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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 a partitioned table called tracking_table and include a TIMESTAMP column.
B. Create a table called tracking_table and include a DATE column.
C. Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.
D. Create a table called tracking_table with a TIMESTAMP column to represent the day.
Answer: A
NEW QUESTION: 2
Which two statement are correct about certified delivery confirmation? (Choose two.)
A. Automatic confirmation of message delivery upon return from the callback function is the default behavior.
B. When confirmation reaches the certified listener, the transport presents DELIVERY.CONFIRM advisory.
C. Explicit confirmation of message delivery by the listening program is the default behavior.
D. When confirmation reaches the certified sender, the transport presents DELIVERY.CONFIRM advisory.
E. When confirmation reaches the certified sender, the transport presents a DELIVERY.CONFIRM advisory.
Answer: A,D
NEW QUESTION: 3
Demand is high leading up to the Christmas holiday every year between Dec 20 and Dec 24 and not on Christmas day (Dec 25). Your customer has two demand plans. Describe the steps to model Christmas causal factor in both demand plans.
A. Open a demand plan and add a new customer specific Christmas causal factor. Create a table displaying the causal factor measure and relevant time period and modify as required. Causal factor changes in this demand plan will reflect in the 2nd demand plan also.
B. Open a demand plan and edit Christmas casual factor measure. Place value of one from Dec 20 to Dec
24 and zero for non-impacted days including Dec 25. Causal factor changes are plan specific, so repeat the steps in the 2nd demand plan.
C. Use FBDI to create a new customer specific Christmas causal factor. Place value of 1 from Dec 20 to Dec 24. Causal factor changes are plan specific, so repeat the steps in the 2nd demand plan.
D. Open a demand plan and edit Christmas casual factor measure. Place value of one from Dec 20 to Dec
24 and zero for non-impacted days including Dec 25. Causal factor changes in this demand plan will reflect in the 2nd demand plan also.
E. Use FBDI to create a new customer specific Christmas causal factor. Place value of 1 from Dec 20 to Dec 34. Causal factor upload to one demand plan will reflect in the 2nd demand plan also.
F. Open a demand plan and add a new customer specific Christmas causal factor. Create a table displaying the causal factor measure and relevant time period and modify as required. Causal factor changes are plan specific, so repeat the steps in the 2nd demand plan.
Answer: A
Explanation:
Explanation
References:
NEW QUESTION: 4
Refer to the exhibit.
How many calls are permitted by the RSVP configuration?
A. eight G.729 calls
B. one G.729 call and one G.711 call
C. two G.729 calls
D. one G.711 call
E. four G.729 calls
Answer: C
