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NEW QUESTION: 1
データパイプラインにセキュリティのベストプラクティスを実装しています。現在、プロジェクトオーナーとして手動でジョブを実行しています。これらのジョブを自動化するには、Google Cloud Storageから非公開情報を含む夜間のバッチファイルを取得し、Google CloudDataprocクラスターでSparkScalaジョブを使用して処理し、結果をGoogleBigQueryに保存します。
このワークロードをどのように安全に実行する必要がありますか？
A. バッチファイルの読み取りとBigQueryへの書き込みが可能なサービスアカウントを使用する
B. Cloud Dataprocクラスターでプロジェクトビューアーロールを持つユーザーアカウントを使用して、バッチファイルを読み取り、BigQueryに書き込みます
C. ファイルを表示できるのは自分だけになるようにGoogle CloudStorageバケットを制限します
D. プロジェクト所有者の役割をサービスアカウントに付与し、それを使用してジョブを実行します
Answer: D
NEW QUESTION: 2
Ein Techniker hat festgestellt, dass in der Unternehmenszentrale die Laptop-Verbindung zum Internet schneller reagiert als bei Verwendung des vom Unternehmen bereitgestellten Telefons als Hotspot. Der Techniker hat die Treiber auf der WLAN-Netzwerkkarte ohne Auflösung aktualisiert und anschließend überprüft, ob die Konfiguration auf dem WLAN korrekt ist.
Welche der folgenden Konfigurationen kann zu Leistungseinbußen führen?
A. Es liegt außerhalb der Reichweite.
B. SSID wurde nicht gefunden.
C. Die Verbindung wird gedrosselt.
D. Es liegt ein niedriges RFID-Signal vor.
Answer: 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.
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 disk size per worker
B. The zone
C. The number of workers
D. The maximum number of workers
Answer: D
