AWS Certified Developer - Associate

The best way to inspect the timing of the DynamoDB API calls made by the Lambda function and identify any bottlenecks is to enable AWS X-Ray tracing for the function. AWS X-Ray allows you to trace requests made by your applications as they travel through various AWS services. By enabling X-Ray, you can get detailed information about the performance of your Lambda function, including the duration of each DynamoDB API call. This information is invaluable for identifying and diagnosing performance bottlenecks.

Other options do not provide a direct method for inspecting the timing of API calls. Adding DynamoDB as an event source or using an ALB is irrelevant to timing DynamoDB API calls. Limiting Lambda concurrency does not help in inspecting API call timing and could adversely affect performance by throttling executions.

References:

• AWS X-Ray and Lambda
• AWS X-Ray Developer Guide

In the scenario described, the issue occurs when the PutRecords API call to Amazon Kinesis occasionally fails due to periodic spikes in clickstream data. Such spikes can lead to throttling of requests, causing exceptions. Choosing the right mitigation techniques is crucial for sustaining the application's performance.

Firstly, reducing the frequency and/or size of the requests can be beneficial. During spikes, if the volume of data being sent in a single request is lower or the requests are sent less frequently, it can alleviate issues stemming from exceeding throughput limits. Thus, this technique directly addresses the issue of overwhelming the Kinesis stream.

Secondly, implementing retries with exponential backoff is a common strategy in distributed systems to handle transient faults gracefully. By retrying failed requests with increasing wait times between each attempt, it reduces the likelihood of overwhelming the system further during a temporary spike. This method is particularly effective in situations where failures may be intermittent, providing the system with time to recover.

Using Amazon SNS instead of Kinesis wouldn’t solve the problem at hand, as SNS is not designed to handle high-throughput streaming data ingestion like Kinesis. Moreover, using the PutRecord API instead of PutRecords minimizes the batch efficiency and potentially increases costs due to the 1MB consumption charges on a per-record basis. Reducing the number of KCL (Kinesis Client Library) consumers is unrelated to throttling issues caused by PutRecords calls and therefore does not address the reported issue.

References:

• Exponential Backoff and Jitter
• Monitoring Amazon Kinesis Data Streams with Amazon CloudWatch

The solution that best improves global performance with the least operational overhead is to set up an Amazon CloudFront distribution using the Application Load Balancer (ALB) as the origin server. CloudFront is a content delivery network (CDN) service that securely delivers data, videos, applications, and APIs to customers globally with low latency and high transfer speeds. Using CloudFront, you can cache content at edge locations close to users, reducing latency and improving application performance for users worldwide.

CloudFront can be configured to point to the ALB as the origin server, enabling requests from users to be routed to the nearest edge location. The edge location will serve the cached content or forward requests to the origin if the content is not cached, ensuring efficient data delivery. This approach has minimal operational overhead because it leverages AWS-managed services to handle regional distribution and cache management.

Deploying the application to multiple Regions and using latency-based routing or launching more EC2 instances in the same Region would increase operational complexity, requiring management of multiple infrastructure components and increased cost without necessarily providing the global performance improvement afforded by using a CDN like CloudFront.

Using an AWS Client VPN with geolocation routing is more suited for secure, private connectivity and does not inherently enhance performance. It also requires additional configuration and user management.

Real-world examples of employing Amazon CloudFront include dynamic web applications where content delivery and caching can significantly enhance user experience by improving load times and providing a seamless browsing experience.

For more details on configuring Amazon CloudFront to work with an Application Load Balancer, consider visiting the AWS CloudFront documentation.

The developer requires a solution where each Lambda function can run independently of the others' success or failure. In such a scenario, publishing the sale event to an Amazon Simple Notification Service (SNS) topic is the most appropriate solution. This design allows different components to subscribe to the SNS topic and be triggered independently when a message is published. It supports a fan-out pattern, which means that each subscriber will receive and process the message independently, even if other subscribers fail or succeed.

Publishing to an Application Load Balancer (ALB) and adding the Lambda functions as targets do not align with the nature of ALBs, which are typically used for routing incoming requests to web servers or containers, and not for triggering Lambda functions directly based on an event. Hence, this approach does not suit the requirement for concurrent and independent processing.

Using Amazon SQS allows for decoupling messages and enables Lambda functions to poll and be triggered by new messages. However, SQS follows a queue paradigm where messages are processed individually, not broadcasted to multiple functions. Specifically, direct polling from a queue would imply a single consumer or a manually implemented mechanism to achieve parallel processing, which is not the scenario posed by SNS.

Implementing an AWS Step Functions state machine could work if coordinating tasks with control flow is necessary. However, it introduces complexity by combining all the logic into one state machine and primarily focuses on handling workflow orchestration rather than simple parallel triggering of independent services.

Examples

A real-world example of this solution would be when an event such as a new sale can generate multiple downstream effects, like updating records in different systems (inventory, accounting, CRM systems), and you want each of these systems to be notified and respond in parallel without being dependent on their individual success or failure.

You can configure SNS to automatically trigger AWS Lambda functions by subscribing the functions to an SNS topic. When the application publishes a message to the SNS topic, all subscribed Lambda functions receive the event, enabling independent processing:

import boto3

sns_client = boto3.client('sns')
response = sns_client.publish(
    TopicArn='string',
    Message='message',
    Subject='subject'
)

References

• Amazon SNS in the AWS documentation
• AWS Lambda and Amazon SNS - Serverless Architectures

To expand an application to run in multiple AWS Regions while meeting the requirement of AMIs being encrypted, the developer should create new AMIs specifying the encryption parameters. Once the AMIs are encrypted, they can be copied to the destination Region. This strategy ensures that AMIs comply with the company's encryption requirement before they are replicated.

The use of KMS is essential in enabling encryption on AMIs. However, it's important to note that you cannot directly apply AWS KMS to an existing AMI to encrypt it. Instead, a new AMI needs to be created with encryption settings specified. AWS Certificate Manager (ACM) cannot be used to encrypt AMIs as ACM is primarily meant for managing SSL/TLS certificates, not for encrypting AMIs. Copying unencrypted AMIs to the destination Region and then enabling encryption "by default" is not feasible as encryption must be set during the AMI creation or replication process.

In practice, when creating a new AMI with encryption, a developer typically launches an EC2 instance based on the existing unencrypted AMI, and then creates a new AMI from that instance while specifying the necessary encryption key. After creating encrypted AMIs, they can then be copied to the target Region.

Example:

Suppose a developer has an unencrypted AMI used in the us-east-1 Region and wants to make an encrypted version available in us-west-2. The developer would launch an EC2 instance using the original unencrypted AMI in us-east-1, apply the required updates or changes, and create a new AMI from the instance. When creating the new AMI, the developer specifies an encryption key via AWS KMS. Once the new encrypted AMI is available, it can be copied to us-west-2, thereby enabling the deployment of the entire application stack with encrypted AMIs.

References:

• Copying an AMI
• Create an Amazon EBS-backed AMI
• AWS KMS

The correct answer is to add an Export declaration to the Outputs section of the original template and use ImportValue in other templates.

This approach represents the native and most efficient way to share resources between CloudFormation stacks. CloudFormation's cross-stack reference feature was specifically designed for this purpose, making it the ideal solution for sharing information between stacks while maintaining loose coupling.

Here's how you would implement this in practice:

In the original template, modify the Outputs section:

Outputs:
  ContentBucketName:
    Value: !Ref ContentBucket
    Export:
      Name: SharedContentBucket

Then in another template, reference it using ImportValue:

Resources:
  SomeResource:
    Properties:
      BucketName: !ImportValue SharedContentBucket

The suggestion to use Fn::Include is incorrect because it's not a valid CloudFormation function and would create unnecessary tight coupling between templates. Creating a custom CloudFormation resource would be overly complex and require additional Lambda code and maintenance. Finally, the option suggesting Exported: true and ImportResource is simply incorrect as these are not valid CloudFormation constructs.

Reference:

• https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/using-cfn-stack-exports.html

To give the AWS Lambda function access to an Amazon RDS DB instance located in a private subnet within a VPC, the developer must ensure the Lambda function is configured with VPC access. Assigning the necessary IAM role to the Lambda function is one step, but, for network access to the DB instance, there are additional requirements.

The Lambda function must be configured to run in the same VPC, allowing it to access resources within that VPC, such as the RDS DB instance. This involves specifying the VPC, subnets, and security groups when configuring your Lambda function. Once configured correctly, Lambda will assign Elastic Network Interfaces (ENIs) to operate within the VPC’s subnets, allowing it to communicate with the RDS DB instance over the private IP network without requiring a public IP address.

Moreover, modifying security group rules is crucial. You will need to ensure that the security group associated with the RDS DB instance allows inbound traffic from the Lambda function. Typically, this would mean allowing incoming database traffic on the appropriate port (e.g., TCP 3306 for MySQL) from the Lambda's security group.

Assigning a public IP to the DB instance makes it accessible over the internet, reducing security without helping internal network communication. AWS Direct Connect and Amazon CloudFront are not suitable for this use case because Direct Connect is about establishing physical connections and CloudFront is a content distribution network, neither of which solve the problem of allowing secure access from Lambda to RDS inside a private VPC subnet.

References:

• Giving Lambda functions access to resources in an Amazon VPC
• Controlling access with security groups

The most secure method for temporarily accessing AWS services through the CLI is using temporary security credentials, which can be obtained using the get-session-token command. This command is part of AWS Security Token Service (STS) and provides temporary credentials that are valid for a specified period. This reduces the risk associated with long-lived credentials, as the temporary credentials expire after a set time.

Using temporary credentials is more secure than specifying access keys directly in commands or using the aws configure command, which stores the credentials in plain text on disk. By using get-session-token, you leverage short-lived credentials, enhancing security by minimizing the window in which compromised credentials can be used.

References:

• Using Temporary Credentials with AWS CLI
• AWS Security Token Service (STS) GetSessionToken

In this scenario, the developer needs to pause the AWS Step Functions workflow to wait for an external validation service to complete its processing. The most effective way to accomplish this is by using the .waitForTaskToken option in the Lambda function task state. When the Lambda function is executed, it generates a task token. This token should be included in the request body sent to the external service. Once the external service completes its data validation, it will use the task token to call back a webhook in AWS Step Functions, thereby resuming the workflow.

This approach is specifically designed for scenarios where workflows need to pause and wait for an external signal, making it ideal for tasks that take a long time to complete. Unlike synchronous or asynchronous methods that may require constant polling or fixed time waits, utilizing a task token ensures that the workflow only continues when the external service is ready, which is efficient and resource-friendly.

Using .waitForTaskToken is particularly useful in applications where external processes might take an extended period, such as data validation checks or third-party approvals, without requiring the workflow to be actively polling or consuming resources unnecessarily.

For further details, you can refer to the AWS documentation on AWS Step Functions Task Tokens.

The most appropriate method for an application running on an EC2 instance to find its public IPv4 address is to query the instance metadata. The instance metadata service provides details about the instance, including its public IP address, accessible via HTTP from the URL http://169.254.169.254/latest/meta-data/public-ipv4. This is a well-known method to retrieve various information about the instance directly from within the instance itself.

• Querying the instance user data would not provide the IP address, as user data is typically used for configuration scripts or settings supplied during instance launch.
• Querying AMI information will not yield the public IP address, as it only provides details about the Amazon Machine Image used to launch the instance.
• The hosts file does not contain dynamic information like the public IP address assigned by AWS.

References:

• Retrieving Instance Metadata

Placing the API behind an Amazon API Gateway and setting server-side throttling limits is an effective way to control the number of requests to the API without modifying the application or client code. API Gateway provides a robust mechanism for throttling requests, which can be applied globally to limit the total number of requests per second to the API. This helps protect the backend system from being overwhelmed by excessive requests, ensuring stability and consistent performance.

• Amazon API Gateway: This service allows you to create, publish, maintain, monitor, and secure APIs. It can handle all the tasks involved in accepting and processing up to hundreds of thousands of concurrent API calls, including traffic management, authorization and access control, monitoring, and API version management. By setting server-side throttling limits, the company can ensure that the API does not receive more requests than it can handle at any given time.

• Network Load Balancer and Application Load Balancer: These services are typically used for distributing incoming application or network traffic across multiple targets, such as EC2 instances. They do not inherently provide request throttling features like API Gateway does. Throttling at the load balancer level would require additional configurations and may not be as straightforward or effective as using API Gateway for this specific need.

• Per-client throttling: While also a feature of API Gateway, focusing on server-side throttling in this scenario is more appropriate because it limits total requests to the API rather than setting specific limits per client. This approach ensures that the application is protected against a sudden surge in requests overall, which is the primary concern here.

In the real world:

A real-world example of using API Gateway for throttling can be found in scenarios where applications need to ensure a consistent quality of service when accessed by a large number of clients. For instance, a billing service API that needs to prevent excessive access that could lead to system overload would benefit from server-side throttling to maintain system stability.

References:

• Amazon API Gateway Throttling
• API Gateway Features

To address the requirement of starting the analysis once each day at a specific time, the most cost-effective solution is to use Amazon EventBridge to schedule the execution of the AWS Lambda function. By configuring an EventBridge scheduled rule, you can easily set up a cron or rate expression that will trigger the Lambda function at the precise time required daily. This approach is not only cost-effective but straightforward to implement and manage within the AWS ecosystem.

Configuring an S3 event notification to invoke the Lambda function is not suitable here because it would attempt to trigger the analysis upon each file upload, rather than at a specific, consolidated time. This could lead to multiple unnecessary invocations if the sales reports are uploaded at different times by different branch offices.

Running the Lambda function continuously is unnecessary and significantly more expensive. Lambda functions are billed based on the number of requests and the duration of execution time. Running the function continuously and waiting for the predefined trigger time would incur substantial costs without practical benefit.

AWS Step Functions provide orchestration for complex workflows, but they introduce additional complexity and cost for this scenario where simply executing a function at a specific time suffices. This approach would be over-engineering the solution for the requirements provided.

Example: You can set up an EventBridge scheduled rule as shown below:

aws events put-rule \
    --schedule-expression "cron(0 12 * * ? *)" \
    --name "DailyLambdaTriggerRule"

This command sets up a cron expression to trigger the Lambda function at 12 PM UTC every day. Replace the cron pattern with your desired schedule.

Reference:

• Amazon EventBridge Scheduler

To meet the requirements of keeping the original records with PII while allowing access to the records without PII, the optimal approach is to use Amazon S3 Object Lambda. This feature allows the transformation of data as it is retrieved from S3, using Lambda functions. This means you can apply logic to remove PII dynamically when the object is accessed, ensuring that only authorized users see the PII-free version.

• S3 Object Lambda: By creating an S3 Object Lambda access point and associating it with the removePii function, the developer can ensure that any request for the data can have PII removed on-the-fly. This setup allows the original data to remain unchanged in S3 and offers a seamless way to comply with data access requirements. The function is triggered only when data is accessed, not when it is stored or altered, maintaining a single source of truth.

• S3 Event Notifications: These are typically used for triggering actions based on changes to the S3 objects, such as uploads (PUT requests) or deletions. They are not designed to modify the content of the objects being retrieved dynamically.

• S3 Access Points with GetObjectLegalHold: This option does not apply to transforming object content on retrieval. It is used for managing legal holds on objects.

A practical example of using S3 Object Lambda would involve creating an access point with transformation capabilities:

1. Set up an S3 Object Lambda access point in the AWS Management Console.
2. Associate the access point with the Lambda function removePii.
3. Ensure that access policies are correctly configured so that only the appropriate users and systems can use the Object Lambda access point.

For more detailed steps, refer to the Transforming objects with S3 Object Lambda.