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Given the source code for an AWS Lambda function in the local file store.py containing a handler function called getstore and the following AWS CloudFormation template:
Transform: AWS::Serverless-2016-10-31
Resources:
StoreFunc:
Type: AWS::Serverless::Function
Properties:
Handler: store.getstore
Runtime: python3.13
What should be done to prepare the template so that it can be deployed using the AWS CLI command aws cloudformation deploy?
For CloudFormation or AWS SAM templates that reference local artifacts, the source code must be uploaded to Amazon S3 before CloudFormation can deploy it. The aws cloudformation package command scans the template for local artifacts, uploads those artifacts to an S3 bucket, and outputs a modified template that references the S3 object locations. That packaged template can then be deployed with aws cloudformation deploy. There is no standard aws cloudformation compile, aws lambda zip, or aws serverless create-package command for this workflow. The Transform: AWS::Serverless-2016-10-31 declaration means SAM resources are being transformed through CloudFormation, but local code artifacts still need packaging. AWS documentation describes aws cloudformation package as the command for uploading local artifacts referenced by a CloudFormation template to S3. (AWS Documentation)
A company runs a serverless application that uses several AWS Lambda functions. The existing Lambda functions run in a VPC. The Lambda functions query public APIs successfully.
To add a new feature to the application, a developer creates a new Lambda function to query external public APIs. The new Lambda function must store aggregated results in an Amazon RDS database that is in a private subnet of the VPC. The developer configures VPC access for the new Lambda function and sets up a working connection to the RDS database. The requests that the new Lambda function makes to the external APIs fail. However, requests from the developer's local workstation to the same APIs are successful.
Which solution will meet this requirement?
When a Lambda function is configured to run inside a VPC, it receives ENIs in the selected subnets and uses those subnets' routing to reach other networks. If the function is placed in private subnets (common when it needs to access an RDS database in private subnets), it typically does not have a direct route to the internet. As a result, outbound calls to public external APIs will fail unless the VPC provides a controlled egress path.
The standard AWS networking pattern for private-subnet internet access is a NAT gateway (or NAT instance) deployed in a public subnet, with a route from the private subnet(s) to the NAT gateway (0.0.0.0/0 NAT). The NAT gateway then uses an internet gateway attached to the VPC to reach the public internet while keeping the Lambda function (and the RDS database) in private address space. This resolves the connectivity issue while maintaining the security posture of private subnets.
Option A is not the solution: Lambda automatically provisions ENIs for VPC-enabled functions; manually provisioning an ENI does not provide internet access. Option C (security group outbound rules) is necessary but not sufficient---security groups control allowed traffic, but without a proper route to the internet (via NAT), the packets still cannot reach external endpoints. Option D (gateway VPC endpoint) is for AWS services like S3 and DynamoDB (gateway endpoints), not for arbitrary public internet APIs, and ''in a public subnet'' is not how gateway endpoints are used; they are associated with route tables.
Therefore, the correct solution is B: add a NAT gateway in a public subnet and update private subnet route tables so the new Lambda function can reach external public APIs while still accessing the private RDS database.
A developer is troubleshooting a three-tier application, which is deployed on Amazon EC2 instances. There is a connectivity problem between the application servers and the database servers.
Which AWS services or tools should be used to identify the faulty component? (Select TWO.)
A company's application has an AWS Lambda function that processes messages from loT devices. The company wants to monitor the Lambda function to ensure that the Lambda function is meeting its required service level agreement (SLA).
A developer must implement a solution to determine the application's throughput in near real time. The throughput must be based on the number of messages that the Lambda function receives and processes in a given time period. The Lambda function performs initialization and post-processing steps that must not factor into the throughput measurement.
What should the developer do to meet these requirements?
A developer is automating a new application deployment with AWS SAM. The new application has one AWS Lambda function and one Amazon S3 bucket. The Lambda function must access the S3 bucket to only read objects.
How should the developer configure AWS SAM to grant the necessary read permission to the S3 bucket?
Step-by-Step Breakdown:
Requirement Summary:
Use AWS SAM to deploy:
1 Lambda Function
1 S3 Bucket
Lambda needs read-only access to the S3 bucket
Solution must be expressed via AWS SAM template
Option A: Reference a second Lambda authorizer function
Incorrect: Lambda authorizers are used in API Gateway for authentication, not for granting S3 permissions.
Option B: Add a custom S3 bucket policy to the Lambda function
Incorrect: Bucket policies control who can access the bucket, not what the Lambda function can do.
The permission must be granted to the Lambda's IAM execution role.
Option C: Create an Amazon SQS topic for only S3 object reads
Incorrect: SQS cannot read objects from S3 and is not relevant to this scenario.
Option D: Add the S3ReadPolicy template to the Lambda function's execution role
Correct: AWS SAM provides managed policy templates like AmazonS3ReadOnlyAccess and shortcuts like S3ReadPolicy.
You can apply these to the Lambda's execution role using the Policies: section in your SAM template.
Example SAM YAML:
yaml
CopyEdit
Resources:
MyFunction:
Type: AWS::Serverless::Function
Properties:
CodeUri: my-code/
Handler: app.handler
Runtime: python3.11
Policies:
- S3ReadPolicy:
BucketName: !Ref MyBucket
MyBucket:
Type: AWS::S3::Bucket
Properties:
BucketName: my-personal-bucket-name
SAM Policy Templates: https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-policy-templates.html
Example using S3ReadPolicy: https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-policy-templates.html#s3-readpolicy
