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NEW QUESTION: 1
질문에 답하려면 아래 정보를 참조하십시오.
보안 전문가는 조직의 네트워크에 대한 클라이언트 기반 공격을 탐지합니다. 이러한 문제를 해결하려면 계획이 필요합니다.
웹 브라우저 외에 악의적 인 목적으로 사용되는 모바일 코드와 관련하여 해결해야 하는 주요 영역은 무엇입니까?
A. 이메일, 프레젠테이션 및 데이터베이스 애플리케이션
B. 이메일, 미디어 플레이어 및 인스턴트 메시징 애플리케이션
C. 텍스트 편집기, 데이터베이스 및 인터넷 전화 애플리케이션
D. 이미지 라이브러리, 프리젠 테이션 및 스프레드 시트 애플리케이션
Answer: B
NEW QUESTION: 2
The System Administrator of a financial company wants to control the network traffic during office hours, for example, social networking or streaming media traffic are forbidden during business hours (08:00 -19:00) but are permitted after 19:00 from Monday to Friday. The consultant from an IBM business partner suggested that the Schedule Object in Network
Access Policy could be used to fulfill this objective.
Which three parameters are required to create this Schedule Object? (Choose three.)
A. Expiration Date
B. Activation Date
C. Daily
D. Comment
E. End Time
F. Start Time
Answer: B,E,F
NEW QUESTION: 3
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 Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.
B. 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.
C. 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.
D. 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.
Answer: D
NEW QUESTION: 4
Given the code snippet from a compiled Java source file:
and this output:
Which command should you run to obtain this output?
A. java MyFile 1 2 2
B. java MyFile 1 2 3 4
C. java MyFile 2 2
D. java MyFile 2
Answer: A