March 26, 2023

Top AWS Fault Injection Simulator (FIS) Interview Questions and Answers

    

    An application's performance, observability, and resilience can all be increased by using the AWS Fault Injection Simulator (FIS), a fully managed service. To help teams gain confidence in the behavior of their applications, FIS streamlines the process of setting up and performing controlled fault injection experiments across a variety of AWS services.


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Ques. 1): AWS Fault Injection Simulator (FIS) – what is it?

Answer:

It is simpler to increase an application's performance, observability, and resiliency by using the AWS Fault Injection Simulator, a fully managed service for fault injection experiments on AWS. Chaos engineering, which is the practise of straining an application in testing or production environments by generating disruptive events, such as a rapid spike in CPU or memory consumption, watching how the system reacts, and making modifications as necessary, uses fault injection experiments.


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Ques. 2): How closely do the defects that AWS FIS injects match up to actual world circumstances?

Answer:

The conditions produced by FIS fault inject actions are quite similar to those that exist in reality. As an illustration, increasing CPU usage actually uses CPU resources. The experience is the same as with any other throttling because the operation to throttle API calls throttles those requests at the control plane level. In contrast to the actual world, FIS gives you the power to halt an experiment at any time and, when practical, to specify rollback actions that go into effect when an action period is up.


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Ques. 3): What fundamental ideas underlie fault injection actions?

Answer:

A fault injection activity in FIS can result in a heavy CPU burden being placed on an EC2 instance. A distinct set of attributes are accepted by every FIS action. The property for this example of CPU load on an EC2 instance would be the% value of the load. There are many FIS actions with various property values. You may also manage the experiment's timeline and duration with the aid of FIS activities. You can select the StartTime and length of the experiment for each action. For instance, you may plan your experiment so that it includes a CPULoad action that raises the CPU load to 75%, starts at StartTime of 0, and runs for 60 seconds.


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Ques. 4): How do I keep track of an AWS FIS experiment's effects?

Answer:

You may check on an experiment's progress and see the results of each completed step using the FIS console. You may also utilise CloudWatch dashboards and monitoring to keep an eye on the condition of your Amazon resources. Throughout an experiment, FIS creates CloudTrail logs for every action it makes on your AWS resources, giving you complete visibility and auditing over all account activity.


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Ques. 5): How can I begin utilising AWS FIS?

Answer:

To define, manage, and control the experiment, utilise either the FIS console or the Amazon CLI. To begin, design an experiment that contains any actions you want to take against any number of targets. The experiment template allows you to define every aspect of the experiment, including the targets, actions, alarm, and stop circumstances. The experiment can then be started. A distinct execution-id is returned by each start-experiment. The execution-id argument can be used to monitor the progress of a particular run.


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Ques. 6): How does Chaos Engineering work?

Answer:

Chaos engineering is the technique of stressing an application in testing or production environments by inflicting disruptive events—like server failures or API throttling—and then watching the system's reaction and making adjustments as necessary. Teams can better understand hidden problems, monitoring blind spots, and performance bottlenecks in distributed systems by using chaos engineering to simulate real-world settings. Starting with steady-state behaviour analysis, creating an experiment hypothesis (for example, terminating x instances will result in x% more retries), running the experiment by injecting fault actions, watching for rollback conditions, and fixing the flaws are the first steps.


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Ques. 7): What are the core concepts of the target resources?

Answer:

The Amazon resources selected as the experiment's targets for fault injection. Examples include tags, instance-id, cluster-id, VPC, etc. Targets could be random or specified Amazon resources (s).


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Ques. 8): How can I find out the progress of the AWS FIS experiment?

Answer:

To monitor the experiment's status, utilise the management console or AWS CLI. You can list all experiment executions with the list-experiment-executions command. You can obtain information about a specific execution with get-experiment-execution. You can obtain the precise actions taken and their corresponding outcomes by requesting information about a specific run. You can record and continually track the actions that are taken using CloudTrail.


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Ques. 9): Is an agent required for AWS FIS?

Answer:

The installation of any agents in your resources is not necessary for many action kinds. The SSM agent is necessary for instance level problems, such as increased CPU and memory usage.


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Ques. 10): Why need I to utilise AWS FIS?

Answer:

To enhance performance, observability, and resiliency, utilise FIS to scale up the discovery of an application's flaws. You no longer need to handle complicated tooling since FIS provides practical fault injection experience from a centralised UI. Using FIS's adaptable experiment template, you can create unique experiments to model complicated outage scenarios and combine several failure kinds within a single experiment. Stop conditions are built-in safety procedures offered by FIS that halt an experiment before it spirals out of control.


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Ques. 11): What defect types can I introduce using AWS FIS?

Answer:

You can run pre-defined fault injection experiments using FIS on several platforms, including computation, databases, networks, and more.


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Ques. 12): What fundamental ideas underpin the conduct of an experiment?

Answer:

One or more sets of Target, Fault Injection Actions, Post Action, and Stop Condition are included in a FIS experiment. With fault injection action(s) and an AWS target, you begin the FIS experiment (s). You broaden the scope of the experiment to incorporate more Amazon resources and fault injection actions as your application's resilience increases. Once the experiment is specified, you may set up a schedule for it to run at particular times.


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Ques. 13): How do I put a halt to the test?

Answer:

You can use the CLI command stop-experiment or the FIS console to execute stop-experiment. You can also halt an experiment by using CloudWatch enable-alarm-actions.


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Ques. 14): Do I need to do anything before using AWS FIS?

Answer:

Before adopting FIS, there are a number of factors to consider. You must first decide on the experiment's intended deployment. If this is your first trial, you might want to think about starting in a test or pre-production setting. You can introduce fault injection as your fault injection experiment develops to continuously evaluate resilience in the production environment. The steady-state behaviour must be defined, and significant technical and business parameters (such as latency and CPU load) must be identified (e.g., failed logins per minute, number of retries, page load speed). The next step is to formulate a hypothesis for the outcome of the experiment. If done, the impact of any commercial or technical indicator should not be greater than, is how your hypothesis is characterised. For an authentication service, for instance, your hypothesis might be "if network latency increases by 10%, there will be fewer than 1% increase in login failures." After the experiment, you assess whether the application's resilience meets your technical and business expectations.


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Ques. 15): Can I incorporate AWS FIS into my CD pipeline?

Answer:

Sure, you may incorporate FIS into your pipeline for continuous delivery. You will be able to do this as part of your software delivery process to repeatedly test how fault actions affect the outcome.


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