AWS Advanced Interview Questions

Question 1:

Imagine a scenario where your application running on EC2 instances in a public subnet needs to access a private S3 bucket. For security best practices, you cannot expose the S3 bucket publicly, nor do you want to put your EC2 instances in a private subnet to avoid managing NAT gateways for internet access required by other services. Describe a secure and scalable method to grant your EC2 instances access to this private S3 bucket without exposing it publicly. Explain the technical details of your proposed solution.

1. The expected answer detailed technical explanation:

The most secure and scalable method is to use VPC Endpoints for S3.

• VPC Endpoints (Gateway Type for S3): VPC endpoints enable you to privately connect your VPC to supported AWS services without requiring an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection. Gateway endpoints for S3 are highly available and scalable virtual devices that you manage within your VPC.
• How it works: When you create a gateway endpoint for S3 in your VPC, AWS adds a route to your VPC route tables that directs traffic destined for S3 to the endpoint. This ensures that traffic between your EC2 instances and the S3 bucket stays within the AWS network and doesn’t traverse the public internet.
• IAM Policies: You still need to ensure that your EC2 instances have the necessary permissions to access the S3 bucket. This is managed through IAM roles attached to the EC2 instances. The IAM role should have policies that explicitly allow actions on the specific S3 bucket and objects. Additionally, you can use S3 bucket policies to further restrict access based on the VPC endpoint or the VPC itself, adding another layer of security.
• Security Benefits: This approach significantly enhances security by:
  • Eliminating the need for internet-facing instances to access S3.
  • Keeping all traffic within the AWS network, reducing the attack surface.
  • Providing granular control over access through IAM policies and bucket policies.
• Scalability and Availability: VPC endpoints are managed by AWS and are highly available and scalable, requiring no additional infrastructure management on your part.

2. The skill/concept being tested: VPC networking, S3 security, IAM, VPC Endpoints.

Question 2:

Your company is migrating a monolithic application to a microservices architecture on AWS. You plan to use Amazon EKS to orchestrate your containerized services. One of your core requirements is to ensure secure communication between these microservices. Describe the different methods you can implement to achieve mutual TLS (mTLS) between services running in your EKS cluster. Discuss the pros and cons of each approach.

1. The expected answer detailed technical explanation:

Implementing mutual TLS (mTLS) in an EKS cluster can be achieved through several methods, each with its own trade-offs:

• Service Mesh (e.g., Istio, Linkerd):
  • Explanation: A service mesh provides a dedicated infrastructure layer for handling service-to-service communication. It often includes features like traffic management, observability, and security, including mTLS. When you enable mTLS in a service mesh, it automatically provisions and manages certificates for your services. The mesh proxies (sidecars) intercept all traffic, perform TLS handshake with certificate validation, and forward the encrypted traffic to the application container.
  • Pros:
    • Centralized Management: Certificate management, rotation, and policy enforcement are handled by the control plane of the service mesh.
    • Transparency: Application code doesn’t need to be modified to implement mTLS. The service mesh handles it at the infrastructure level.
    • Rich Features: Service meshes often provide other valuable features like traffic shaping, retries, circuit breaking, and detailed observability.
  • Cons:
    • Complexity: Introducing a service mesh adds significant complexity to your EKS cluster.
    • Performance Overhead: The sidecar proxies can introduce some latency and resource overhead.
    • Learning Curve: Operating and troubleshooting a service mesh requires specialized knowledge.
• Certificate Manager with Webhooks (e.g., cert-manager with a custom webhook or integration):
  • Explanation: You can use a Kubernetes certificate management controller like cert-manager to automate the provisioning and management of TLS certificates from various sources (e.g., Let’s Encrypt, private CA). To enforce mTLS, you could develop a custom webhook that intercepts service creation or update requests. This webhook would ensure that each service has a valid certificate and potentially configure the application or a sidecar container to use these certificates for mTLS.
  • Pros:
    • Flexibility: You have more control over the certificate issuance and management process.
    • Lightweight compared to a full service mesh: If mTLS is your primary concern, this can be a less resource-intensive approach.
  • Cons:
    • Increased Development Effort: Implementing and maintaining the custom webhook and the mTLS configuration within your applications or sidecars requires significant development effort.
    • Distributed Configuration: Managing mTLS configuration across different services can become complex.
• Application-Level mTLS:
  • Explanation: Each microservice is responsible for managing its own certificates and implementing the TLS handshake with certificate validation in its application code.
  • Pros:
    • No external dependencies: Doesn’t require a service mesh or custom controllers.
    • Fine-grained control: Applications have complete control over the TLS configuration.
  • Cons:
    • Code Duplication and Complexity: Implementing mTLS in each service leads to code duplication and increases the complexity of individual services.
    • Inconsistent Implementation: Ensuring consistent and secure mTLS implementation across all services can be challenging.
    • Difficult Certificate Management: Managing and rotating certificates for a large number of services can become cumbersome.

2. The skill/concept being tested: EKS networking, Kubernetes security, Mutual TLS (mTLS), Service Mesh, Certificate Management.

Question 3:

Your organization uses AWS CloudFormation to manage its infrastructure as code. You need to implement a rollback mechanism for stack updates to ensure that if an update fails, the stack automatically reverts to its last known good state. Describe how you would configure CloudFormation to achieve this, detailing the key parameters and considerations.

1. The expected answer detailed technical explanation:

CloudFormation provides built-in rollback capabilities that can be configured during stack creation or update. To ensure automatic rollback on update failure, you primarily rely on the OnFailure parameter and, optionally, the RollbackConfiguration.

• OnFailure Parameter: This parameter specifies the action CloudFormation takes if stack creation fails. For stack updates, a failure during the update process will trigger the behavior defined for the stack’s original OnFailure setting. Common values for OnFailure include:
  • ROLLBACK (Default): If the stack fails to create or update, CloudFormation rolls back the stack to the last known working state. For updates, this means reverting to the previous template and resource configurations.
  • DELETE: If the stack fails to create, CloudFormation deletes all the resources that were successfully created. This option is not directly relevant for rollback on update failure.
  • DO_NOTHING: If the stack fails, CloudFormation leaves the stack in its current failed state, allowing for manual inspection and troubleshooting. This is generally not the desired option for automatic rollback.
• RollbackConfiguration (for more granular control during updates): This parameter allows you to specify triggers that CloudFormation monitors during the stack update. If any of these triggers are breached (e.g., an alarm in CloudWatch goes into an ALARM state), CloudFormation initiates a rollback.
  • RollbackTriggers: A list of CloudWatch alarms that, if they enter the ALARM state during the stack update, will cause CloudFormation to roll back the stack. Each trigger includes the Amazon Resource Name (ARN) of the CloudWatch alarm and the alarm type (currently only “CloudWatch”).
  • MonitoringTimeInMinutes: The amount of time (in minutes) for CloudFormation to monitor all the specified rollback triggers after the stack update starts. If any trigger enters an ALARM state within this timeframe, a rollback is initiated. If all triggers remain in an OK state or sufficient data is not available within this timeframe, the update proceeds.
• Implementation Steps:
  1. During Stack Creation: Ensure the OnFailure parameter is set to ROLLBACK (this is often the default).
  2. During Stack Update (Basic Rollback): If you want a simple automatic rollback on any update failure, ensure the stack was initially created with OnFailure: ROLLBACK. Any error during the update process will then trigger a rollback to the previous state.
  3. During Stack Update (Advanced Rollback with Triggers):
     • Define relevant CloudWatch alarms that monitor the health and performance of your application or infrastructure components that are being updated.
     • In your CloudFormation update command or template, include the RollbackConfiguration section with the ARNs of these CloudWatch alarms in the RollbackTriggers list and set an appropriate MonitoringTimeInMinutes.
• Considerations:
  • Time to Rollback: The rollback process can take time, depending on the number and complexity of the resources being reverted.
  • Data Consistency: Be mindful of potential data inconsistencies during a rollback, especially for stateful resources like databases. Design your application and database schemas to handle rollbacks gracefully.
  • Testing Rollbacks: Regularly test your rollback mechanisms in non-production environments to ensure they function as expected and that your application can recover.
  • Monitoring: Monitor CloudFormation events and CloudWatch alarms during stack updates to understand if a rollback is initiated and why.
  • Resource Replacement vs. Update: Some resource updates might involve replacement (creating a new resource and deleting the old one). A rollback in such cases will revert to the old resource, but any data in the new resource before the rollback will be lost unless specifically handled.
  • Dependencies: Ensure that dependencies between resources are correctly defined in your CloudFormation template to facilitate a smooth rollback.

2. The skill/concept being tested: AWS CloudFormation, Infrastructure as Code (IaC), Stack Updates, Rollback Mechanisms, CloudWatch Alarms.

Question 4:

Your company has a multi-tier web application running on EC2 instances behind an Application Load Balancer (ALB). You’ve noticed intermittent HTTP 503 Service Unavailable errors being returned to users. How would you approach troubleshooting this issue, and what are some of the key AWS services and metrics you would investigate?

1. The expected answer detailed technical explanation:

Troubleshooting intermittent HTTP 503 errors with an ALB involves systematically investigating potential issues across the load balancer, the target EC2 instances, and the application itself. Here’s a breakdown of the approach and key AWS services/metrics:

Phase 1: Initial Investigation and Monitoring

1. ALB Metrics in CloudWatch:
   • HTTPCode_ELB_5XX_Count: Check if the ALB itself is generating 5xx errors. A non-zero value here indicates a problem with the ALB’s ability to connect to or receive healthy responses from the backend instances.
   • HealthyHostCount and UnHealthyHostCount: Monitor the health status of your target EC2 instances within the target group. A fluctuating or consistently low HealthyHostCount could explain the 503 errors if the ALB has insufficient healthy targets.
   • RequestCount: Observe the overall request volume to see if the errors correlate with traffic spikes that might be overwhelming the backend.
   • TargetResponseTime: High latency in responses from the backend instances can sometimes lead to the ALB timing out and returning a 503.
   • BackendConnectionErrors: This metric indicates issues with the ALB establishing connections to the backend instances.
2. ALB Access Logs: Enable ALB access logs to get detailed information about each request, including the time, client IP, request path, response code, and the target instance that handled the request. This can help identify patterns or specific requests that are failing.
3. Health Checks Configuration: Review the health check configuration for your target group. Ensure it’s correctly configured to accurately reflect the health of your application instances. Issues with the health check path, timeouts, or response code expectations can lead to instances being marked unhealthy prematurely or incorrectly.

Phase 2: Backend Instance Analysis

If the ALB metrics indicate healthy hosts and no ALB-generated 5xx errors, the problem likely lies with the backend EC2 instances or the application running on them.

1. EC2 Instance Metrics in CloudWatch:
   • CPUUtilization: High CPU usage can lead to the application becoming unresponsive.
   • MemoryUtilization: Insufficient memory can cause application crashes or slowdowns.
   • NetworkIn and NetworkOut: Check for network bottlenecks.
   • DiskReadOps and DiskWriteOps: High disk I/O can impact application performance.
   • StatusCheckFailed (Instance and System): These metrics indicate underlying problems with the EC2 instance itself.
2. Application Logs: Examine the application logs on the EC2 instances for any errors, exceptions, or performance issues that might be causing the application to fail to respond to requests.
3. Web Server Logs (e.g., Apache, Nginx): Check the web server logs for 5xx errors or slow request processing times.
4. Operating System Logs: Review system logs for any resource exhaustion or other system-level errors.
5. Application Performance Monitoring (APM) Tools (e.g., AWS X-Ray, Datadog, New Relic): If you have APM tools integrated, they can provide detailed insights into application performance, identify slow database queries, external service dependencies causing issues, and pinpoint the exact code sections contributing to errors or latency.
6. Load Testing (if applicable): If the 503 errors seem to occur during periods of high traffic, consider running load tests in a staging environment to reproduce the issue and identify bottlenecks.

Phase 3: Deeper Investigation and Potential Solutions

Based on the findings from the monitoring and log analysis, you can proceed with more targeted troubleshooting:

• Scaling Issues: If CPU or memory utilization is consistently high during error occurrences, consider scaling up your instance types or scaling out by increasing the number of instances in your target group. Configure auto-scaling policies based on relevant metrics to handle traffic fluctuations.
• Application Errors: Debug the application code based on the logs and APM data to identify and fix the root cause of the errors.
• Database Issues: If the application relies on a database, investigate database performance, connection issues, or slow queries.
• External Dependencies: If the application interacts with external services, check the health and performance of those services. Implement proper error handling and timeouts for external service calls.
• Health Check Issues: If instances are being marked unhealthy too aggressively, adjust the health check parameters (e.g., increase timeouts, adjust success codes).
• ALB Configuration: Review the ALB idle timeout settings. If backend instances take longer to respond than the idle timeout, the ALB might close the connection prematurely.

Tools and Services to Leverage:

• Amazon CloudWatch: For monitoring metrics and setting up alarms.
• AWS Management Console: For a visual overview of ALB and EC2 health.
• AWS CLI: For programmatic access to metrics and logs.
• Amazon VPC Flow Logs: To capture information about the IP traffic going to and from network interfaces in your VPC.
• AWS X-Ray: For tracing requests through your distributed application.
• Application Logs (CloudWatch Logs): Centralized logging for your application.

By following this systematic approach and leveraging the appropriate AWS services and metrics, you can effectively troubleshoot intermittent HTTP 503 errors originating from your Application Load Balancer.

2. The skill/concept being tested: Load Balancing (ALB), Application Monitoring, Troubleshooting, CloudWatch Metrics, EC2 Instance Health, Application Performance Analysis.