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NEW QUESTION: 1
While you are printing checks, one of the checks gets jammed in the printer and must be reprinted.
You need to cancel the damaged check.
Which feature should you use for cancelations?
A. Void the payment
B. Undo settlement
C. Void the transaction
D. Payment reversal
Answer: A
Explanation:
Section: (none)
Explanation/Reference:
References:
https://gfeaxblog.wordpress.com/2016/09/25/void-unposted-checks/

NEW QUESTION: 2
View the Exhibit and examine the data in ORDERS_MASTER and MONTHLYjDRDERS tables.

Evaluate the following MERGE statement:
MERGE INTO orders_master o USING monthly_orders m ON (o.order_id = m.order_id) WHEN MATCHED THEN UPDATE SET o.order_total = m.order_total DELETE WHERE (m.order_total IS NULL) WHEN NOT MATCHED THEN
INSERT VALUES (m.order_id, m.order_total);
What would be the outcome of the above statement?
A. The ORDERS_MASTER table would contain the ORDERJDs 1,2 and 3.
B. The ORDERS_MASTER table would contain the ORDERJDs 1,2 and 4.
C. The ORDERS MASTER table would contain the ORDER IDs 1,2,3 and 4.
D. The ORDERS_MASTER table would contain the ORDERJDs 1 and 2.
Answer: B

NEW QUESTION: 3
Machine Learning Specialist는 AUC (Area Under the ROC Curve)를 객관적인 지표로 사용하는 Amazon SageMaker를 사용하여 트리 기반 앙상블 모델에 대한 하이퍼 파라미터 조정 작업을 시작합니다.
이 워크 플로는 결국 24 시간마다 오래된 데이터에 대한 클릭률을 모델링하기 위해 매일 밤 하이퍼 파라미터를 재 학습 및 조정하는 파이프 라인에 배포됩니다.
이러한 모델을 훈련하는 데 걸리는 시간을 줄이고 궁극적으로 비용을 줄이기 위해 전문가는 입력 하이퍼 파라미터 범위를 재구성 하려고 합니다.
이 작업을 수행하는 시각화는 무엇입니까?
A. 각 훈련 반복에 대한 객관적 메트릭의 성능을 보여주는 산점도.
B. t-Distributed Stochastic Neighbor Embedding (t-SNE)을 사용하여 많은 수의 입력 변수를 읽기 쉬운 차원으로 시각화하는 대상 변수로 색상이 지정된 점이있는 산점도입니다.
C. 최대 트리 깊이와 객관적인 메트릭 간의 상관 관계를 보여주는 산점도.
D. 가장 중요한 입력 특성이 가우스인지 여부를 보여주는 히스토그램입니다.
Answer: B
Explanation:
https://medium.com/all-things-ai/in-depth-parameter-tuning-for-random-forest-d67bb7e920d