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NEW QUESTION: 1
この質問では、下線付きのテキストを評価して正しいかどうかを判断する必要があります。
スパイウェアやウイルスを積極的にスキャンするには、Action Centerがインストールされ実行されていることを確認する必要があります。
下線が引かれている文章が正しくない場合は、正しい答えを選択してください。
下線が引かれている文章が正しい場合は、「変更不要」を選択してください。
A. 変更不要です。
B. Windows Defender
C. 悪意のあるソフトウェアの削除ツール
D. マイクロソフトセキュリティエッセンシャル
Answer: D
NEW QUESTION: 2
For this question, refer to the Mountkirk Games case study.
Mountkirk Games wants to set up a real-time analytics platform for their new game. The new platform must meet their technical requirements. Which combination of Google technologies will meet all of their requirements?
A. Container Engine, Cloud Pub/Sub, and Cloud SQL
B. Cloud Pub/Sub, Compute Engine, Cloud Storage, and Cloud Dataproc
C. Cloud Dataflow, Cloud Storage, Cloud Pub/Sub, and BigQuery
D. Cloud Dataproc, Cloud Pub/Sub, Cloud SQL, and Cloud Dataflow
E. Cloud SQL, Cloud Storage, Cloud Pub/Sub, and Cloud Dataflow
Answer: C
Explanation:
Explanation
A real time requires Stream / Messaging so Pub/Sub, Analytics by Big Query Ingest millions of streaming events per second from anywhere in the world with Cloud Pub/Sub, powered by Google's unique, high-speed private network. Process the streams with Cloud Dataflow to ensure reliable, exactly-once, low-latency data transformation. Stream the transformed data into BigQuery, the cloud-native data warehousing service, for immediate analysis via SQL or popular visualization tools.
From scenario: They plan to deploy the game's backend on Google Compute Engine so they can capture streaming metrics, run intensive analytics.
Requirements for Game Analytics Platform
* Dynamically scale up or down based on game activity
* Process incoming data on the fly directly from the game servers
* Process data that arrives late because of slow mobile networks
* Allow SQL queries to access at least 10 TB of historical data
* Process files that are regularly uploaded by users' mobile devices
* Use only fully managed services
References: https://cloud.google.com/solutions/big-data/stream-analytics/
NEW QUESTION: 3
For this question refer to the TerramEarth case study
Operational parameters such as oil pressure are adjustable on each of TerramEarth's vehicles to increase their efficiency, depending on their environmental conditions. Your primary goal is to increase the operating efficiency of all 20 million cellular and unconnected vehicles in the field How can you accomplish this goal?
A. Capture all operating data, train machine learning models that identify ideal operations, and host in Google Cloud Machine Learning (ML) Platform to make operational adjustments automatically.
B. Capture all operating data, train machine learning models that identify ideal operations, and run locally to make operational adjustments automatically.
C. Implement a Google Cloud Dataflow streaming job with a sliding window, and use Google Cloud Messaging (GCM) to make operational adjustments automatically.
D. Have your engineers inspect the data for patterns, and then create an algorithm with rules that make operational adjustments automatically.
Answer: A
Explanation:
Explanation
References: https://cloud.google.com/customers/ocado/