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NEW QUESTION: 1
Amazon Cognito로 인증하는 사용자는 여러 단계의 프로세스를 거쳐 자격 증명을 부트 스트랩합니다.
Amazon Cognito는 공익 사업자와의 인증을 위한 두 가지 흐름이 있습니다. 다음 중 두 가지 흐름은 무엇입니까?
A. 단일 단계 및 다단계
B. 향상된 기본
C. 인증 및 비인증
D. 공개 및 비공개
Answer: B
Explanation:
설명:
Amazon Cognito로 인증하는 사용자는 여러 단계의 프로세스를 거쳐 자격 증명을 부트 스트랩합니다. Amazon Cognito는 공익 사업자 인증을위한 두 가지 흐름을 가지고 있습니다.
향상된 기본.
http://docs.aws.amazon.com/cognito/devguide/identity/concepts/authentication-flow/

NEW QUESTION: 2
Your company has a Microsoft Office 365 subscription.
The company has three departments. Each department contains a group as shown in the following table.

You plan to create a team for each group.
You need to ensure that a team can be created for each group. The solution must minimize administrative effort.
What should you do to each group? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

NEW QUESTION: 3
You need to correct the model fit issue.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:
Explanation:

Explanation:
Step 1: Augment the data
Scenario: Columns in each dataset contain missing and null values. The datasets also contain many outliers.
Step 2: Add the Bayesian Linear Regression module.
Scenario: You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.
Step 3: Configure the regularization weight.
Regularization typically is used to avoid overfitting. For example, in L2 regularization weight, type the value to use as the weight for L2 regularization. We recommend that you use a non-zero value to avoid overfitting.
Scenario:
Model fit: The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.
Incorrect Answers:
Multiclass Decision Jungle module:
Decision jungles are a recent extension to decision forests. A decision jungle consists of an ensemble of decision directed acyclic graphs (DAGs).
L-BFGS:
L-BFGS stands for "limited memory Broyden-Fletcher-Goldfarb-Shanno". It can be found in the wwo-Class Logistic Regression module, which is used to create a logistic regression model that can be used to predict two (and only two) outcomes.
References:
<https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regr ession>