Amazon SCS-C02 Practice Test - Questions Answers, Page 27

The correct answer is C. Specify Amazon CloudWatch as the destination for the access logs. Export the CloudWatch logs to an Amazon S3 bucket. Use Amazon Athena to query the logs and to filter the logs by host.

According to the AWS documentation1, AWS WAF offers logging for the traffic that your web ACLs analyze. The logs include information such as the time that AWS WAF received the request from your protected AWS resource, detailed information about the request, and the action setting for the rule that the request matched. You can send your logs to an Amazon CloudWatch Logs log group, an Amazon Simple Storage Service (Amazon S3) bucket, or an Amazon Kinesis Data Firehose.

To create a log analysis solution for the AWS WAF web ACLs, you can use Amazon Athena, which is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL2. You can use Athena to query and filter the AWS WAF logs by host or any other criteri

a. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

To use Athena with AWS WAF logs, you need to export the CloudWatch logs to an S3 bucket. You can do this by creating a subscription filter that sends your log events to a Kinesis Data Firehose delivery stream, which then delivers the data to an S3 bucket3. Alternatively, you can use AWS DMS to migrate your CloudWatch logs to S34.

After you have exported your CloudWatch logs to S3, you can create a table in Athena that points to your S3 bucket and use the AWS service log format that matches your log schema5. For example, if you are using JSON format for your AWS WAF logs, you can use the AWSJSONSerDe serde. Then you can run SQL queries on your Athena table and filter the results by host or any other field in your log data.

Therefore, this solution meets the requirements of creating a log analysis solution for the AWS WAF web ACLs with the most operational efficiency. This solution does not require setting up any additional infrastructure or services, and it leverages the existing capabilities of CloudWatch, S3, and Athena.

The other options are incorrect because:

A) Specifying Amazon Redshift as the destination for the access logs is not possible, because AWS WAF does not support sending logs directly to Redshift. You would need to use an intermediate service such as Kinesis Data Firehose or AWS DMS to load the data from CloudWatch or S3 to Redshift. Deploying the Amazon Athena Redshift connector is not necessary, because you can query Redshift data directly from Athena without using a connector6. This solution would also incur additional costs and operational overhead of managing a Redshift cluster.

B) Specifying Amazon CloudWatch as the destination for the access logs is possible, but using Amazon CloudWatch Logs Insights to design a query to filter the logs by host is not efficient or scalable. CloudWatch Logs Insights is a feature that enables you to interactively search and analyze your log data in CloudWatch Logs7. However, CloudWatch Logs Insights has some limitations, such as a maximum query duration of 20 minutes, a maximum of 20 log groups per query, and a maximum retention period of 24 months8. These limitations may affect your ability to perform complex and long-running analysis on your AWS WAF logs.

D) Specifying Amazon CloudWatch as the destination for the access logs is possible, but using Amazon Redshift Spectrum to query the logs and filter them by host is not efficient or cost-effective. Redshift Spectrum is a feature of Amazon Redshift that enables you to run queries against exabytes of data in S3 without loading or transforming any data9. However, Redshift Spectrum requires a Redshift cluster to process the queries, which adds additional costs and operational overhead. Redshift Spectrum also charges you based on the number of bytes scanned by each query, which can be expensive if you have large volumes of log data10.

1: Logging AWS WAF web ACL traffic - Amazon Web Services 2: What Is Amazon Athena? - Amazon Athena 3: Streaming CloudWatch Logs Data to Amazon S3 - Amazon CloudWatch Logs 4: Migrate data from CloudWatch Logs using AWS Database Migration Service - AWS Database Migration Service 5: Querying AWS service logs - Amazon Athena 6: Querying data from Amazon Redshift - Amazon Athena 7: Analyzing log data with CloudWatch Logs Insights - Amazon CloudWatch Logs 8: CloudWatch Logs Insights quotas - Amazon CloudWatch 9: Querying external data using Amazon Redshift Spectrum - Amazon Redshift 10: Amazon Redshift Spectrum pricing - Amazon Redshift

The correct answer is C. Change the Resource element to ''arn:aws:s3:::DOC-EXAMPLE-BUCKET/*''.

The reason is that the Resource element in the bucket policy specifies which objects in the bucket are affected by the policy. In this case, the policy only applies to the bucket itself, not the objects inside it. Therefore, the Lambda function cannot access the objects with the s3:GetObject permission. To fix this, the Resource element should include a wildcard (*) to match all objects in the bucket. This way, the policy grants the Lambda function permission to read any object in the bucket.

The other options are incorrect for the following reasons:

A) Removing the Condition element would not help, because it only restricts access based on the source IP address of the request. The Principal element should not be changed to the Lambda function ARN, because it specifies who is allowed or denied access by the policy. The policy should allow access to any principal ('*') and rely on IAM roles or policies to control access to the Lambda function.

B) Changing the Action element to include s3:GetBucket* would not help, because it would grant additional permissions that are not needed by the Lambda function, such as s3:GetBucketAcl or s3:GetBucketPolicy. The s3:GetObject* permission is sufficient for reading objects in the bucket.

D) Changing the Resource element to the Lambda function ARN would not make sense, because it would mean that the policy applies to the Lambda function itself, not the bucket or its objects. The Principal element should not be changed to s3.amazonaws.com, because it would grant access to any AWS service that uses S3, not just Lambda.

You can establish a private connection between your VPC and Secrets Manager by creating an interface VPC endpoint. Interface endpoints are powered by AWS PrivateLink, a technology that enables you to privately access Secrets Manager APIs without an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection.

Reference: https://docs.aws.amazon.com/secretsmanager/latest/userguide/vpc-endpoint-overview.html

The correct answer is D. Configure a Secrets Manager interface VPC endpoint. Include the Lambda function's private subnet during the configuration process.

A Secrets Manager interface VPC endpoint is a private connection between the VPC and Secrets Manager that does not require an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection1. By configuring a Secrets Manager interface VPC endpoint, the security engineer can enable the custom Lambda function to communicate with Secrets Manager without sending or receiving network traffic through the internet. The security engineer must include the Lambda function's private subnet during the configuration process to allow the function to use the endpoint2.

The other options are incorrect for the following reasons:

A) An egress-only internet gateway is a VPC component that allows outbound communication over IPv6 from instances in the VPC to the internet, and prevents the internet from initiating an IPv6 connection with the instances3. However, this option does not meet the requirement that the VPC must not send or receive network traffic through the internet. Moreover, an egress-only internet gateway is for use with IPv6 traffic only, and Secrets Manager does not support IPv6 addresses2.

B) A NAT gateway is a VPC component that enables instances in a private subnet to connect to the internet or other AWS services, but prevents the internet from initiating connections with those instances4. However, this option does not meet the requirement that the VPC must not send or receive network traffic through the internet. Additionally, a NAT gateway requires an elastic IP address, which is a public IPv4 address4.

C) A VPC peering connection is a networking connection between two VPCs that enables you to route traffic between them using private IPv4 addresses or IPv6 addresses5. However, this option does not work because Secrets Manager does not have a default VPC that can be peered with. Furthermore, a VPC peering connection does not provide a private connection to Secrets Manager APIs without an internet gateway or other devices2.

The correct answer is D) Attach the CloudWatchAgentServerPolicy AWS managed policy to the EC2 instance role.

According to the AWS documentation1, the CloudWatch agent is a software agent that you can install on your EC2 instances to collect system-level metrics and logs. To use the CloudWatch agent, you need to attach an IAM role or user to the EC2 instance that grants permissions for the agent to perform actions on your behalf. The CloudWatchAgentServerPolicy is an AWS managed policy that provides the necessary permissions for the agent to write metrics and logs to CloudWatch2. By attaching this policy to the EC2 instance role, the security engineer can resolve the issue of CloudWatch not receiving the custom application-security logs.

The other options are incorrect for the following reasons:

A) Adding AWS CloudTrail to the trust policy of the EC2 instance is not relevant, because CloudTrail is a service that records API activity in your AWS account, not custom application logs3. Sending the custom logs to CloudTrail instead of CloudWatch would not meet the requirement of forwarding them to CloudWatch.

B) Adding Amazon S3 to the trust policy of the EC2 instance is not necessary, because S3 is a storage service that does not require any trust relationship with EC2 instances4. Configuring the application to write the custom logs to an S3 bucket that CloudWatch can use to ingest the logs would be an alternative solution, but it would be more complex and costly than using the CloudWatch agent directly.

C) Adding Amazon Inspector to the trust policy of the EC2 instance is not helpful, because Inspector is a service that scans EC2 instances for software vulnerabilities and unintended network exposure, not custom application logs5. Using Amazon Inspector instead of the CloudWatch agent would not meet the requirement of forwarding them to CloudWatch.

1: Collect metrics, logs, and traces with the CloudWatch agent - Amazon CloudWatch 2: CloudWatchAgentServerPolicy - AWS Managed Policy 3: What Is AWS CloudTrail? - AWS CloudTrail 4: Amazon S3 FAQs - Amazon Web Services 5: Automated Software Vulnerability Management - Amazon Inspector - AWS

The correct answer is D. Remove Amazon SES from the root SCP.

This answer is correct because the root SCP is the most restrictive policy that applies to all accounts in the organization. The root SCP explicitly denies access to Amazon SES by using the NotAction element, which means that any action that is not listed in the element is denied. Therefore, removing Amazon SES from the root SCP will allow the developers to access it, as long as there are no other SCPs or IAM policies that deny it.

The other options are incorrect because:

A) Adding a resource policy that allows each member of the group to access Amazon SES is not a solution, because resource policies are not supported by Amazon SES1. Resource policies are policies that are attached to AWS resources, such as S3 buckets or SNS topics, to control access to those resources2. Amazon SES does not have any resources that can have resource policies attached to them.

B) Adding a resource policy that allows ''Principal'': {''AWS'': ''arn:aws:iam::account-number:group/Dev''} is not a solution, because resource policies do not support IAM groups as principals3. Principals are entities that can perform actions on AWS resources, such as IAM users, roles, or AWS accounts4. IAM groups are not principals, but collections of IAM users that share the same permissions5.

C) Removing the AWS Control Tower control (guardrail) that restricts access to Amazon SES is not a solution, because AWS Control Tower does not have any guardrails that restrict access to Amazon SES6. Guardrails are high-level rules that govern the overall behavior of an organization's accounts7. AWS Control Tower provides a set of predefined guardrails that cover security, compliance, and operations domains8.

1: Amazon Simple Email Service endpoints and quotas 2: Resource-based policies and IAM policies 3: Specifying a principal in a policy 4: Policy elements: Principal 5: IAM groups 6: AWS Control Tower guardrails reference 7: AWS Control Tower concepts 8: AWS Control Tower guardrails

The correct answer is B. Use data key caching. Use the local cache that the AWS Encryption SDK provides with a caching cryptographic materials manager.

This answer is correct because data key caching can improve performance, reduce cost, and help the company stay within the service limits of AWS KMS. Data key caching stores data keys and related cryptographic material in a cache, and reuses them for encryption and decryption operations. This reduces the number of requests to AWS KMS endpoints and avoids throttling. The AWS Encryption SDK provides a local cache and a caching cryptographic materials manager (caching CMM) that interacts with the cache and enforces security thresholds that the company can set1.

The other options are incorrect because:

A) Using keyrings with the AWS Encryption SDK does not address the problem of throttling or cost optimization. Keyrings are used to generate, encrypt, and decrypt data keys, but they do not cache or reuse them. Using each keyring individually or combining them into a multi-keyring does not reduce the number of requests to AWS KMS endpoints2.

C) Using KMS key rotation does not address the problem of throttling or cost optimization. Key rotation is a security practice that creates new cryptographic material for a KMS key every year, but it does not affect the data that the KMS key protects. Key rotation does not reduce the number of requests to AWS KMS endpoints, and it might incur additional costs for storing multiple versions of key material3.

D) Using keyrings with the AWS Encryption SDK does not address the problem of throttling or cost optimization, as explained in option A. Moreover, using any of the wrapping keys in the multi-keyring to decrypt the data is not a valid option, because only one of the wrapping keys can decrypt a given data key. The wrapping key that encrypts a data key is stored in the encrypted data key structure, and only that wrapping key can decrypt it4.

1: Data key caching - AWS Encryption SDK 2: Using keyrings - AWS Encryption SDK 3: Rotating AWS KMS keys - AWS Key Management Service 4: How keyrings work - AWS Encryption SDK

The correct answer is B. Store the API key value in AWS Secrets Manager. In the template, replace all references to the value with {{resolve:secretsmanager:MySecretId:SecretString}}.

This answer is correct because AWS Secrets Manager is a service that helps you protect secrets that are needed to access your applications, services, and IT resources. You can store and manage secrets such as database credentials, API keys, and other sensitive data in Secrets Manager. You can also use Secrets Manager to rotate, manage, and retrieve your secrets throughout their lifecycle1. Secrets Manager integrates with AWS CloudFormation, which allows you to reference secrets from your templates using the {{resolve:secretsmanager:...}} syntax2. This way, you can avoid exposing your secrets in plaintext and still use them in your resources.

The other options are incorrect because:

A) Storing the API key value as a SecureString parameter in AWS Systems Manager Parameter Store is not a solution, because AWS CloudFormation does not support references to SecureString parameters. This means that you cannot use the {{resolve:ssm:...}} syntax to retrieve encrypted parameter values from Parameter Store3. You would have to use a custom resource or a Lambda function to decrypt the parameter value, which adds complexity and overhead to your template.

C) Storing the API key value in Amazon DynamoDB is not a solution, because AWS CloudFormation does not support references to DynamoDB items. This means that you cannot use the {{resolve:dynamodb:...}} syntax to retrieve item values from DynamoDB tables4. You would have to use a custom resource or a Lambda function to query the DynamoDB table, which adds complexity and overhead to your template.

D) Storing the API key value in a new Amazon S3 bucket is not a solution, because AWS CloudFormation does not support references to S3 objects. This means that you cannot use the {{resolve:s3:...}} syntax to retrieve object values from S3 buckets5. You would have to use a custom resource or a Lambda function to download the object from S3, which adds complexity and overhead to your template.

1: What is AWS Secrets Manager? 2: Referencing AWS Secrets Manager secrets from Parameter Store parameters 3: Using dynamic references to specify template values 4: Amazon DynamoDB 5: Amazon Simple Storage Service (S3)

The correct answer is C. Use AWS Resource Access Manager (AWS RAM) to share Account-A's VPC subnets with the remaining member accounts. Configure the member accounts to use the shared subnets to launch workloads.

This answer is correct because AWS RAM is a service that helps you securely share your AWS resources across AWS accounts, within your organization or organizational units (OUs), and with IAM roles and users for supported resource types1. One of the supported resource types is VPC subnets2, which means you can share the subnets in Account-A's VPC with the other member accounts using AWS RAM. This way, you can meet the requirements of using a centrally managed VPC, avoiding duplicate VPCs in each account, and launching workloads in shared subnets. You can also control the access to the shared subnets by using IAM policies and resource-based policies3, which can prevent one account from modifying another account's resources.

The other options are incorrect because:

A) Using a CloudFormation template in the member accounts to launch workloads and using the Fn::ImportValue function to obtain the subnet ID values is not a solution, because Fn::ImportValue can only import values that have been exported by another stack within the same region4. This means that you cannot use Fn::ImportValue to reference the subnet IDs that are exported by Account-A's CloudFormation template, unless all the member accounts are in the same region as Account-A. This option also does not avoid creating duplicate VPCs in each account, which is one of the requirements.

B) Using a transit gateway in the VPC within Account-A and configuring the member accounts to use the transit gateway to access the subnets in Account-A to launch workloads is not a solution, because a transit gateway does not allow you to launch workloads in another account's subnets. A transit gateway is a network transit hub that enables you to route traffic between your VPCs and on-premises networks5, but it does not enable you to share subnets across accounts.

D) Creating a peering connection between Account-A and the remaining member accounts and configuring the member accounts to use the subnets in Account-A through the VPC peering connection to launch workloads is not a solution, because a VPC peering connection does not allow you to launch workloads in another account's subnets. A VPC peering connection is a networking connection between two VPCs that enables you to route traffic between them privately6, but it does not enable you to share subnets across accounts.

1: What is AWS Resource Access Manager? 2: Shareable AWS resources 3: Managing permissions for shared resources 4: Fn::ImportValue 5: What is a transit gateway? 6: What is VPC peering?

The steps that the Security Engineer should take to check for known vulnerabilities and limit the attack surface are:

B) Review the application security groups to ensure that only the necessary ports are open. This is a good practice to reduce the exposure of the EC2 instances to potential attacks from the Internet. Application security groups are a feature of Azure that allow you to group virtual machines and define network security policies based on those groups1.

D) Use Amazon Inspector to periodically scan the backend instances. This is a service that helps you to identify vulnerabilities and exposures in your EC2 instances and applications. Amazon Inspector can perform automated security assessments based on predefined or custom rules packages2.