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NEW QUESTION: 1

Refer to the exhibit. Which two design options reduce the size of OSPF database? (Choose two)
A. Incremental SPF
B. Prefix suppression
C. Layer 2 link aggregation between core switches
D. Loop Free Alternate
E. Type 3 LSA filtering
Answer: B,C

NEW QUESTION: 2
Your network contains a Network Policy and Access Services server named Server1.
All certificates in the organization are issued by an enterprise certification authority (CA) named Server2.
You have a standalone computer named Computer1 that runs Windows 7. Computer1 has a VPN connection that connects to Server1 by using SSTP. You attempt to establish the VPN connection to Server1 and receive the following error message:
"A certificate chain processed, but terminated in a root certificate which is not trusted by the trust provider."
You need to ensure that you can successfully establish the VPN connection to Server1.
What should you do on Computer1?
A. Import the server certificate of Server1 to the user's Trusted Root Certification Authorities store.
B. Import the root certificate of Server2 to the user's Trusted Publishers store.
C. Import the root certificate of Server2 to the computer's Trusted Root Certification Authorities store.
D. Import the server certificate of Server1 to the computer#s Trusted Root Certification Authorities store.
Answer: C
Explanation:
A certificate chain establishes a "chain of trust" from a certification authority (CA) to an individual certificate. Trust occurs if a valid certificate from that CA can be found in your root certificate directory. As long as you trust the CA, you will automatically trust any other certificates signed by that CA.

NEW QUESTION: 3
バイナリ分類モデルを作成して、人に疾患があるかどうかを予測します。
分類エラーの可能性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：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:

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