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NEW QUESTION: 1
FIPS Publication 199によると、機密性、整合性、および可用性が損なわれた場合に、組織に及ぼす可能性のある3つのレベルは何ですか?
A. 低、中、高
B. 低、通常、高
C. 機密、秘密、および高
D. 最小、中程度、および高
Answer: A
NEW QUESTION: 2
バイナリ分類モデルを作成します。
モデルのパフォーマンスを評価する必要があります。
どの2つの指標を使用できますか?それぞれの正解は完全な解決策を提示します。
注:それぞれの正しい選択には1ポイントの価値があります。
A. accuracy
B. relative absolute error
C. precision
D. mean absolute error
E. coefficient of determination
Answer: A,C
Explanation:
The evaluation metrics available for binary classification models are: Accuracy, Precision, Recall, F1 Score, and AUC.
Note: A very natural question is: 'Out of the individuals whom the model, how many were classified correctly (TP)?' This question can be answered by looking at the Precision of the model, which is the proportion of positives that are classified correctly.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
NEW QUESTION: 3
購入スキーム詐欺のリスクを減らすのに効果的な制御方法は次のうちどれですか?
1.定期的にベンダーリストを調べて、異常なベンダーと住所を探します。
2.金額の購入、受領、出荷、および会計の職務の分離。
3.発注書の順次整合性を検証します。
4.私書箱の住所を含む請求書の有効性を確認します。
A. 1、2、3、および4
B. 1、2、および4のみ
C. 1と2のみ
D. 3および4のみ
Answer: D
NEW QUESTION: 4
What is the difference between encryption and tokenization?
A. Tokenization can be mathematically reversed to provide the original information
B. The token contains the all original information
C. Tokenization combined with hashing is always better than encryption
D. Encryption can be mathematically reversed to provide the original information
Answer: D