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NEW QUESTION: 1
storage1という名前のAzure Storageアカウントと次の表に示すユーザーを含むAzureサブスクリプションがあります。

storage1を監視し、次の表に示す信号の電子メール通知を構成する予定です。

計画された監視に必要なアラートルールとアクショングループの最小数を特定する必要があります。
いくつのアラートルールとアクショングループを特定する必要がありますか？回答するには、回答領域で適切なオプションを選択します。
注：それぞれの正しい選択は1ポイントの価値があります。

Answer:
Explanation:

NEW QUESTION: 2
What is the process for Cisco ISE to obtain a signed certificate from a CA?
A. Submit a CSR to the CA; download the certificate from the CA; bind the CA-signed certificate with its private key, and import the CA-signed certificate into ISE.
B. Request a certificate from the CA, and import the CA-signed certificate into ISE.
C. Generate a CSR; export the CSR to the local file system and send to the CA; download the certificate from the CA, and bind the CA-signed certificate with its private key.
D. Generate a CSR; download the certificate from the CA; bind the CA-signed certificate with its private key, and import the CA-signed certificate into ISE.
Answer: C

NEW QUESTION: 3
ビジネスプロセスでは、貸借対照表のバランスシステムルールによって計算された結果を変更する必要があります。D18912E1457D5D1DDCBD40AB3BF70D5Dデフォルトの貸借対照表のバランス計算の結果を変更する適切な方法はどれですか。
A. 統合後に実行する計算マネージャーでオンデマンドルールを作成します。
B. 統合後、WebフォームまたはSmartViewを使用して番号を編集します。
C. [統合プロセス]ページで[構成可能な統合]をクリックし、構成可能な統合ルールセットを作成します。
D. [統合プロセス]ページの[最終計算]をクリックし、計算マネージャーで構成可能な計算ルールを追加します。
Answer: C
Explanation:
From the Consolidation Process page, select the Consolidated tab, and then selectConfigurable Consolidation.
Click the Create Rule Set.
Reference:
consolidation_creating_rule_sets.html

NEW QUESTION: 4
You create a binary classification model to predict whether a person has a disease.
You need to detect possible classification errors.
Which error type should you choose for each description? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation


Box 1: True Positive
A true positive is an outcome where the model correctly predicts the positive class Box 2: True Negative A true negative is an outcome where the model correctly predicts the negative class.
Box 3: False Positive
A false positive is an outcome where the model incorrectly predicts the positive class.
Box 4: False Negative
A false negative is an outcome where the model incorrectly predicts the negative class.
Note: Let's make the following definitions:
"Wolf" is a positive class.
"No wolf" is a negative class.
We can summarize our "wolf-prediction" model using a 2x2 confusion matrix that depicts all four possible outcomes:
Reference:
https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative