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NEW QUESTION: 1
会社のデータの多くは頻繁にアクセスする必要がなく、取得に数時間かかる可能性があるため、Amazon Glacierに保存されます。ただし、組織内の誰かが、自分のデータが他のデータよりも機密性が高いという懸念を表明しており、S3で知っている高レベルの暗号化がより安価なGlacierサービスでも使用されているのではないかと考えています。この懸念に関して、次のうちどれが最も当てはまりますか？
A. Amazon Glacierは、AES-128を使用してAmazon S3よりも低い暗号化方式を使用してデータを自動的に暗号化しますが、支払いを希望する場合はAES-256に変更できます。
B. Amazon Glacierは、Amazon S3と同じAES-256を使用してデータを自動的に暗号化します。
C. Amazon Glacierには暗号化がないため、安価です。
D. Amazon Glacierは、AES-128を使用してAmazon S3よりも低い暗号化方式でデータを自動的に暗号化します。
Answer: B
Explanation:
Amazon S3と同様に、Amazon Glacierサービスは、低コストで安全で耐久性のあるストレージを提供します。ただし、S3が高速検索用に設計されている場合、Glacierは、頻繁にアクセスされず、数時間の検索時間が適切なデータのアーカイブサービスとして使用することを目的としています。
Amazon Glacierは、AES-256を使用してデータを自動的に暗号化し、不変の形式で永続的に保存します。 Amazon Glacierは、年間平均耐久性を提供するように設計されています
アーカイブの場合は99.999999999％。各アーカイブを複数の施設と複数のデバイスに保存します。
面倒なデータ検証と手動修復が必要な従来のシステムとは異なり、Glacierは定期的かつ体系的なデータ整合性チェックを実行し、自動的に自己修復するように構築されています。
参照：http://d0.awsstatic.com/whitepapers/Security/AWS%20Security%20Whitepaper.pdf

NEW QUESTION: 2
Which of the following lines includes a payment method object?
A. $quote->getShippingAddress ( )->setPaymentMethod(isset ($data['method']) ? $data['method'] : null); inside Mage_Checkout_Model_Type_Onepage
B. $method = $this->getMethodInstance ( ); inside Mage_Sales_Model_Quote_Payment
C. $instance->setInfoInstance($this); inside Mage_Payment_Model_Info
D. .public function setPayment(Mage_Sales_Model_Quote_Payment $payment) inside Mage_Sales_Model_Quote
Answer: B

NEW QUESTION: 3
If there is an error created by reading or writing data from a device attached to a Windows system, what should be checked for information?
A. errpt
B. error report
C. syslog
D. event log
Answer: D

NEW QUESTION: 4
群衆感情モデルの評価戦略を定義する必要があります。
順番に実行する必要がある3つのアクションはどれですか？回答するには、適切なアクションをアクションのリストから回答エリアに移動し、正しい順序に並べます。

Answer:
Explanation:

Explanation:
Scenario:
Experiments for local crowd sentiment models must combine local penalty detection data.
Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds.
Note: Evaluate the changed in correlation between model error rate and centroid distance In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean (centroid) is closest to the observation.
References:
https://en.wikipedia.org/wiki/Nearest_centroid_classifier
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/sweep-clustering