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NEW QUESTION: 1
Click the Exhibit button.
What is the outcome of the code?

A. Gobstopper Scrumdiddlyumptious
B. Compilation fails.
C. Scrumdiddlyumptious
D. Gobstopper Fizzylifting
E. Scrumdiddlyumptious Fizzylifting
Answer: D

NEW QUESTION: 2
제약 회사는 구내에서 저장된 처방전의 디지털 버전을 디지털화 했습니다. 회사는 이러한 처방을 AWS로 옮기고 그 데이터에 대한 분석을 수행하려고 합니다. 이 데이터를 사용하려면 전송 및 저장시 데이터를 암호화해야 합니다.
어떤 애플리케이션 흐름이 AWS의 데이터 보호 요구 사항을 충족합니까?
A. 디지털화 된 파일-> Amazon Kinesis Data Streams-> Kinesis Client Library 소비자-> Amazon S3-> Athena
B. 디지털화 된 파일-> Amazon Kinesis Data Analytics
C. 디지털화 된 파일-> Amazon Kinesis Data Firehose-> Amazon S3-> Amazon Athena
D. 디지털화 된 파일-> Amazon Kinesis Data Firehose-> Amazon Elasticsearch
Answer: C

NEW QUESTION: 3
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.
MJTelco's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations.
You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?
A. The zone
B. The disk size per worker
C. The maximum number of workers
D. The number of workers
Answer: A

NEW QUESTION: 4
最近、Admin1という名前のユーザーを含む新しいAzureサブスクリプションを作成しました。
Admin1は、Azure Resource Managerテンプレートを使用してAzure Marketplaceリソースをデプロイしようとします。
Admin1は、Azure PowerShellを使用してテンプレートをデプロイし、次のエラーメッセージを受け取ります。法的条件に同意するには、Azureポータル（http://go.microsoft.com/fwlink/?LinkId=534873）にアクセスして、Marketplaceアイテムのプログラムによるデプロイメントを構成するか、初めてそこに作成してください。」確認する必要があります。そのAdmin1は、Marketplaceリソースを正常にデプロイできます。
あなたは何をするべきか？
A. Azureポータルから、Microsoft.Marketplaceリソースプロバイダーを登録します。
B. Azureポータルから、課金管理者の役割をAdmin1に割り当てます。
C. Azure PowerShellから、Set-AzMarketplaceTermsコマンドレットを実行します
D. Azure PowerShellから、Set-AzApiManagementSubscriptionコマンドレットを実行します
Answer: C
Explanation:
Reference:
https://docs.microsoft.com/en-us/powershell/module/az.marketplaceordering/set-azmarketplaceterms?view=azps