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NEW QUESTION: 1
다음 중 필요에 따라 스토리지를 자동으로 늘리는 MySQL 호환 데이터베이스는 무엇입니까?
A. MySQL 용 Amazon Relational Database Service (Amazon RDS)
B. 아마존 오로라
C. Amazon Lightsail
D. Amazon Elastic Compute Cloud (Amazon EC2)
Answer: B
Explanation:
Explanation
Amazon Aurora is a relational database service that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. The MySQL-compatible edition of Aurora delivers up to 5X the throughput of standard MySQL running on the same hardware, and enables existing MySQL applications and tools to run without requiring modification.
Amazon Aurora will automatically grow the size of your database volume as your database storage needs grow. Your volume will grow in increments of 10 GB up to a maximum of 64 TB. You don't need to provision excess storage for your database to handle future growth.
Reference: https://aws.amazon.com/rds/aurora/mysql-features/

NEW QUESTION: 2
レインボーテーブル攻撃とブルートフォース攻撃を区別する特性は次のうちどれですか？ （2つ選択してください。）
A. レインボーテーブルには、事前に計算されたハッシュが含まれている必要があります。
B. レインボーテーブル攻撃では、ハッシュされたパスワードへのアクセスは必要ありません。
C. レインボーテーブル攻撃は、失敗したログイン制限の最大数をバイパスします。
D. レインボーテーブル攻撃は、攻撃時の計算サイクルを大幅に削減します。
E. レインボーテーブル攻撃はネットワークで実行する必要があります。
Answer: A,C

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 visualization for operations teams with the following requirements:
Telemetry must include data from all 50,000 installations for the most recent 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. You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types.
You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?
A. Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.
B. Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.
C. Look through the current data and compose a series of charts and tables, one for each possible combination of criteria.
D. Export the data to a spreadsheet, compose a series of charts and tables, one for each possible combination of criteria, and spread them across multiple tabs.
Answer: B