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問題 #500
A company is building a compute-intensive application that will run on a fleet of Amazon EC2 instances. The application uses attached Amazon Elastic Block Store (Amazon EBS) volumes for storing data. The Amazon EBS volumes will be created at time of initial deployment. The application will process sensitive information. All of the data must be encrypted. The solution should not impact the application's performance.
Which solution will meet these requirements?
答案：A
問題 #501
A developer is using AWS CodeDeploy to automate a company's application deployments to Amazon EC2.
Which application specification file properties are required to ensure the software deployments do not fail?
(Select TWO.)
答案：C,E
解題說明：
Comprehensive and Detailed Step-by-Step Explanation:
To ensure successful software deployments using AWS CodeDeploy, the application specification file (appspec.yml or appspec.json) must adhere to specific requirements:
* File Format Requirement (Option B):
* The appspec.yml file is a YAML-formatted file required for defining deployment actions and file locations. This is the modern and recommended format for application specification files.
* It can also be a JSON-formatted file named appspec.json, but YAML is most commonly used and accepted.
* File Placement Requirement (Option D):
* The application specification file must reside in the root directory of the application's source code. This is necessary so that AWS CodeDeploy can detect and use the file during deployment.
* Incorrect Options:
* Option A: While a JSON-formatted file (appspec.json) is valid, this is not a mandatory requirement. YAML is also acceptable, and this option does not account for it.
* Option C: The application specification file is not required to be stored in AWS CodeBuild; it must be included in the source code's directory structure.
* Option E: The application specification file does not need to be stored in Amazon S3. S3 is commonly used for application artifacts, but the appspec.yml or appspec.json file must exist within the deployment package or source code root.
References:
AWS CodeDeploy User Guide: Application Specification File
問題 #502
An application that runs on AWS Lambda requires access to specific highly confidential objects in an Amazon S3 bucket. In accordance with the principle of least privilege a company grants access to the S3 bucket by using only temporary credentials.
How can a developer configure access to me S3 bucket in the MOST secure way?
答案：C
解題說明：
Explanation
This solution will meet the requirements by creating a Lambda function execution role, which is an IAM role that grants permissions to a Lambda function to access AWS resources such as Amazon S3 objects. The developer can attach a policy to the role that grants access to specific objects in the S3 bucket that are required by the application, following the principle of least privilege. Option A is not optimal because it will hardcode the credentials that are required to access S3 objects in the application code, which is insecure and difficult to maintain. Option B is not optimal because it will create a secret access key and access key ID with permission to access the S3 bucket, which will introduce additional security risks and complexity for storing and managing credentials. Option D is not optimal because it will store the secret access key and access key ID as environment variables in Lambda, which is also insecure and difficult to maintain.
References: [AWS Lambda Execution Role], [Using AWS Lambda with Amazon S3]
問題 #503
A company has deployed an application on AWS Elastic Beanstalk. The company has configured the Auto Scaling group that is associated with the Elastic Beanstalk environment to have five Amazon EC2 instances.
If the capacity is fewer than four EC2 instances during the deployment, application performance degrades.
The company is using the all-at-once deployment policy.
What is the MOST cost-effective way to solve the deployment issue?
答案：C
解題說明：
This solution will solve the deployment issue by deploying the new version of the application to one new EC2 instance at a time, while keeping the old version running on the existing instances. This way, there will always be at least four instances serving traffic during the deployment, and no downtime or performance degradation will occur. Option A is not optimal because it will increase the cost of running the Elastic Beanstalk environment without solving the deployment issue. Option B is not optimal because it will split the traffic between two versions of the application, which may cause inconsistency and confusion for the customers. Option D is not optimal because it will deploy the new version of the application to two existing instances at a time, which may reduce the capacity below four instances during the deployment.
References: AWS Elastic Beanstalk Deployment Policies
問題 #504
A company uses an AWS Lambda function to perform natural language processing (NLP) tasks. The company has attached a Lambda layer to the function. The Lambda layer contain scientific libraries that the function uses during processing.
The company added a large, pre-trained text-classification model to the Lambda layer. The addition increased the size of the Lambda layer to 8.7 GB. After the addition and a recent deployment, the Lambda function returned a RequestEntityTooLargeException error.
The company needs to update the Lambda function with a high-performing and portable solution to decrease the initialization time for the function.
Which solution will meet these requirements?
答案：B
解題說明：
Requirement Summary:
* NLP Lambda function with alarge pre-trained model
* Lambda layer became8.7 GB# Exceeds AWS limits
* Function returns RequestEntityTooLargeException
* Need:High-performing,portable, lowinitialization time
Important AWS Limits:
* Lambda Layers size limit(combined across all layers):250 MB (unzipped)
* Deployment package size(unzipped):250 MB
* Lambdacontainer image supportallows up to10 GBimage size
Evaluate Options:
A: Store model in S3 and load during execution
* #Leads tocold start latencyevery time
* Model loading from S3 isslowerand not suitable for real-time NLP
* Not optimal for performance
B: Use EFS mounted to Lambda
* ##Valid for large models, butadds latencyduring cold start as model loads from EFS
* Requires EFS setup, VPC, and has addednetwork I/O overhead
* Still slower than bundling in container image
C: Split into five Lambda layers
* #Still violates thetotal layer size limitof 250 MB (unzipped)
* You cannot exceed that even with multiple layers
#D: Use Docker container image
* #Allows bundlingup to 10 GBof dependencies and models
* #Highportabilityandperformance
* #Avoids latency of downloading models at runtime
* #Ideal for scientific/NLP models
* Lambda container image support:https://docs.aws.amazon.com/lambda/latest/dg/images-create.html
* Lambda limits:https://docs.aws.amazon.com/lambda/latest/dg/gettingstarted-limits.html
* Using large models with Lambda:https://aws.amazon.com/blogs/machine-learning/deploying-large- machine-learning-models-on-aws-lambda-with-container-images/
問題 #505
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