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NEW QUESTION: 1
Note: This question is a part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
Your company has 1,000 users in a Microsoft Office 365 subscription.
A Power BI administrator named Admin1 creates 20 dashboards and shares them with 50 users.
You discover that a use name User1 can access all the dashboards.
You need to prevent User1 from accessing all the dashboards.
Solution: From the Power BI Admin portal, you modify the Dashboard settings.
Does this meet the goal?
A. Yes
B. No
Answer: B
Explanation:
Explanation
References:
https://docs.microsoft.com/en-us/power-bi/service-admin-administering-power-bi-in-your-organization#how-do-

NEW QUESTION: 2
프로젝트 시작 단계에서 프로젝트 관리자는 비즈니스 사례를 검토하고 정보가 불완전하다는 것을 알게됩니다. 프로젝트 관리자는 어떻게 해야 합니까?
A. SOW (Protecting Statement of Work)를 검토하여 프로젝트 헌장 작성
B. 위험 레지스터에 정보 격차 추가
C. 스폰서 조직과 비즈니스 사례를 검토하여 프로젝트가 비즈니스 요구에 부합하는지 확인
D. 보호 범위를 좁히고 보호 헌장을 작성하기 위해 비즈니스 사례 수정
Answer: C

NEW QUESTION: 3
You have a model with a large difference between the training and validation error values.
You must create a new model and perform cross-validation.
You need to identify a parameter set for the new model using Azure Machine Learning Studio.
Which module you should use for each step? To answer, drag the appropriate modules to the correct steps.
Each module may be used once or more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Box 1: Split data
Box 2: Partition and Sample
Box 3: Two-Class Boosted Decision Tree
Box 4: Tune Model Hyperparameters
Integrated train and tune: You configure a set of parameters to use, and then let the module iterate over multiple combinations, measuring accuracy until it finds a "best" model. With most learner modules, you can choose which parameters should be changed during the training process, and which should remain fixed.
We recommend that you use Cross-Validate Model to establish the goodness of the model given the specified parameters. Use Tune Model Hyperparameters to identify the optimal parameters.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample