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NEW QUESTION: 1
You have configured a Cisco ISE 1.2 deployment for self-registration of guest users. What two options can you select from to determine when the account duration timer begins?
(Choose two.)
A. BeginLogin
B. StartTime
C. FirstLogin
D. CreateTime
Answer: C,D

NEW QUESTION: 2
An EMC customer has an Avamar server integrated with a Data Domain appliance. Their administrator noticed in the Avamar Administrator GUI that the Data Domain status bar icon is showing the Data Domain system as unresponsive.
What is the most likely impact of the problem?
A. Avamar cannot retrieve information from the Data Domain system. However, backups and restores can continue during this condition.
B. Avamar cannot connect to the Data Domain system, or the Data Domain system is disabled in some way. Backups and restores do not occur during this condition.
C. Only recovery operations can be performed.
D. DD Boost cannot be used for backup operations.
Answer: A

NEW QUESTION: 3
Which of the following persons would not be required to register with the state as an agent under the guidelines of the Uniform Securities Act (USA)?
A. Stefan is a sales representative for SecureMoney Broker-Dealers and sells only mutual fund shares.
B. Keith is a salaried employee of Middlesex County in Massachusetts who sells revenue bonds issued by the county to the public.
C. Preetham is part-owner of SecureMoney Broker-Dealers and executes the purchase and sale of securities for the firm's customers.
D. John is employed by TrustUs Corporation to sell shares of the firm's stock to the firm's employees and receives a commission on the shares he sells.
Answer: B
Explanation:
Explanation/Reference:
Explanation: Keith would not have to register as an agent since he is a salaried employee of a county in Massachusetts selling county-issued bonds to the public. He is not representing a broker-dealer; he is not receiving a commission on the bonds he sells; and he is selling exempt (government-issued) securities.
John receives a commission on his sales, so he is considered to be an agent. Stefan is a sales representative employed by a broker-dealer, which makes him an agent under USA guidelines. Even though Preetham is part-owner of the broker-dealer for which he is effecting transactions, he is acting as an agent in doing so.

NEW QUESTION: 4
Case Study: 2 - MJTelco
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:
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 Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.
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 table.
Answer: A