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NEW QUESTION: 1

SQL> AUDIT POLICY ORA_DATABASE_PARAMETER EXCEPT SYS; SQL> AUDIT POLICY ORA_DATABASE_PARAMETER EXCEPT SYSTEM;
SQL> AUDIT POLICY ORA_DATABASE_PARAMETER EXCEPT SCOTT;

A. Option D
B. Option B
C. Option A
D. Option C
Answer: B

NEW QUESTION: 2
After a Belt has put data through the smoothing process which chart would be used to look for trends in the data?
A. Moving Average Chart
B. Multi-Vari Chart
C. Pareto Chart
D. X bar Chart
Answer: A

NEW QUESTION: 3
A company is building a web and mobile application that uses a serverless architecture powered by AWS Lambda and Amazon API Gateway. The company wants to fully automate the backend Lambda deployment based on code that is pushed to the appropriate environment branch in an AWS CodeCommit repository.
The deployment must have the following:
- Separate environment pipelines for testing and production.
- Automatic deployment that occurs for test environments only.
Which steps should be taken to meet these requirements?
A. Create two AWS CodePipeline configurations for test and production environments. Configure the production pipeline to have a manual approval step. Create one CodeCommit repository with a branch for each environment. Set up each CodePipeline to retrieve the source code from the appropriate branch in the repository. Set up the deployment step to deploy the Lambda functions with AWS CloudFormation.
B. Configure a new AWS CodePipeline service. Create a CodeCommit repository for each environment.
Set up CodePipeline to retrieve the source code from the appropriate repository. Set up a deployment step to deploy the Lambda functions with AWS CloudFormation.
C. Create an AWS CodeBuild configuration for test and production environments. Configure the production pipeline to have a manual approval step. Create one CodeCommit repository with a branch for each environment. Push the Lambda function code to an Amazon S3 bucket. Set up the deployment step to deploy the Lambda functions from the S3 bucket.
D. Create two AWS CodePipeline configurations for test and production environments. Configure the production pipeline to have a manual approval step. Create a CodeCommit repository for each environment. Set up each CodePipeline to retrieve the source code from the appropriate repository. Set up the deployment step to deploy the Lambda functions with AWS CloudFormation.
Answer: A
Explanation:
First, A&B both are in-correct: As a basic policy - do not create a repo for the same code for multiple environments. Always create a branch from the same repo. The strategy is wrong for A&B. Now C&D: D uses Lambda function with s3, whereas C uses code pipeline to store and build. Using code pipeline is a smart choice rather than using S3 as a code pipeline that offers better branching strategy and controls.