Ashutosh Sharma

Basically, I'm a DevOps engineer with more than 3% experience in the institution with telemedicine DevOps by Docker, Fibonets, Terraform, and. So I work for a management client. Here I made virtual machine to get a backup through a sort of office. They are tested application, uh, launches the security pipeline, build back on this platform, and also the data backup. These sort of task and perform.

We can do manual roll rollback. As if that employment fails, We can do manual rollback. This can be done by selecting the previous successful build pipeline in Visitor's office and change the department manually, Or you can also use the automated role by feature. There, we can do the automated role by by scripting with our in our pipeline. We use use that feature. Or we can also use a dev the boss environment. Uh, you'll just submit. In that environment, we can help by using multiple stages at like dev test and allow the defined strategies when you do the automated development. That's vegan for using the rule of strategies.

K. So for this question, I will thinking that uh, we I can use Terraform modules as Terraform help us to organize configuration file and use them again and again for various environment also. And we can also use tool file version control. You know, store all the files in Terraform deployment in virtual system like GitHub. In git, if we can maintain clear folder structure for the user of our self. And the strategy that we we can direct to it better less file content that departs. We can use template files for showing that data, or we can use separate environments for functional file to to do the testing of the deployment. That's what we can do. Or we can also use Terraform file provisioner for managing the state files. And and so you use the most storage files for storing the data, like, as your block storage on AWS, AWS s 3 for storage data. These are things we can do to basically manage the state files and telephone or to consider permanence. That's it.

You for this question, So for this portion, we the votes we can do is begin For this. Okay. So what's this thing I can do? I can use, I can review model changes before updating. I can remove the change in log or use the notes or the telephone module, understand what the changes are being introduced and assess their impact on our infrastructure. And And the second, what we can use is we can test in the nonproduction environment. Then we can perform testing of the updated module in a nonproduction environment and validate that new model version behave as expected and does not shift with any unexpected issues. And the third set is we can do backup confirmation. We can back up our current airfoam state and commercial file to ensure that we can revert back to the v s state if of the upgrade is not up to the mark. And for the deployment phase, we can do all the strategies and implement the phased rollout strategy for maintaining the modules. And we can also have 1 other certificate and automated rule of that plan. We can prepare an automated rollback plan in case of issues or downtime during the update. This could involve having scripts or telephone ready to quickly divert to the previous model version. There's a sort of studies we can also use. And for the execution phase, we can execute upgrade, deploy the particular module during the during this window, all low traffic period to minimize user impact and monitor that deployment closely for any errors or performance issues. After this validation, testing must be done. Validate all sources managed by the module are functioning correctly was upgraded. Perform functional test to ensure that the admission all structured there as expected. And this we can perform and monitoring and roll back if necessary. We can monitoring the admission performance. My admission performance and user feedback immediately after the update. If issues or downtime occur as new the rollback then from people to restore the service to the previous state. And after the post of this phase, we can postmortem analysis. Conduct the postmortem analysis to review the update process and any issues encountered. Identify is our improvement so we can improve that areas. Then we can do documentation and reporting. Update the document details of the upgrade module version and any changes made to the process and provide summary put to the stakeholders on the outcome of the upgrade including any downtime or impact experienced. By following this structured approach, we can mitigate risk as such as the upgrading Terraform modules that could potentially post downtime. That is the best thing to do before planning, testing, and conclusion is necessary. First, smooth application of the process will be no downtime impact.

For this motion. So also as for the constraint, as your concern is the question asking. Okay. For this, the approach we can do is we can do automation setup. We can use as your SDK for Python. We can use as your SDK for Python as your management to interact with as well. So for employee, install the required packages using it. After you use as your service principal, you know, as your service principal, the appropriate permissions to deploy and meet as your resources. Store the services and potentials Securly suggest an environment as your keyboard. After that, we do deployment or to assist tab. They use Azure resource manager templates. Define you as your instructor using ARM templates. ARM templates is not defined structure, and you as your policy definition and assignment. Define as your person using JSON or Python SDK and create policy assignments to enforce these policies across your SQL subscription and source group programmatically. Now we can use our Python script execution. We can write Python script to the bare ARM tablets and integrate the Azure policy assignments within our Python script to enforce compliance during deployments. This we can perform it. And for the transition, we can do error handling logging. We can implement robust error handling and logging mechanism in our Python script to capture deployment errors if any happens and keep testing it itself. By leveraging the Python and the Azure SDK, we can automate as your deployment by ensuring compliance with Azure policy, thereby maintaining governance and security. Plus, there's also sufficient.

K. So for this portion, the answer must be we can, the strategy can use the following strategy that we give you as your monitoring step set up We can set up as your monitor to create a performance metrics for our Azure resources and define the metrics that are critical for automation. It does like CPU usage, memory usage, and latency request frequency. After that, we can use our power shelves to design. We can write a process for them to authenticate as you're using Azure self or use, and and we get a treat of the performance metrics as your monitor to get the marketing part. And we can then evaluate the metrics against the different threshold potential which we apply. And we can take action based on the evolution. And we can use authentication and module input. We can authenticate any to us using service principle, then should all interactive login. And you can import the Azure PowerShell module to connect to our Azure subscription. And we can also use retrieval prospecting metrics. We can use metric command line to the profile metrics for resources and specify parameters such as source group, so on the same metrics and time range. Now we can get metrics to evaluate against different thresholds or conditions and define the logic. But the managers will be different based on the current metrics. We can also use the automation actions and implement automation action based on evolution condition. Actions can include as scaling as a social like VM or m services for the status of sales. And we can basically secure the powershell script to produce as your automation as a function of others to do to monitor this resolution and just threshold as per the needs. There's a third thing we can perform. So by following this strategy, we can effectively automate Azure Resource Management based on power form a DC PowerShell to ensure personal skills appropriately to meet the workload dependence.

So so the code in the black part black part. So I have to use the We can again ask the question for more approval. I think. K. I'm gonna send this question. So from the code, I get you there that code that has to start the show the virtual machine, which should be starting them instead of the error. I get that at the error is basically compute plan, bunch of machine stop detail optician. It should be written accurately. Uh, compute dash client dot virtual dash machines dot start. So for the answer will be compute dash client dot virtual dash machine dot start. This is the answer for this error for the code.

Another portion of that area. So to find the center and expect impact. Okay. The less center I have placed in the impact of the script as usual. Okay. So for this, I will So the syntax error, which I got in the script regarding the east US is in the symbol before the start of East cuisine not included as the data centers of the script. The script must be written properly. As the east to us is need to be changed And the easiest should be put in the columns is not there in the syntax. That's causing the error. We have to add the upper. Okay. So the I got the answer was no. The syntax error will cause the script to fail as the east west string will be interpreted as a variable instead of the reason. As a result, the script will not be able to create the resource group. And then the action of creating new Azure resource group named resource work group will not be achieved. This will be the answer for this very good question. So that's it.

Okay. So now it's a lot of trouble. Still. So basically, the liability helps to get the solution with high availability of fault tolerance. You can consider LCI load balancing and security implements strong authentication of user users and encrypted data at rest and in transit. And post project is also opt optimize the Azure services and sources. And, of course, we select active services, tiers, and source size. It's not. Do you want the question again? You can use a Terraform to create a highly available of evolution using Azure app service and Azure database. Basically, for reliability, you use use the app services in a multiscient confusion using the load balancer and directory manager to ensure high ability and security. We can use the implement authentication with Azure Active Directory and could data address and attach it and convert fiber tools. And for cost optimization, you can use to the appropriate service tiers and resources based on the performance needs and utilize these are instances for computer resources and for of performance efficiency, we can implement the actual strategies for frequently accessed data. And now we come to operation excellence. For that, we can use automated department with Terraform and monitoring the solution as you are monitoring and trade with the SRT box for CAC testing of the ablutions. And by combining this test station, we with the venture, therefore, we can create a solution at least with the requirements as well after your framework. There's the best strategy.

So for this question, we can by can this tooling can similarly enhance CICD pipelines for Azure by providing flexibility or tuition abilities. And for this, we can leverage use improved CICD workflows across multiple Azure services data like Azure SDK and libraries. Python of our of our server SDK libraries for Azure suggest Azure MGMT packages, which allows to interact with your services automatically. We can use SDK to automate deployments, manage resources, and integrate as a service into our CICD pipelines. And the second, we can use Azure CLI integration. Python can wrap from Azure CLI command using support or OS modules enabling seamless edition of Azure CLI command with Python service. Service provides flexibility in handling Azure resources and service data from Python. We can use the infrastructure as a code. Python can be managed to enforce as a code of public user data forms using tools like Terraform or Azure resource manager, which is ER and TapCris. We can automate the provision and confusion of Azure resources and suit consistency and reduce the flaws environments. We can use testing and validation. And by this, we can integrate testing frameworks to automate testing of Azure deployments and services since that the departments meets functional and not for requirements before prompting change changes to production. Now we can use the monitoring and logging. Python can can integrate with Azure monitor APIs or SDK to retrieve monitoring data and logs from Azure services, send us proactive monitoring and troubleshoot deployments. Now we can use the format or tradition, but you can set an operational layer for Compass CICD workforce and all multiple sources of business. There is user security and complex automation. Python's it can automate security checks and compress where addition with CICD pipeline, you can create with Azure Security Center API or Azure policy API to enforce security policy and compliance standard for Azure resources. So by leveraging Python in our CSCD pipelines of Azure, we can achieve data automation efficiency and reliability across Azure services.

So Okay. So we're getting high ability as you're using the iPhone was designing the structure with 10 failures and made double check continuity. So for this approach, we can use a virtual machine with ability cells. We can use as your ability set to our PM states across multiple fault domains and update domains within Azure data center. And telephone supports defining ability sets and function VM senses, but they have to ensure that if 1 physical server or rack fails, your VM instances remain available so that we can begin also use the Azure load balancer. We can deploy load balancer to switch incoming traffic even across multiple view instances and the Terraform configure back end pools and help us and low balance rules for both internal and external traffic. And we can use your SQL database. Key as your SQL database built in fireability features such as geo replication and failover groups. Terraform can configure these features to a complete data across Azure regions for distribute disaster recovery and automation failover. We can also use Azure app services with app service environment for high scalability. And as soon as your app services can use app service environment, which provides dedicated and executed environments. Configure Azure service environment using Terraform to ensure that your abuses are escalated. We use that schema feature. And we can use Azure Kubernetes services, deploy EKS cluster across multiple resources for latency and high rating. User Terraform would find EKS conversion will not post networking and get cluster auto scaling. So this can see we can use for cost management and monitoring and backup disaster recovery, basically. So following the solution with Terraform, we can design and deploy high level as well as effectively and service and ability in our missions and service running as your adjust the configuration based on your specific permits and workload patterns to achieve of your performance and reliability so that we can do.