A. When a Template references an earlier version of itself.
B. When Resources form a Depend On loop.
C. When a Template references a region, which references the original Template.
D. When Nested Stacks depend on each other.
Answer: B
Explanation:
설명
To resolve a dependency error, add a Depends On attribute to resources that depend on other resources in your template. In some cases, you must explicitly declare dependencies so that AWS CloudFormation can create or delete resources in the correct order. For example, if you create an Elastic IP and a VPC with an Internet gateway in the same stack, the Elastic IP must depend on the Internet gateway attachment. For additional information, see Depends On Attribute.
http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/troubleshooting.html#troubleshooting-error

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