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NEW QUESTION: 1
5 학년과 6 학년의 RST의 시간이자 이익 비율을 비교하면 어떤 결론이 나옵니까?
A. RST의 장기 해결 능력이 향상되었습니다.
B. RST의 유동성이 향상되었습니다.
C. RST의 유동성이 떨어졌습니다.
D. RST의 장기 지급 능력이 감소했습니다.
Answer: A
Explanation:
The times-interest-earned ratio increased from 14.67 times in Year 5 44,0On - 3,00n) to
23.33 times in Year 6 70,000 - 3.000). This increase signifies that RST has more profit
available to meet the interest payments on its debt, and that long-run solvency has
improved. Consequently, creditors will RST's obligations as less risky.
An entity had the following opening and closing inventory balances during the current year:
The following transactions and events occurred during the current year 300,000 of raw materials were purchased, of which 20,000 were returned because of defects.
600,000 of direct labor costs were incurred. 750,000 of production overhead costs were incurred.
NEW QUESTION: 2
Which of the following statements are FALSE regarding recognition and rewards?
A. To be effective, recognition and rewards should be based under a person's control
B. Zero sum rewards, that only a limited number of project team members can achieve,
may undetermined team cohesiveness and performance
C. Recognition and rewards should consider cultural differences
D. Recognition and rewards are tool/ techniques of the develop project team process
E. None of the choices are correct
Answer: E
NEW QUESTION: 3
この質問については、Mountkirk Gamesのケーススタディを参照してください。
Mountkirk Gamesは、新しいゲーム用にリアルタイムの分析プラットフォームをセットアップしたいと考えています。新しいプラットフォームは、技術要件を満たす必要があります。すべての要件を満たすGoogleテクノロジーの組み合わせはどれですか？
A. Cloud Pub/Sub, Compute Engine, Cloud Storage, and Cloud Dataproc
B. Cloud SQL, Cloud Storage, Cloud Pub/Sub, and Cloud Dataflow
C. Container Engine, Cloud Pub/Sub, and Cloud SQL
D. Cloud Dataflow, Cloud Storage, Cloud Pub/Sub, and BigQuery
E. Cloud Dataproc, Cloud Pub/Sub, Cloud SQL, and Cloud Dataflow
Answer: D
Explanation:
説明
リアルタイムにはストリーム/メッセージングが必要なため、Pub / Sub、Big Queryによる分析Google独自の高速プライベートネットワークを活用したCloud Pub / Subで、世界中のどこからでも毎秒数百万のストリーミングイベントを取り込みます。 Cloud Dataflowでストリームを処理して、信頼性が高く、1回だけの低遅延データ変換を保証します。変換されたデータをクラウドネイティブのデータウェアハウジングサービスであるBigQueryにストリーミングし、SQLまたは一般的な視覚化ツールを使用して即座に分析します。
シナリオから：ゲームのバックエンドをGoogle Compute Engineにデプロイして、ストリーミングメトリックをキャプチャし、集中的な分析を実行することを計画しています。
ゲーム分析プラットフォームの要件
*ゲームアクティビティに基づいて動的に拡大または縮小する
*ゲームサーバーから直接オンザフライで着信データを処理する
*モバイルネットワークが遅いために遅れて到着するデータを処理する
* SQLクエリが少なくとも10 TBの履歴データにアクセスできるようにする
*ユーザーのモバイルデバイスによって定期的にアップロードされるファイルを処理する
*完全に管理されたサービスのみを使用する
参照：https://cloud.google.com/solutions/big-data/stream-analytics/
NEW QUESTION: 4
DRAG DROP
Answer:
Explanation: