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NEW QUESTION: 1
Which standards framework offers a set of IT Service Management best practices to assist organisations in aligning IT service delivery with business goals - including security goals?
A. COBIT
B. ITIL.
C. SABSA.
D. ISAGA.
https://www.cherwell.com/it-service-management/library/essential-guides/essential-guide-to-itil-framework-and-processes/
Answer: B
NEW QUESTION: 2
Two CKs in a CKG of RAID2.0+ can come from the same hard disk.
A. FALSE
B. TRUE
Answer: A
Explanation:
Explanation
Resolution:
CKG is a logical storage unit composed of CKs from different hard drives in the same DG according to the RAID algorithm.
The smallest unit of resources allocated by the pool from the hard disk domain.
NEW QUESTION: 3
An Alexa Skill Builder submitted a child-directed skill for certification that lists the nearest skateboard parks.
The Builder ensured that
* The skill does not link to an external account
* The skill stored the child's preference byuserId
* The skill has a valid privacy policy link in the skill Distribution page Given this information, why will the skill fail certification?
A. The child'suserIdcannot be used because of Personally Identifiable Information (PII) restrictions.
B. The skill should not be child-directed as it can be used by children over 13 years old.
C. Child-directed skills cannot use location information
D. The Builder did not upload a privacy policy document
Answer: A
NEW QUESTION: 4
二項分類モデルを作成して、人が病気にかかっているかどうかを予測します。
考えられる分類エラーを検出する必要があります。
説明ごとにどのエラータイプを選択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：正しい選択はそれぞれ1ポイントの価値があります。
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
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