Cisco 300-910 Practice Test Questions, Exam Dumps

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300-910 Cisco Practice Test Questions and Exam Dumps

Question No 1:

A DevOps engineer must validate the working state of the network before implementing a CI/CD pipeline model.

Which configuration management tool is designed to accomplish this?

A. Jenkins
B. Genie CLI
C. Travis CI
D. Python YAML data libraries

Answer: B

Explanation:

To validate the working state of a network before implementing a Continuous Integration/Continuous Deployment (CI/CD) pipeline, a DevOps engineer would typically rely on a configuration management or network automation tool that helps check the health and status of network devices and services.

Here’s an analysis of the options provided:

Option A – Jenkins:
Jenkins is a widely used CI/CD tool that automates the build, test, and deployment process. While Jenkins plays a significant role in implementing CI/CD pipelines, it does not focus on validating the network's working state. Jenkins is primarily used for software automation, not for direct network state validation. Therefore, A is not the right tool for this task.

Option B – Genie CLI:
Genie CLI is a tool designed for network automation. It allows network engineers and DevOps engineers to interact with and validate network devices and configurations. Genie is part of Cisco's Automation Suite and is built specifically to help in validating network states, gathering device configurations, and performing network verification tasks before proceeding with higher-level automation like CI/CD pipeline implementation. Therefore, B is the correct answer because it is the tool designed to validate network states.

Option C – Travis CI:
Travis CI is a cloud-based CI/CD tool that automates the building, testing, and deployment of software applications. While Travis CI is great for software development and deployment, it is not designed to validate network configurations or states. It focuses more on the software delivery lifecycle, not on network validation. Therefore, C is not the correct answer.

Option D – Python YAML data libraries:
Python libraries for working with YAML data (like PyYAML) allow for easy reading and writing of YAML configuration files. While YAML is often used to describe configurations (including CI/CD pipelines or network configurations), these libraries are not specialized in network state validation. They may be part of the automation workflow, but by themselves, they do not validate network states. Therefore, D is not the best choice for validating the network before implementing a CI/CD pipeline.

In conclusion, B (Genie CLI) is the best tool for the task because it is specifically designed for validating network states and configurations, making it ideal for ensuring the network is ready before implementing CI/CD automation.

Question No 2:

Which two practices help make the security of an application a more integral part of the software development lifecycle? (Choose two.)

A Add a step to the CI/CD pipeline that runs a dynamic code analysis tool during the pipeline execution.
B Add a step to the CI/CD pipeline that runs a static code analysis tool during the pipeline execution.
C Use only software modules that are written by the internal team.
D Add a step to the CI/CD pipeline to modify the release plan so that updated versions of the software are made available more often.
E Ensure that the code repository server has enabled drive encryption and stores the keys on a Trusted Platform Module or Hardware Security Module.

Correct Answer: A and B

Explanation:

To integrate security more effectively into the software development lifecycle (SDLC), it’s important to adopt practices that not only detect vulnerabilities but also prevent them from being introduced into the system. Here’s a breakdown of each option:

A Add a step to the CI/CD pipeline that runs a dynamic code analysis tool during the pipeline execution:
Dynamic Code Analysis tests an application while it's running, simulating real-world attacks and identifying vulnerabilities during runtime. By adding dynamic code analysis to the CI/CD pipeline, vulnerabilities can be detected as part of the development process, allowing teams to address security issues before they reach production. This practice helps make security a continuous part of development, detecting issues in real time.
B Add a step to the CI/CD pipeline that runs a static code analysis tool during the pipeline execution:
Static Code Analysis analyzes the source code without executing it. It looks for potential security flaws such as code that could lead to SQL injection, buffer overflows, or other vulnerabilities. Integrating static code analysis into the CI/CD pipeline allows developers to catch security issues early in the development process, making security a built-in part of the software lifecycle.
C Use only software modules that are written by the internal team:
While it might seem like using only internal software modules would improve security, this is not a primary practice for integrating security into the SDLC. In fact, many external modules and libraries are well-tested and may have security benefits due to external scrutiny and active maintenance. Relying solely on internal development without evaluating external solutions might limit access to better security practices.
D Add a step to the CI/CD pipeline to modify the release plan so that updated versions of the software are made available more often:
While frequent updates to the software can be important for improving functionality and responsiveness, simply releasing updated versions more frequently doesn’t directly contribute to security. Regular updates are beneficial for patching vulnerabilities but don’t necessarily improve security practices within the SDLC. This practice is more related to software deployment and release management rather than security integration.
E Ensure that the code repository server has enabled drive encryption and stores the keys on a Trusted Platform Module or Hardware Security Module:
While securing the code repository server is important for data protection, it is more related to data confidentiality and storage security rather than integrating security into the development process. It’s a good security practice, but it doesn’t directly impact the way the development process identifies or mitigates vulnerabilities in the code itself.

In conclusion, the most effective practices for making security an integral part of the SDLC are A and B, as they ensure that security testing (both dynamic and static) is built into the development pipeline, allowing for early identification and mitigation of vulnerabilities.

Question No 3:

A CI/CD pipeline that builds infrastructure components using Terraform must be designed. A step in the pipeline is needed that checks for errors in any of the .tf files in the working directory. It also checks the existing state of the defined infrastructure.

Which command does the pipeline run to accomplish this goal?

A. terraform plan
B. terraform check
C. terraform fmt
D. terraform validate

Answer: D

Explanation:

In this case, the goal is to check for errors in any of the .tf files (Terraform configuration files) and also verify the existing state of the defined infrastructure. The appropriate Terraform command to accomplish this is terraform validate.

Let’s review each option:

A. terraform plan: The terraform plan command is used to create an execution plan, showing the actions Terraform will take to change the infrastructure to match the configuration. It checks for changes between the current infrastructure state and the desired state defined in the Terraform files, but it doesn’t validate the syntax or configuration correctness of the .tf files. While it does check the current state of the infrastructure, its primary purpose is to preview changes, not to validate configuration files.
B. terraform check: There is no terraform check command. Terraform does not have a command called "check." Therefore, this option is not valid.
C. terraform fmt: The terraform fmt command is used to format Terraform configuration files to ensure they follow the correct style and indentation. However, it does not validate the configuration files or check for errors. It only focuses on formatting the code and is not designed for checking the configuration correctness or infrastructure state.
D. terraform validate: The terraform validate command checks the syntax and validity of the Terraform configuration files in the working directory, including .tf files. It ensures that the configuration is structurally correct and internally consistent but does not apply the changes to the infrastructure. Additionally, it verifies that the configuration can be processed correctly, and it checks the existing state of the defined infrastructure. This command is exactly what is needed to check for errors in the .tf files and ensure that the configurations are valid before applying any changes.

In conclusion, the correct command to run in the CI/CD pipeline to check for errors in the Terraform files and verify the existing state of the infrastructure is terraform validate.

Therefore, the correct answer is D.

Question No 4:

Which type of testing should be integrated into a CI/CD pipeline to ensure the correct behavior of all of the modules in the source code that were developed using TDD?

A Soak testing
B Unit testing
C Load testing
D Volume testing

Correct answer: B

Explanation:

Test-Driven Development (TDD) is a software development process where tests are written before the code itself. The goal of TDD is to ensure that each small unit of functionality is thoroughly tested before it is integrated into the larger application. In this context, the most suitable type of testing for a CI/CD (Continuous Integration/Continuous Delivery) pipeline would focus on validating the behavior of individual modules or components of the codebase.

Let’s analyze each option:

A Soak testing: Soak testing is a type of performance testing where the system is subjected to a load over a long period of time to evaluate its stability and performance under sustained use. While important for performance validation, soak testing is not designed to validate individual code modules or the behavior of functionality developed using TDD. Therefore, this option is not appropriate for ensuring correct behavior of modules in TDD.

B Unit testing: Unit testing is the correct answer. Unit tests focus on testing individual modules or units of code in isolation to verify that each part functions as expected. In TDD, unit tests are written first, and they help ensure the correctness of each small piece of code as it is developed. Since TDD emphasizes writing tests for specific functionality before the code itself, unit testing fits perfectly within a CI/CD pipeline. These tests can be automated and run frequently as part of the pipeline to catch any regressions or issues early in the development process.

C Load testing: Load testing evaluates how a system performs under heavy load or stress, focusing on its scalability and behavior when handling a large number of users or requests. While important for assessing system performance, load testing does not address the correctness of individual code modules. Therefore, it is not the right fit for ensuring correct behavior of modules in a TDD-driven development environment.

D Volume testing: Volume testing involves testing the system with large volumes of data to ensure it can handle significant amounts of input. Like load testing, volume testing is focused on performance and scalability, not on ensuring the correctness of individual modules developed with TDD.

Given the focus on ensuring the correct behavior of the code modules developed using TDD, unit testing is the most appropriate choice. Unit tests check that each unit of functionality behaves as expected, making them essential for validating TDD implementations in a CI/CD pipeline.

Therefore, the correct answer is B unit testing.

Question No 5:

You are troubleshooting a Jenkins job that has failed, and the error message is displayed. How should you proceed with troubleshooting the job based on the error provided?

A Verify what the responding file created.
B Update pip.
C Install dependencies.
D Place the code in a container and run the job again.

Correct Answer: C

Explanation:

When a Jenkins job fails, particularly if the issue involves missing dependencies or Python packages, the most common troubleshooting step is to ensure that all required dependencies are installed.

Why "Install dependencies" is the Best Choice:

In many cases, Jenkins jobs fail due to missing libraries or packages that the job needs to run successfully. If the error message indicates that some dependencies are not found or are incorrect, the appropriate action would be to install the necessary dependencies. This can usually be done by running a command like pip install -r requirements.txt if it's a Python-based job. Ensuring that all dependencies are available and up-to-date is often the first step in resolving such issues.

Why the Other Options are Less Likely to be the Solution:

A. Verify what the responding file created:
This step might be useful if the error is related to a specific file creation or output. However, this doesn't directly address common issues related to missing dependencies or Python package errors.
B. Update pip:
While updating pip might help resolve issues related to outdated installation tools, this isn't always the first step. If the error message points to missing dependencies, updating pip might not be the most effective solution. It’s better to focus on ensuring that all required dependencies are installed, which is usually the root cause.
D. Place the code in a container and run the job again:
This option would be more suitable if the failure is due to environmental inconsistencies, such as differences between development and production environments. Running the code in a container (like Docker) ensures a consistent environment. However, this is a more complex solution and may not be necessary unless you're facing environment-related issues.

The most effective troubleshooting step in this case is to install the missing dependencies, making C the correct answer.

Question No 6:

Configuration changes to the production network devices are performed by a CI/CD pipeline. The code repository and the CI tool are running on separate servers.Some configuration changes are pushed to the code repository, but the pipeline did not start.

Why did the pipeline fail to start?

A. The CI server was not configured as a Git remote for the repository.
B. The webhook call from the code repository did not reach the CI server.
C. Configuration changes must be sent to the pipeline, which then updates the repository.
D. The pipeline must be started manually after the code repository is updated.

Answer: B

Explanation:

In a typical CI/CD pipeline, the flow of automation happens when changes (such as configuration updates) are pushed to a code repository. The CI server (Continuous Integration server) listens for changes and then triggers the appropriate actions to deploy or test the code. The way the CI server knows when to begin its task is through webhooks — automated calls sent from the code repository to the CI server whenever changes are pushed.

In this scenario, the pipeline did not start after changes were made to the repository. This suggests that there was an issue with the mechanism that is supposed to trigger the pipeline. A likely cause is that the webhook call from the code repository did not successfully reach the CI server. This can happen due to misconfigurations, network issues, or the webhook being disabled or incorrectly set up.

Why the other options are not correct:

A. The CI server was not configured as a Git remote for the repository:
While it's true that the CI server typically needs access to the repository, it does not necessarily have to be configured as a Git remote for the repository. Instead, the CI server typically accesses the repository via webhooks or by polling the repository. The issue is not about the Git remote configuration but about the webhook failing to notify the CI server.
C. Configuration changes must be sent to the pipeline, which then updates the repository:
This is incorrect. In a typical CI/CD setup, the repository is updated first, and the pipeline is triggered automatically (via a webhook or polling), not the other way around. The pipeline itself does not update the repository; it simply reacts to changes in the repository.
D. The pipeline must be started manually after the code repository is updated:
This is unlikely to be the issue unless explicitly configured that way, but it's generally considered poor practice to manually trigger CI/CD pipelines in modern workflows. The expectation is that the pipeline is automatically triggered by webhooks when code is pushed to the repository.

The most likely reason the pipeline did not start is that the webhook call from the code repository did not reach the CI server, meaning the CI server was not notified of the changes. Therefore, the correct answer is B.

Question No 7:

A new version of an application is being released by creating a separate instance of the application that is running the new code. Only a small portion of the user base will be directed to the new instance until that version has been proven stable.

Which deployment strategy is this example of?

A recreate
B blue/green
C rolling
D canary

Answer: D

Explanation:

The scenario described in the question—where a new version of an application is released and only a small portion of users are directed to it initially—represents the canary deployment strategy. Let's break down each of the options to understand why canary is the correct choice:

Option A: Recreate

In the recreate deployment strategy, the old version of the application is completely replaced by the new version. This means that the entire user base is switched to the new version of the application at once. There is no gradual rollout or segmentation of the user base. This is not the correct choice because the question involves gradual exposure to the new version.

Option B: Blue/Green

The blue/green deployment strategy involves maintaining two separate environments: one for the current version (blue) and one for the new version (green). Once the green environment is fully tested and stable, all users are switched from the blue environment to the green environment simultaneously. While this strategy also involves switching to a new version, it does not involve gradual rollout to a small portion of users. The entire user base moves to the new version at once after testing.

Option C: Rolling

In a rolling deployment strategy, the new version is gradually rolled out to different parts of the user base over time. This often happens by updating servers or instances one at a time, but it is done in a more continuous and rolling manner. However, it is typically not limited to directing traffic to a small portion of users at first. Instead, it involves a broader and more incremental approach, as opposed to the specific small initial portion mentioned in the question.

Option D: Canary

The canary deployment strategy involves releasing the new version of the application to a small subset of users (often referred to as the "canary group") first. This group helps test the stability of the new version in a live environment before rolling it out to the rest of the user base. In this strategy, if the new version proves to be stable, the deployment is gradually expanded to a larger portion of users. This matches the description in the question perfectly—where only a small portion of users are directed to the new version until it has been proven stable.

The canary deployment strategy is specifically designed for scenarios like the one described, where a new version of an application is released to a small portion of the user base first and expanded gradually. Thus, the correct answer is D.

Question No 8:

Which description of a canary deployment is true?

A Deployment by accident
B Deployment that is rolled back automatically after a configurable amount of minutes
C Deployment relating to data mining development
D Deployment to a limited set of servers or users

Answer: D

Explanation:

A canary deployment is a deployment strategy used in software engineering, particularly for deploying new features or versions of software in a controlled manner. The main goal of a canary deployment is to limit the exposure of potential issues by deploying the update to only a small, select group of users or servers before a full-scale rollout. This helps to test the new features or updates in a real-world environment, ensuring they perform as expected without affecting the entire user base or system.

The term “canary” comes from the practice of using canaries in coal mines to detect harmful gases. Similarly, in canary deployments, the "canaries" (small groups of users or servers) act as early indicators of any issues, allowing for quick action if problems arise. This strategy minimizes the risk associated with deploying new changes by ensuring that only a small portion of the user base is impacted if something goes wrong.

Here’s why D is the correct answer:

D. Deployment to a limited set of servers or users: This accurately describes the essence of a canary deployment. In this strategy, the update is first deployed to a small, controlled group (such as a few servers or a small percentage of users). This helps ensure that the deployment is stable and that any critical issues can be detected early before it affects a larger portion of the system or user base.

Now, let's explore why the other options are not correct:

A. Deployment by accident: A canary deployment is a deliberate and strategic approach to minimize risks during the rollout of new changes. It is not an accidental deployment but rather a well-planned method of testing and monitoring updates before a wider release.
B. Deployment that is rolled back automatically after a configurable amount of minutes: While some deployments may have rollback mechanisms based on time or conditions, this is not a defining feature of canary deployments. Canary deployments are about gradually releasing updates to a subset of users, not necessarily rolling them back after a set time. The focus is on controlled exposure and monitoring, not automatic rollback.
C. Deployment relating to data mining development: Canaries are not specifically tied to data mining or any particular area of software development. The canary deployment method is about risk management and controlled release of software updates across various domains, not just data mining.

In conclusion, D is the correct answer because it correctly describes the practice of deploying software to a limited set of users or servers in a canary deployment, allowing organizations to mitigate risk by catching issues early in the deployment process.