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- Name: ArcGIS Online Administration Associate 2024
- Exam Code: EAOA_2024
- Certification: EAOA_2024
- Vendor: Esri
- Total Question: 630
- Price: 49$
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NEW QUESTION: 1
MapMySite는 AWS VPC에서 웹 응용 프로그램을 설정합니다. 조직은 HA 및 DR 요구 사항에 대해 자체 DB 인스턴스를 사용하는 대신 AWS RDS를 사용하기로 결정했습니다.
또한 조직에서는 RDS 액세스를 보호하려고 합니다.
웹 응용 프로그램을 RDS로 어떻게 설정해야 합니까?
A. 두 개의 개별 VPC를 만들고 하나의 VPC에서 웹 응용 프로그램을 시작하고 별도의 VPC에서 RDS를 시작하고 VPC 피어링과 연결합니다.
B. VPC 내의 다른 AZ에 공용 및 두 개의 개인 서브넷을 설정하고 서브넷 그룹을 만듭니다. 해당 서브넷 그룹으로 RDS를 시작합니다.
C. 하나의 공용 서브넷과 하나의 전용 서브넷으로 VPC를 만듭니다. 공용 서브넷에서 RDS가 개인 서브넷에서 시작되는 동안 응용 프로그램 인스턴스를 시작합니다.
D. 네트워크 인터페이스를 만들고 두 개의 서브넷을 연결합니다. DB 인스턴스를 시작하는 동안 네트워크 인터페이스를 RDS와 연결하십시오.
Answer: B
Explanation:
설명
가상 사설망 (VPC)은 사용자의 AWS 계정 전용 가상 네트워크입니다. 사용자가 정의한 가상 네트워크에 RDS와 같은 AWS 리소스를 시작할 수 있습니다. 서브넷은 사용자가 보안 및 운영상의 필요에 따라 VPC 리소스 그룹에 지정할 수있는 VPC의 IP 주소 범위의 세그먼트입니다.
DB 서브넷 그룹은 사용자가 VPC에서 만들고 RDS DB 인스턴스에 할당 할 수 있는 서브넷 (일반적으로 개인)의 모음입니다. DB 서브넷 그룹을 사용하면 DB 인스턴스를 만들 때 특정 VPC를 지정할 수 있습니다. 각 DB 서브넷 그룹에는 주어진 영역에서 적어도 두 개의 가용 영역에 서브넷이 있어야 합니다.
http://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_VPC.html
NEW QUESTION: 2
Which of the following statements are true in relation to Principal Component Analysis (PCA) as applied to a system of term structures?
I. The factor weights on the first principal component will show whether there is common trend in the system II. The factors to be applied to principal components are obtained from eigenvectors of the correlation matrix III. PCA is a standard method for reducing dimensionality in data when considering a large number of correlated variables IV. The smallest absolute eigenvalues and their associated eigenvectors are the most useful for explaining most of the variation
A. I, II and III
B. I and IV
C. II and IV
D. I and III
Answer: C
Explanation:
Explanation
If you have not studied PCA prior to the preparing for the PRMIA exams, you will find the handbook to be a bit difficult to understand this topic. However, PRMIA have been asking questions about PCA on their exams so it is important to conceptually know what PCA is (and be able to answer the exam questions which are unlikely to ever require you to do a calculation).
Using PCA, a complex correlated system (eg, the rates in a term structure which are all correlated to each other) is effectively transformed into a 'principal component representation'. A principal component representation expresses each of the independent variables in the system as a linear representation based on
'principal components'. Principal components are derived from the eigenvectors of the correlation matrix.
Remember that if you have a positive semi-definite correlation matrix (which will be an n x n square matrix), it will have 'n' eigenvectors, each with an eigenvalue. The eigenvectors will be orthogonal (ie perpendicular) to each other. The eigenvalues for each of the eigenvectors determine which are principal components - in descending order of the eignevalues. Therefore the eigenvector with the highest eigenvalue is the first principal component, the second highest value the second principal component and so on. If there are n variables in the system, then eigenvalue divided by n gives us the proportion of the variation explained by that particular principal component.
In the case of a PCA done on the term structure of interest rates, the first principal component is the 'trend' component, and under a principal component representation its coefficients are given by the first eigenvector.
(By the way, remember the next two principal components are tilt and curvature respectively). If these coefficients are the same (they are, for an interest rate term structure), this means all rates move up or down when the value of the first principal component moves. Thust factor weights on the first component show whether there is common trend in the system. Statement I is correct.
The factors to be applied to the principal components are indeed obtained from the eigenvectors of the correlation matrix (and not the covariance matrix), and therefore statement II is correct.
PCA is used to reduce dimensionality, this is true and in fact one of the main reasons why PCA is used at all.
Therefore statement III is correct.
Statement IV is false as it is the largest eigenvalues and not the smallest that determine the eigenvectors affecting most of the variation (through the principal components they represent).
Thus Choice 'b' is the correct answer.
NEW QUESTION: 3
A legal right to confidentiality of client information is waived when:
A. a family member requests health care information of a client.
B. a living will takes effect.
C. the client is declared incompetent by the legal system.
D. a court system subpoenas information.
Answer: D
Explanation:
Explanation/Reference:
Explanation:
The legal right to confidentiality is waived when the court requires information to be given to the court for legal proceedings to occur (summonses, court orders, litigation information necessary for the court, subpoenas, and so on), when the state requires mandatory reporting of certain illnesses, when sharing information is necessary because a client has revealed an intent to harm himself or others, or when a client cannot make a safe and rational decision (competence). Coordinated Care
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