DevOps Most Asked Real Time Interview Question And Answer - Set 4

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 "In the world of DevOps, success is not measured by how quickly you can deploy, but by how seamlessly you can integrate, automate, and collaborate across the entire software lifecycle."{alertInfo}
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Question 76: Difference between replica set and deployment

A replica set is a Kubernetes controller that manages identical copies of a pod to ensure high availability and fault tolerance. It ensures that a specified number of pod replicas are running at any given time, replacing any pods that fail or become unavailable.

On the other hand, a deployment is a higher-level Kubernetes resource that manages replica sets and provides additional features such as rolling updates and rollback functionality. Deployments allow you to define the desired state of your application, including the number of replicas, container images, and update strategies.

In summary, a replica set manages pod replicas directly, while a deployment manages replica sets and provides additional features for managing application updates and rollbacks.

Question 77: If pods not coming online post deployment, how to fix that, what steps we can execute to fix it

If pods are not coming online after a deployment, several steps can be taken to troubleshoot and fix the issue:

  1. Check pod status: Use the kubectl get pods command to check the status of the pods. Look for any error messages or events associated with the pods.
  2. Check pod logs: Use the kubectl logs command to view the logs of the pods. Look for any error messages or issues that may indicate why the pods are not coming online.
  3. Check resource constraints: Ensure that the pods have enough resources (CPU, memory) allocated to them. If the pods are resource-constrained, they may fail to start or be evicted by the scheduler.
  4. Check network connectivity: Verify that the pods can communicate with other services and resources they depend on. Network issues can prevent pods from starting or accessing required resources.
  5. Rollback deployment: If the issue occurred after a recent deployment, consider rolling back to the previous version using kubectl rollout undo.
  6. Debug container startup: If the pods are failing to start due to issues within the container, consider running the container locally with the same configuration to debug the issue.
  7. Review configuration changes: Check for any recent configuration changes or updates that may have caused the issue. Revert any changes that may be responsible for the problem.

By following these steps, you can diagnose and resolve issues preventing pods from coming online after deployment.

Question 78: How to export pod logs to CloudWatch or Grafana

Exporting pod logs to CloudWatch or Grafana can be achieved through various methods depending on your setup and requirements:

Exporting to CloudWatch:

  1. Install the CloudWatch Agent: Set up the CloudWatch agent on your Kubernetes cluster nodes to collect logs from pod containers.
  2. Configure Log Groups: Configure the CloudWatch agent to send logs to specific log groups in CloudWatch.
  3. Deploy Fluentd or Fluent Bit: Use Fluentd or Fluent Bit as a log collector in your Kubernetes cluster to forward logs to CloudWatch.
  4. Configure Fluentd/Fluent Bit: Configure Fluentd or Fluent Bit to collect logs from pod containers and send them to CloudWatch using the CloudWatch Logs plugin.
  5. Verify Logs: Verify that logs are successfully sent to CloudWatch by checking the log groups in the AWS Management Console.

Exporting to Grafana:

  1. Install Prometheus and Grafana: Set up Prometheus and Grafana in your Kubernetes cluster to collect and visualize metrics and logs.
  2. Deploy Loki: Deploy Loki, a log aggregation system, in your Kubernetes cluster to collect logs from pod containers.
  3. Configure Loki: Configure Loki to scrape logs from pod containers and store them in its database.
  4. Install Grafana Loki Plugin: Install the Grafana Loki plugin to visualize logs in Grafana.
  5. Create Dashboards: Create dashboards in Grafana to visualize and analyze logs from your Kubernetes pods.

By following these steps, you can export pod logs to CloudWatch or Grafana for monitoring and analysis purposes.

Question 79: How to monitor Pods in monitoring tools like Grafana or Prometheus

Monitoring pods in tools like Grafana or Prometheus involves setting up monitoring agents and configuring them to collect metrics and logs from Kubernetes pods. Here's how you can monitor pods using Grafana or Prometheus:

Monitoring with Prometheus:

  1. Install Prometheus: Set up Prometheus in your Kubernetes cluster to collect metrics from various Kubernetes components, including pods.
  2. Configure Service Discovery: Configure Prometheus to discover pods using Kubernetes service discovery mechanisms.
  3. Define Service Monitors: Create ServiceMonitor objects in Kubernetes to specify which pods and endpoints Prometheus should monitor.
  4. Define Alerting Rules: Define alerting rules in Prometheus to trigger alerts based on pod metrics such as CPU usage, memory usage, and error rates.
  5. Visualize Metrics: Use Grafana to visualize metrics collected by Prometheus by setting up dashboards and panels.

Monitoring with Grafana:

  1. Install Grafana: Set up Grafana in your Kubernetes cluster to visualize metrics and logs.
  2. Install Prometheus Data Source: Install the Prometheus data source plugin in Grafana to query metrics collected by Prometheus.
  3. Import Dashboards: Import pre-built dashboards for Kubernetes and pod monitoring from Grafana's dashboard repository.
  4. Customize Dashboards: Customize dashboards to include specific metrics and visualizations for monitoring pods.
  5. Set Up Alerts: Configure alerting rules in Grafana to receive notifications when pod metrics exceed predefined thresholds.

By following these steps, you can monitor pods in Grafana or Prometheus and gain insights into their performance and health.

Question 80: Explain end to end deployment cycle

The end-to-end deployment cycle encompasses all stages involved in deploying an application or software update from development to production. It typically includes the following phases:

  1. Planning: Define deployment goals, requirements, and timelines. Determine the scope of the deployment and assess potential risks.
  2. Development: Develop and test the application or software update in a development environment. Implement new features or bug fixes as needed.
  3. Version Control: Use version control systems such as Git to manage code changes and track revisions throughout the deployment cycle.
  4. Continuous Integration (CI): Automate the process of integrating code changes into a shared repository and running automated tests to ensure code quality and stability.
  5. Continuous Deployment (CD): Automate the deployment process to deliver code changes to production environments rapidly and reliably. This may involve techniques such as blue-green deployments or canary releases.
  6. Testing: Conduct thorough testing of the application or software update in staging or pre-production environments to identify and address any issues before deployment to production.
  7. Deployment: Deploy the application or software update to production environments using automated deployment tools or scripts. Monitor the deployment process and verify its success.
  8. Monitoring and Maintenance: Continuously monitor the deployed application or software for performance, availability, and security issues. Perform regular maintenance tasks such as applying patches and updates.
  9. Rollback and Recovery: Establish rollback procedures to revert to a previous version of the application in case of deployment failures or issues. Implement disaster recovery plans to recover from unexpected outages or disasters.

By following this end-to-end deployment cycle, organizations can streamline the deployment process, reduce risks, and deliver high-quality software to end-users efficiently.

Question 81: What is CI and CD?

CI (Continuous Integration) and CD (Continuous Deployment/Delivery) are practices in software development aimed at automating and improving the process of delivering code changes to production environments.

Continuous Integration (CI):

CI is the practice of frequently integrating code changes into a shared repository, typically several times a day. Each integration triggers an automated build and automated tests to validate the

Question 82: In Jenkins if we want to deploy particular jobs from slave server then where we can make that changes

In Jenkins, if you want to deploy particular jobs from a slave server, you can make those changes in the job configuration itself. When configuring the job, you can specify the label expression to restrict the execution to a specific slave node. This ensures that the job runs only on the designated slave server.

Question 83: If you got the requirement for migrating data center servers to AWS, how would you plan for it?

Planning for migrating data center servers to AWS involves several steps:

  1. Assess current infrastructure and dependencies.
  2. Select appropriate AWS services for migration.
  3. Estimate resource requirements and costs.
  4. Develop a migration strategy and timeline.
  5. Set up AWS infrastructure and networking.
  6. Migrate data and applications using tools like AWS Server Migration Service (SMS) or AWS Database Migration Service (DMS).
  7. Test migrated resources thoroughly.
  8. Implement monitoring and optimization strategies post-migration.

Question 84: How to migrate an on-premises data center database to AWS RDS?

To migrate an on-premises data center database to AWS RDS (Relational Database Service):

  1. Evaluate compatibility and plan the migration strategy.
  2. Take backups of the on-premises database.
  3. Create an RDS instance matching the database engine and configuration.
  4. Use AWS Database Migration Service (DMS) or other compatible tools to migrate the data.
  5. Test the migrated data thoroughly to ensure integrity and functionality.
  6. Update application configurations to point to the new RDS instance.
  7. Monitor performance and optimize configurations post-migration.

Question 85: What action will you take if post-migration, you are encountering data mismatch issues?

If encountering data mismatch issues post-migration, the following actions can be taken:

  1. Identify the source of the data mismatch, whether it's in the migration process or application logic.
  2. Validate data migration logs and compare data between source and target environments.
  3. Rollback to the previous state if necessary backups are available.
  4. Rectify data discrepancies manually or through migration tools.
  5. Perform thorough testing to ensure data consistency and integrity.
  6. Implement monitoring to catch any discrepancies early on.

Question 86: Explain types of migration.

Types of migration include:

  1. Lift and shift: Moving applications or systems to the cloud without significant modifications.
  2. Re-platforming: Slightly modifying applications for better compatibility with the target cloud platform.
  3. Re-architecting: Redesigning applications to take full advantage of cloud-native features and services.
  4. Repurchasing: Replacing on-premises software with cloud-based alternatives.
  5. Retiring: Decommissioning legacy systems that are no longer needed.
  6. Refactoring: Restructuring applications for improved performance, scalability, or cost-efficiency in the cloud.

Question 87: How to migrate very large data like petabyte from on-premises to AWS?

Migrating very large data like petabytes from on-premises to AWS involves several steps:

  1. Assess the data and plan the migration strategy considering bandwidth, time, and costs.
  2. Use AWS Snowball or AWS Snowmobile for offline data transfer in case of huge volumes.
  3. Utilize AWS DataSync for online data transfer with minimal downtime.
  4. Optimize data transfer by compressing, deduplicating, or using incremental updates where possible.
  5. Monitor the migration process closely and verify data integrity post-transfer.
  6. Implement strategies for ongoing synchronization or backups to ensure data consistency.

Question 88: How to avoid major downtime during migration from on-premises to AWS?

To avoid major downtime during migration from on-premises to AWS:

  1. Plan meticulously and perform thorough testing beforehand.
  2. Implement a phased migration approach, prioritizing critical systems first.
  3. Utilize AWS services like AWS Database Migration Service (DMS) or AWS Server Migration Service (SMS) to minimize downtime.
  4. Set up parallel environments for testing and validation.
  5. Implement traffic shifting techniques such as blue-green deployments or canary releases.
  6. Configure load balancers and DNS to seamlessly switch traffic between on-premises and AWS environments.
  7. Monitor closely during the migration process and be prepared to rollback if necessary.

Question 89: How to handle application traffic worldwide to improve application performance?

To handle application traffic worldwide and improve performance:

  1. Utilize content delivery networks (CDNs) to cache and serve static assets closer to users.
  2. Implement a multi-region architecture with AWS Global Accelerator or Route 53 Latency Based Routing for directing users to the nearest server.
  3. Optimize application code and database queries for efficiency.
  4. Utilize AWS services like Amazon CloudFront for dynamic content caching and AWS Lambda@Edge for serverless computing at the edge.
  5. Implement distributed caching solutions like Amazon ElastiCache for reducing database load.
  6. Continuously monitor and optimize application performance using AWS CloudWatch and other monitoring tools.

Question 90: How to secure an S3 bucket?

To secure an S3 bucket:

  1. Enable versioning to protect against accidental deletion or overwrite.
  2. Set up access controls using IAM policies to restrict access to authorized users and applications.
  3. Use bucket policies to define access permissions at the bucket level.
  4. Implement Access Control Lists (ACLs) to further refine access controls on individual objects.
  5. Enable encryption at rest using server-side encryption (SSE) with AWS-managed keys (SSE-S3), customer-provided keys (SSE-C), or AWS Key Management Service (SSE-KMS).
  6. Enable logging to track access and changes to objects within the bucket.
  7. Regularly audit permissions and configurations to ensure compliance with security best practices.
  8. Consider using AWS S3 Block Public Access to prevent inadvertent public exposure of data.

Question 91: How to reduce cost for AWS infrastructure?

To reduce costs for AWS infrastructure, you can consider several strategies:

  1. Right-sizing resources: Choose instance types and sizes that match your workload requirements to avoid over-provisioning.
  2. Utilize auto-scaling: Automatically adjust resources based on demand to avoid paying for unused capacity.
  3. Use Reserved Instances (RIs) and Savings Plans: Commit to usage for a period to get discounted rates on EC2 instances and other services.
  4. Optimize storage: Evaluate storage usage and use lifecycle policies to move data to cheaper storage classes when appropriate.
  5. Monitor and analyze usage: Use AWS Cost Explorer and other monitoring tools to identify areas of overspending and adjust accordingly.
  6. Leverage spot instances: Utilize spare EC2 capacity at significantly reduced rates for fault-tolerant and flexible workloads.
  7. Implement tagging: Tag resources with relevant metadata to track costs and allocate expenses accurately.

Question 92: What is the difference between reserve instance and saving plan? Which one is better?

Reserved Instances (RIs) require you to commit to a specific instance type in a region for a term of one or three years, providing a significant discount compared to On-Demand pricing. Savings Plans offer flexibility by providing savings on usage across any EC2 instance type or other eligible AWS services, without the need to commit to a specific instance type or term. Savings Plans typically offer similar discounts to RIs but are more flexible. The choice between them depends on your workload's predictability and flexibility requirements.

Question 93: How to save cost for Lambda functions, what is power tuning?

To save costs on Lambda functions:

  1. Optimize memory allocation: Match memory allocation to the actual requirements of your functions to avoid over-provisioning.
  2. Opt for shorter execution times: Efficiently design and optimize code to minimize execution time.
  3. Use provisioned concurrency: Pre-warm Lambda functions to reduce cold start times and improve performance.
  4. Implement power tuning: Adjust the memory settings of Lambda functions to find the optimal balance between performance and cost. Power tuning involves analyzing historical usage patterns and adjusting the memory allocated to Lambda functions accordingly.

Question 94: How to save cost for lower environments like dev and test infrastructure?

To save costs for lower environments like development and testing:

  1. Use on-demand instances sparingly: Instead, utilize spot instances or auto-scaling groups for non-critical workloads.
  2. Implement resource scheduling: Shut down non-essential instances during off-hours or periods of low activity.
  3. Utilize AWS Free Tier: Leverage free-tier eligible services and resources for development and testing purposes.
  4. Implement cost-awareness: Educate teams about cost implications and encourage resource optimization practices.
  5. Use managed services: Utilize managed services like AWS Fargate or AWS Lambda, which can offer cost savings compared to managing infrastructure directly.

Question 95: What are the AWS 6 pillars and why should every architecture follow them?

The AWS Well-Architected Framework outlines six core pillars for building secure, high-performing, resilient, and efficient infrastructures in the cloud:

  1. Operational Excellence: Focuses on operational processes, automation, and continuous improvement.
  2. Security: Emphasizes protecting data, systems, and assets through risk management and compliance measures.
  3. Reliability: Ensures systems can recover from failures and continue to function as expected.
  4. Performance Efficiency: Optimizes resource usage to meet system requirements and maintain performance.
  5. Cost Optimization: Maximizes the value of cloud investments by minimizing costs while maintaining required performance and reliability.
  6. Sustainability: Addresses the environmental impact of architectures and promotes sustainable practices.

Following these pillars ensures that architectures are well-designed, efficient, and aligned with best practices, leading to better outcomes in terms of performance, cost, and security.

Question 96: What is Terraform and why should we use it?

Terraform is an open-source infrastructure as code (IaC) tool developed by HashiCorp. It allows users to define and provision infrastructure resources using declarative configuration files, which can be version-controlled and shared. Terraform supports multiple cloud providers, including AWS, Azure, and Google Cloud Platform, as well as on-premises infrastructure.

Key reasons to use Terraform include:

  1. Infrastructure as Code: Terraform enables the definition of infrastructure as code, allowing for automated provisioning and management of resources.
  2. Declarative Configuration: Infrastructure is defined using human-readable configuration files, making it easy to understand and maintain.
  3. Multi-Cloud Support: Terraform supports multiple cloud providers, allowing for consistent infrastructure management across different environments.
  4. Dependency Management: Terraform automatically manages dependencies between resources, ensuring proper provisioning order and avoiding configuration drift.
  5. Collaboration and Version Control: Terraform configurations can be version-controlled using tools like Git, enabling collaboration and tracking changes over time.
  6. Modular Design: Terraform configurations can be modularized, allowing for reusability and easier management of complex infrastructures.

Question 97: Are there any limitations in Terraform?

While Terraform is a powerful tool for infrastructure automation, it does have some limitations:

  1. State Management: Terraform uses a state file to track the state of deployed resources, which can become cumbersome to manage in large-scale deployments.
  2. Provider Support: Not all cloud providers and services are supported equally in Terraform, and new features may not be immediately available.
  3. Learning Curve: Terraform has a steep learning curve for beginners, especially those new to infrastructure as code concepts.
  4. Community Modules: While Terraform benefits from a large ecosystem of community-contributed modules, the quality and maintenance of these modules can vary.
  5. Lack of Fine-Grained Control: Some cloud provider features may not be fully exposed or controllable through Terraform, requiring additional manual configuration or scripting.

Question 98: What is the difference between Ansible & Terraform?

Ansible is a configuration management tool primarily used for automating software provisioning, configuration, and deployment tasks on existing servers. It uses YAML-based playbooks to define tasks and configurations.

Terraform, on the other hand, is an infrastructure as code tool focused on provisioning and managing infrastructure resources across various cloud providers. It uses its own declarative configuration language called HashiCorp Configuration Language (HCL) to describe infrastructure resources.

Question 99: Let’s say I have created resources like EC2, S3 bucket, and Lambda function using Terraform in AWS. But when I am going to create other services like RDS or CloudFront, one of my lambda functions got deleted. What could be the reason and how to fix that?

Possible reasons for the deletion of the Lambda function when creating other services like RDS or CloudFront in Terraform could include implicit dependencies or overlapping resource definitions in the Terraform configuration. To fix this, review the Terraform configuration, ensure separate resource creation, use Terraform state for insight, test changes in a staging environment, and implement resource lifecycle management.

100. How to secure resources while creating it by using Terraform.

To secure resources while creating them using Terraform, follow these best practices:

  • Utilize IAM roles and policies to control access to AWS resources.
  • Implement security groups and network ACLs to restrict network traffic.
  • Enable encryption for sensitive data using AWS Key Management Service (KMS).
  • Apply least privilege principles by granting only necessary permissions to resources.
  • Use parameterization to avoid hardcoding sensitive information in Terraform configuration files.
  • Regularly review and audit Terraform configurations for security vulnerabilities and compliance with security policies.
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