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NEW QUESTION: 1
The business analyst has been assigned to a project which delivers functionality for a much larger solution.
The project has many requirements but has not received adequate funding. In addition, the stakeholders' views conflict.
Which tools and techniques should the business analyst use to maximize business value while establishing the requirements baseline?
A. Traceability matrix, context diagrams, and scope document
B. Decision trees, weighted matrix, and context diagrams
C. Stakeholder register, decision tree, and capability matrix
D. Weighted matrix, process flow, and organizational chart
Answer: B
NEW QUESTION: 2
A company called Universal Containers would like to track bugs within Sales force. The company needs to track the bug's severity and type as well as its status and description. Bugs should be related to Cases, but the bug's owner will be different than the owner of the case. How can the Universal Containers administrator meet these requirements?
A. Create a section on the case page layout
B. Create a relationship between the standard bug object and the standard case object
C. Create a field on cases
D. Create a custom object for bugs and relate it to cases
Answer: D
Explanation:
Explanation/Reference:
Explanation/Reference:
The relationship would be established using a look up to the cases object so that independent ownership can be established. It will also allow the related bugs to be visible as a related list on the cases screen.
NEW QUESTION: 3
대형 소비재 제조업체는 다음과 같은 제품을 판매합니다
* 34 가지의 치약 변형
* 48 가지 칫솔 변형
* 43 가지 구강 세척제
이 모든 제품의 전체 판매 내역은 Amazon S3에서 확인할 수 있습니다. 현재이 회사는 맞춤형 자동 회귀 통합 이동 평균 (ARIMA) 모델을 사용하여 해당 제품에 대한 수요를 예측하고 있습니다. 회사는 곧 새로운 제품에 대한 수요를 예측하려고 합니다. 머신 러닝 전문가가 어떤 솔루션을 적용해야 합니까?
A. 맞춤형 ARIMA 모델을 교육하여 신제품에 대한 수요를 예측하십시오.
B. Amazon SageMaker k- 평균 클러스터링 알고리즘을 훈련시켜 신제품에 대한 수요를 예측하십시오.
C. 새로운 제품에 대한 수요를 예측하기 위해 사용자 정의 XGBoost 모델을 훈련
D. Amazon SageMaker DeepAR 알고리즘을 훈련하여 새로운 제품에 대한 수요 예측
Answer: D
Explanation:
Explanation
The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future.