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NEW QUESTION: 1
Which of the following actions is NOT an adverse action?
A. Refusal to increase the amount of credit on an existing account after a request
B. Refusal to grant credit on substantially the same terms and conditions as requested by the applicant
C. Termination of an account
D. Refusal to grant credit on the grounds that the lender does not offer the type of credit requested
Answer: D

NEW QUESTION: 2

A. Option A
B. Option C
C. Option B
D. Option D
Answer: C

NEW QUESTION: 3
What are two benefits of using SPAN with promiscuous mode deployment? (Choose two.)
A. SPAN can analyze network traffic from multiple points.
B. SPAN does not introduce latency to network traffic.
C. Promiscuous Mode can silently block traffic flows on the IDS.
D. SPAN can perform granular scanning on captures of per-IP-address or per-port monitoring.
Answer: A,B

NEW QUESTION: 4
広告応答のモデリング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:
Step 1: Implement a K-Means Clustering model
Step 2: Use the cluster as a feature in a Decision jungle model.
Decision jungles are non-parametric models, which can represent non-linear decision boundaries.
Step 3: Use the raw score as a feature in a Score Matchbox Recommender model The goal of creating a recommendation system is to recommend one or more "items" to "users" of the system. Examples of an item could be a movie, restaurant, book, or song. A user could be a person, group of persons, or other entity with item preferences.
Scenario:
Ad response rated declined.
Ad response models must be trained at the beginning of each event and applied during the sporting event.
Market segmentation models must optimize for similar ad response history.
Ad response models must support non-linear boundaries of features.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/multiclass-decision-jungle
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/score-matchbox-recommender