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NEW QUESTION: 1
제조업체는 특정 성수기를 가지고 있습니다. 즉 크리스마스 시즌, 여름 시즌 및 2 월 마지막 2 주입니다. 이러한 생산량 증가 기간 동안 회사는 추가 생산 장비를 임대하고 추가 임시 직원을 고용합니다. 다음 중 어떤 예산 기술이 이 회사의 요구에 가장 적합합니까?
A. 유연한 예산.
B. 프로젝트 예산.
C. 제로 기반 예산.
D. 정적 예산 _
Answer: A
Explanation:
A flexible budget is a series of several budgets prepared for many levels of sales. It is designed to allow adjustment of the budget to the actual level of activity before comparing the budgeted activity with actual results. A firm with peak seasons may prefer flexible budgeting because of its difficulties in predicting the activity level.

NEW QUESTION: 2
Amazon QuickSight가 지원하는 모든 데이터 소스를 선택하십시오.
3 가지 정답을 선택하십시오.
A. 오로라
B. 아테나
C. DynamoDB
D. RDS
Answer: A,B,D
Explanation:
You can connect to AWS data sources , including Amazon RDS, Amazon Aurora, Amazon Redshift, Amazon Athena and Amazon S3. You can also upload Excel spreadsheets or flat files (CSV, TSV, CLF, and ELF), connect to on-premises databases like SQL Server, MySQL and PostgreSQL and import data from SaaS applications like Salesforce.
Reference:
https://quicksight.aws/faq/

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 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.
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: C