The Open Group OGEA-103 TOGAF Enterprise Architecture Combined Part 1 and Part 2 Exam Dumps and Practice Test Questions Set 6 Q76-90

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Question 76

Which solution best ensures automated compliance checks in Azure DevOps pipelines while preventing non‑compliant resources from being deployed?

A) Use Azure Monitor alerts to detect violations
B) Apply Azure Policy integrated with pipeline deployments
C) Configure manual reviews before each release
D) Document compliance standards in a shared wiki

Answer: B)

Explanation:

Azure Monitor alerts provide visibility into metrics and logs, enabling detection of anomalies and violations after resources are deployed. While useful for monitoring, this approach is reactive. It identifies issues post‑deployment rather than preventing them from occurring. Compliance enforcement requires proactive measures that stop non‑compliant resources from being created in the first place.

Manual reviews before each release rely on human intervention to check compliance. This method is slow, error‑prone, and inconsistent. It does not scale across multiple teams or environments. While reviews can catch issues, they cannot guarantee that all deployments meet standards, especially in fast‑paced DevOps environments where automation is critical.

Documenting compliance standards in a shared wiki provides guidance and reference material for developers. It helps raise awareness but does not enforce compliance. Developers may overlook documentation or interpret standards differently. Documentation alone cannot prevent non‑compliant resources from being deployed.

Applying Azure Policy integrated with pipeline deployments is the correct solution. Azure Policy enforces compliance by auditing and denying non‑compliant configurations. When integrated with pipelines, it ensures that deployments adhere to organizational standards automatically. Policies can restrict resource types, enforce naming conventions, require tags, and validate configurations. This proactive enforcement prevents violations before they reach production.

The reasoning for selecting Azure Policy integration is that compliance must be automated and enforced consistently. Azure Policy provides proactive governance, ensuring that pipelines deploy only compliant resources. Monitoring, manual reviews, and documentation are supportive but insufficient. Automated enforcement through Azure Policy is the best choice.

Question 77

Which practice best supports continuous delivery in Azure DevOps by enabling safe, incremental releases with automated rollback?

A) Deploy changes manually during maintenance windows
B) Use feature flags with progressive exposure
C) Schedule weekly batch deployments to production
D) Apply static configuration files for all environments

Answer: B)

Explanation:

Deploying changes manually during maintenance windows introduces delays and risks. Manual deployments are error‑prone and lack automation. They do not provide incremental exposure or rollback capabilities. This approach undermines continuous delivery principles, which emphasize automation, speed, and safety.

Scheduling weekly batch deployments to production delays feedback and increases risk. Large batches contain many changes, making it harder to identify and fix issues. This approach reduces agility and increases the likelihood of defects reaching users. Continuous delivery requires small, frequent releases, not large, infrequent batches.

Applying static configuration files for all environments ensures consistency but lacks flexibility. Static configurations cannot adapt to progressive exposure or rollback. They provide repeatability but do not support safe, incremental releases. Continuous delivery requires dynamic control over deployments.

Using feature flags with progressive exposure is the correct practice. Feature flags allow teams to enable or disable functionality at runtime without redeploying code. Progressive exposure gradually releases features to subsets of users, reducing risk. If issues occur, features can be rolled back instantly by toggling flags. This approach supports safe, incremental releases, rapid feedback, and automated rollback. It aligns with continuous delivery principles by enabling frequent, controlled deployments.

The reasoning for selecting feature flags is that continuous delivery requires automation, safety, and flexibility. Feature flags provide dynamic control, enabling progressive exposure and rollback. Manual deployments, batch releases, and static configurations lack these capabilities. Feature flags are the best choice for safe, incremental releases.

Question 78

Which approach best enables automated infrastructure provisioning in Azure DevOps pipelines while supporting version control and repeatability?

A) Use Azure Resource Manager templates stored in source control
B) Manually configure resources in the Azure portal
C) Apply ad‑hoc CLI commands during deployments
D) Document infrastructure setup in a shared wiki

Answer: A)

Explanation:

Manually configuring resources in the Azure portal is error‑prone and inconsistent. It relies on human effort, which can lead to misconfigurations and drift between environments. Manual processes do not scale and cannot guarantee repeatability.

Applying ad‑hoc CLI commands during deployments provides automation but lacks declarative definitions. CLI commands are imperative, describing how to perform actions rather than the desired end state. This makes it harder to maintain consistency across environments and increases the risk of drift.

Documenting infrastructure setup in a shared wiki provides guidance but does not enforce consistency. Developers must manually follow instructions, which can lead to errors and inconsistencies. Documentation is useful for knowledge sharing but not for automation or enforcement.

Using Azure Resource Manager templates stored in source control is the correct approach. ARM templates define infrastructure declaratively, specifying the desired state of resources. Pipelines can deploy templates automatically, ensuring consistency and repeatability across environments. Templates support parameterization, enabling customization for different scenarios while maintaining a common structure. They also integrate with source control, allowing versioning and auditing of infrastructure changes.

The reasoning for selecting ARM templates is that infrastructure as code requires declarative, automated definitions of resources. ARM templates provide consistency, repeatability, and scalability, aligning with DevOps principles. Other methods either rely on manual effort or lack declarative definitions, making ARM templates the best choice.

Question 79

Which solution best enforces security and compliance in Azure DevOps pipelines by preventing deployments that violate organizational rules?

A) Use Azure Monitor to detect violations after deployment
B) Apply Azure Policy integrated with pipeline workflows
C) Rely on manual reviews before each release
D) Document compliance standards in a shared wiki

Answer: B)

Explanation:

Azure Monitor provides observability into metrics, logs, and alerts across Azure resources. It can detect violations after deployment, such as misconfigured resources or unusual activity. While valuable for monitoring, this approach is reactive. It identifies issues post‑deployment rather than preventing them from occurring. Compliance enforcement requires proactive measures that stop non‑compliant resources from being created in the first place.

Applying Azure Policy integrated with pipeline workflows is the correct solution. Azure Policy enforces compliance by auditing and denying non‑compliant configurations. When integrated with pipelines, it ensures that deployments adhere to organizational standards automatically. Policies can restrict resource types, enforce naming conventions, require tags, and validate configurations. This proactive enforcement prevents violations before they reach production.

Question 80

Which practice best supports continuous deployment in Azure DevOps by enabling safe, incremental releases with automated rollback?

Answer: B)

Explanation:

Deploying changes manually during maintenance windows introduces delays and risks. Manual deployments are error‑prone and lack automation. They do not provide incremental exposure or rollback capabilities. This approach undermines continuous deployment principles, which emphasize automation, speed, and safety.

Scheduling weekly batch deployments to production delays feedback and increases risk. Large batches contain many changes, making it harder to identify and fix issues. This approach reduces agility and increases the likelihood of defects reaching users. Continuous deployment requires small, frequent releases, not large, infrequent batches.

Using feature flags with progressive exposure is the correct practice. Feature flags allow teams to enable or disable functionality at runtime without redeploying code. Progressive exposure gradually releases features to subsets of users, reducing risk. If issues occur, features can be rolled back instantly by toggling flags. This approach supports safe, incremental releases, rapid feedback, and automated rollback. It aligns with continuous deployment principles by enabling frequent, controlled deployments.

The reasoning for selecting feature flags is that continuous deployment requires automation, safety, and flexibility. Feature flags provide dynamic control, enabling progressive exposure and rollback. Manual deployments, batch releases, and static configurations lack these capabilities. Feature flags are the best choice for safe, incremental releases.

Question 81

Which approach best enables infrastructure as code in Azure DevOps pipelines while ensuring consistency and repeatability across environments?

Answer: A)

Explanation:

Question 82

Which solution best ensures secure management of secrets and credentials in Azure DevOps pipelines while supporting rotation and auditing?

A) Store secrets in pipeline variables with masking
B) Use Azure Key Vault integrated with pipelines
C) Embed credentials directly in YAML pipeline definitions
D) Save secrets in Git repository with restricted access

Answer: B)

Explanation:

Storing secrets in pipeline variables with masking provides a basic level of protection by hiding values in logs and restricting visibility. It is convenient for small-scale scenarios but lacks advanced features such as automated rotation, centralized management, and fine-grained access control. Masked variables can still be exposed if misconfigured, and they do not provide auditing capabilities.

Embedding credentials directly in YAML pipeline definitions is highly insecure. It hardcodes sensitive information into version-controlled files, making them visible to anyone with repository access. This approach violates security best practices, increases the risk of accidental leaks, and complicates rotation. Credentials should never be stored directly in source code or configuration files.

Saving secrets in a Git repository with restricted access also poses significant risks. Even with access controls, secrets stored in repositories are vulnerable to accidental exposure, cloning, or mismanagement. Rotation becomes difficult, and auditing secret usage is nearly impossible. This method is discouraged because repositories are designed for code, not sensitive data.

Using Azure Key Vault integrated with pipelines is the correct solution. Key Vault provides centralized secret management, encryption, rotation, and auditing. Pipelines can securely retrieve secrets at runtime without exposing them in code or logs. Access policies ensure that only authorized identities can access secrets, and integration with Azure Active Directory strengthens authentication. Key Vault also supports certificates and keys, making it versatile for multiple security needs.

The reasoning for selecting Key Vault integration is that it aligns with DevOps principles of security, automation, and compliance. It ensures secrets are managed securely, rotated automatically, and audited effectively. Other methods either lack security features or introduce risks, making Key Vault the best choice.

Question 83

Which practice best supports continuous integration in Azure DevOps by ensuring rapid feedback and preventing broken builds from reaching main branches?

A) Enable gated check-ins with build validation
B) Schedule nightly builds for integration testing
C) Allow direct commits to main without checks
D) Run manual builds before merging changes

Answer: A)

Explanation:

Scheduling nightly builds for integration testing has been a traditional approach in software development teams to ensure that newly integrated code functions correctly with the rest of the system. This method executes a batch build process, typically at the end of each workday or during off-hours, to compile the entire codebase, run automated tests, and report on any integration issues. While this provides a level of assurance that the code integrates correctly, it inherently introduces delays in feedback to developers. If a defect is introduced early in the day, it may not be detected until the nightly build completes, potentially leaving broken or unstable code in the repository for several hours. This delayed feedback increases the risk of propagating errors, making it more difficult to pinpoint the source of the problem. Developers may continue working on top of faulty code, compounding issues and increasing the effort required to diagnose and resolve defects. Nightly builds are fundamentally reactive; they detect problems after the fact rather than preventing them from entering the main branch, which goes against the principles of continuous integration that emphasize early, immediate detection of issues.

Allowing developers to commit changes directly to the main branch without any validation checks is another practice that introduces significant risk. Direct commits bypass automated build and test pipelines, meaning there is no guarantee that changes are validated before they become part of the shared codebase. This practice can easily introduce broken builds, regressions, or subtle defects into the main branch. Once a main branch is compromised, the stability of the entire system is threatened, and other developers may encounter conflicts or errors when integrating their changes. In larger teams or complex projects, this can quickly escalate into a situation where the main branch is unreliable, reducing confidence in the codebase and slowing down development velocity. Continuous integration is designed to prevent such issues by enforcing automated, consistent validation of every change, and bypassing this process undermines those benefits.

Running manual builds before merging changes is another approach some teams attempt to use to ensure code correctness. In this scenario, developers are expected to initiate builds locally or on a separate build server before submitting their changes. While this may catch some errors, it relies heavily on individual discipline and consistency. Developers may forget to run builds, execute them incorrectly, or skip certain tests, resulting in incomplete validation. Manual processes are inherently inconsistent and prone to human error, particularly in larger teams or when complex dependencies exist. Additionally, manual builds do not scale effectively as the project grows. The process becomes more cumbersome and less reliable as more developers contribute, increasing the likelihood that untested or broken code is integrated into the shared repository.

Enabling gated check-ins with build validation provides the most effective solution to these challenges. Gated check-ins prevent changes from being merged into the main branch until they have successfully passed a series of automated validation steps. These validations typically include compilation, unit tests, linting, security scans, and any other project-specific quality checks. When a developer submits a change, the gated check-in system triggers an automated build and test process. If any part of the validation fails, the change is rejected, and the developer receives immediate feedback about the failure. This mechanism ensures that only fully validated code enters the main branch, maintaining the integrity and stability of the shared codebase. Gated check-ins enforce discipline automatically, reducing the reliance on individual judgment or memory to perform manual builds or tests.

The reasoning for selecting gated check-ins as the preferred practice lies in the core principles of continuous integration. Continuous integration emphasizes the importance of validating every code change as soon as it is made, providing rapid feedback, and preventing defects from propagating. Gated check-ins enforce these principles by automating validation at the point of submission, ensuring that every change meets the project’s quality standards before it is merged. Compared to nightly builds, which provide delayed feedback, or manual builds, which are inconsistent and error-prone, gated check-ins provide immediate, reliable, and consistent feedback. Unlike allowing direct commits, which risk compromising the main branch, gated check-ins safeguard stability while supporting rapid development cycles. This approach enables teams to maintain a high-quality, stable codebase while accelerating development velocity, providing a controlled yet efficient mechanism for continuous integration. By incorporating automated build and test steps into the merge process, gated check-ins align with best practices in modern software development, ensuring that defects are detected early, quality is maintained, and the main branch remains a reliable foundation for ongoing development and deployment.

Question 84

Which approach best enables infrastructure as code in Azure DevOps pipelines while ensuring consistency and repeatability across environments?

A) Use Azure Resource Manager templates in pipelines
B) Manually configure resources in the Azure portal
C) Apply configuration changes directly via CLI commands
D) Document infrastructure setup in a wiki for developers

Answer: A)

Explanation:

Manually configuring resources in the Azure portal is error-prone and inconsistent. It relies on human effort, which can lead to misconfigurations and drift between environments. Manual processes do not scale and cannot guarantee repeatability.

Applying configuration changes directly via CLI commands provides automation but lacks declarative definitions. CLI commands are imperative, meaning they describe how to perform actions rather than the desired end state. This makes it harder to maintain consistency across environments and increases the risk of drift.

Documenting infrastructure setup in a wiki provides guidance but does not enforce consistency. Developers must manually follow instructions, which can lead to errors and inconsistencies. Documentation is useful for knowledge sharing but not for automation or enforcement.

Using Azure Resource Manager templates in pipelines is the correct approach. ARM templates define infrastructure declaratively, specifying the desired state of resources. Pipelines can deploy templates automatically, ensuring consistency and repeatability across environments. Templates support parameterization, enabling customization for different scenarios while maintaining a common structure. They also integrate with source control, allowing versioning and auditing of infrastructure changes.

Question 85

Which solution best ensures automated compliance checks in Azure DevOps pipelines while preventing non‑compliant resources from being deployed?

Answer: B)

Explanation:

Azure Monitor alerts provide observability into metrics and logs, enabling detection of anomalies and violations after resources are deployed. This approach is reactive, identifying issues post‑deployment rather than preventing them from occurring. Compliance enforcement requires proactive measures that stop non‑compliant resources from being created in the first place.

Question 86

Which practice best supports continuous delivery in Azure DevOps by enabling safe, incremental releases with automated rollback?

Answer: B)

Explanation:

Deploying changes manually during designated maintenance windows is an approach that some organizations still rely on, primarily to try to maintain control over production systems. This method involves manually executing scripts, updating configuration files, and restarting services at specific scheduled times. While the intention is to reduce the impact on users, this method introduces a series of risks and inefficiencies that are significant in modern software delivery practices. Manual deployments depend heavily on human effort, which increases the probability of errors occurring during the process. Even highly experienced operators can make mistakes under pressure or when handling complex systems, leading to misconfigurations, partial deployments, or failures that could have been avoided with automation. Furthermore, this manual process is inherently slow and introduces delays. Every deployment must be carefully coordinated, reviewed, and executed, which prevents organizations from releasing new features quickly or responding to urgent issues promptly. There is also no inherent mechanism for incremental exposure in manual deployments. Once a change is applied, it affects the entire production environment immediately, increasing the potential blast radius if something goes wrong. Rollback capabilities are often cumbersome, requiring manual intervention to revert systems to a previous state, which may further increase downtime or risk to business operations. This approach directly undermines the principles of continuous delivery, which emphasize automated, repeatable, safe, and fast deployments that can be executed frequently without disruption.

Scheduling weekly batch deployments to production presents a similar set of challenges. When organizations consolidate multiple changes into a single, large deployment, they may believe they are increasing efficiency by reducing the number of deployment events. In practice, however, this strategy introduces delays in feedback because changes are not released incrementally. Developers must wait until the batch is complete and deployed to learn whether their code is working correctly in production. Large batch deployments also increase risk because they combine many changes at once. If a defect is discovered post-deployment, identifying its root cause is more difficult because multiple changes are involved. In addition, batch deployments reduce agility, preventing organizations from responding quickly to user needs or emerging market opportunities. Continuous delivery, by contrast, emphasizes the value of small, frequent releases. Smaller changes are easier to test, monitor, and, if necessary, roll back. Frequent releases reduce the likelihood that defects affect all users simultaneously, while providing faster feedback loops to development teams so they can continuously improve their code.

Applying static configuration files across all environments ensures a certain level of repeatability and consistency, but it lacks the flexibility necessary for dynamic, progressive releases. Static configuration files apply the same settings to all users at once, which means there is no mechanism to gradually expose features or conduct controlled experiments. These files may enforce repeatable deployments, but they do not enable the organization to mitigate risk by progressively rolling out new functionality. In addition, static configurations complicate rollback in the event of a problem. Any rollback requires either manually reverting files or reapplying previous configurations, which adds time, complexity, and the potential for human error. Modern continuous delivery frameworks require a higher degree of adaptability, allowing features to be released incrementally and controlled in real-time according to performance metrics, user feedback, or operational considerations.

Using feature flags with progressive exposure addresses all of these limitations effectively. Feature flags enable teams to toggle functionality on or off at runtime, without requiring a new deployment. This allows for incremental releases, where features are initially exposed to a small subset of users and gradually rolled out to a wider audience. Progressive exposure limits risk by ensuring that if a bug or undesired behavior is discovered, only a small number of users are affected, and the feature can be quickly disabled with minimal operational impact. Feature flags also provide rapid feedback to development teams. Metrics can be collected from the subset of users experiencing the new feature, enabling developers to monitor performance, usability, and any unintended consequences before a full rollout. In addition, feature flags facilitate automated rollback. If an issue arises, toggling the flag immediately removes the feature from the user experience, eliminating the need for time-consuming manual intervention and reducing potential downtime. This approach supports continuous delivery principles by providing the automation, control, and safety mechanisms required to deploy changes frequently, reliably, and with minimal risk.

The reasoning for selecting feature flags over manual deployments, weekly batch releases, or static configuration files is rooted in their alignment with the core goals of continuous delivery: automation, safety, and flexibility. Manual deployments are slow, error-prone, and difficult to scale, making them unsuitable for frequent releases. Batch deployments delay feedback, increase risk, and reduce agility. Static configuration files ensure repeatability but lack the flexibility for incremental release and rollback, which are essential for modern software delivery. Feature flags uniquely provide the dynamic control necessary to manage risk, enable progressive exposure, collect early feedback, and allow immediate rollback if problems occur. They support small, frequent, and controlled releases, which reduces risk while enabling faster delivery of value to users. By combining automation, operational safety, and runtime flexibility, feature flags represent the most effective approach for implementing continuous delivery in a modern software development environment, making them the optimal choice for organizations seeking to deploy features efficiently and reliably while minimizing potential disruption to users.

Question 87

Which approach best enables infrastructure as code in Azure DevOps pipelines while ensuring consistency and repeatability across environments?

Answer: A)

Explanation:

Question 88

Which solution best ensures secure management of secrets and credentials in Azure DevOps pipelines while supporting rotation and auditing?

Answer: B)

Explanation:

Question 89

Which practice best supports continuous integration in Azure DevOps by ensuring rapid feedback and preventing broken builds from reaching main branches?

A) Enable gated check-ins with build validation
B) Schedule nightly builds for integration testing
C) Allow direct commits to main without checks
D) Run manual builds before merging changes

Answer: A)

Explanation:

Scheduling nightly builds for integration testing provides delayed feedback. While useful for detecting issues, it allows broken code to remain in the repository for hours, slowing down development and increasing the cost of fixing defects. Nightly builds are reactive rather than proactive.

Allowing direct commits to main without checks is risky. It bypasses validation and increases the likelihood of introducing defects into the main branch. This practice undermines continuous integration principles and leads to unstable builds.

Running manual builds before merging changes relies on developer discipline. It is inconsistent and error-prone, as developers may forget or skip builds. Manual processes do not scale and cannot guarantee that all changes are validated.

Enabling gated check-ins with build validation is the correct practice. Gated check-ins ensure that changes are built and tested before being merged into the main branch. If validation fails, the changes are rejected, preventing broken builds from reaching the repository. This provides rapid feedback, enforces quality, and maintains stability. Build validation can include unit tests, linting, security scans, and other checks, ensuring comprehensive validation.

The reasoning for selecting gated check-ins is that continuous integration requires automated, consistent validation of every change. Gated check-ins enforce discipline, provide immediate feedback, and prevent defects from propagating. Other practices either delay feedback or rely on manual effort, making gated check-ins the best choice.

Question 90

Which approach best enables infrastructure as code in Azure DevOps pipelines while ensuring consistency and repeatability across environments?

Answer: A)

Explanation:

Manually configuring resources in the Azure portal is a method often used by individuals and teams who are initially exploring cloud services or managing small-scale environments. This approach involves logging into the Azure portal, selecting the required resource type, filling in configuration details, and deploying the resource directly through the graphical user interface. While this method may seem straightforward and accessible, it has significant drawbacks that make it unsuitable for production-grade environments or large-scale operations. Manual configuration is inherently error-prone because it relies heavily on human input. A simple oversight, such as misentering a configuration value, can lead to misconfigured resources, security vulnerabilities, or performance issues. Additionally, manual processes introduce inconsistency because the same configuration steps may be interpreted or executed differently by different team members, resulting in environments that drift from one another over time. This inconsistency makes troubleshooting difficult, complicates collaboration, and increases the risk of operational failures. Scaling manual processes is also challenging. In small environments, it may be possible to manage resources by hand, but as the number of resources and dependencies grows, manual provisioning becomes increasingly time-consuming, inefficient, and difficult to maintain. Manual methods cannot also enforce repeatability. Once a resource is configured, there is no guarantee that it can be recreated in the same state in another environment without following the same manual steps, which again introduces the possibility of error.

Using command-line interface (CLI) commands to apply configuration changes provides a level of automation beyond the manual portal approach. CLI tools allow scripts to be written and executed, automating repetitive tasks and reducing human intervention. These commands can be executed locally or as part of automated pipelines to speed up deployment and configuration processes. However, CLI-based approaches still have limitations. CLI commands are imperative, meaning they specify the exact steps to act rather than defining the desired end state of the infrastructure. Because they focus on instructions rather than outcomes, maintaining consistency across environments can become challenging. Any deviation in script execution, missing parameters, or differences in environment settings can result in inconsistencies, resource drift, or failures in deployment. While CLI automation reduces manual effort, it does not fully address the need for consistent, repeatable, and version-controlled infrastructure, which is essential in modern DevOps practices.

Documenting infrastructure setup in a wiki or similar knowledge repository can guide teams on how to create and configure resources. Documentation often includes step-by-step instructions, screenshots, and explanations of configurations. While this approach helps onboard new team members and share institutional knowledge, it does not enforce consistency or automation. Developers and operators must manually follow the instructions in the documentation, which reintroduces the risk of human error. Documentation alone cannot guarantee that resources are configured identically across multiple environments or that updates are applied consistently when configurations change. Additionally, documentation does not integrate easily with automated testing, auditing, or version control, limiting its usefulness in environments that require high reliability, reproducibility, and governance.

Using Azure Resource Manager (ARM) templates in pipelines provides the most effective approach for managing cloud infrastructure in a consistent, automated, and repeatable manner. ARM templates are declarative, meaning they define the desired state of resources rather than specifying the exact steps to reach that state. These templates describe resources, configurations, dependencies, and parameters, allowing the infrastructure to be deployed consistently across multiple environments. By integrating ARM templates into CI/CD pipelines, organizations can automatically deploy resources, ensuring that every environment matches the intended configuration. Pipelines also allow for parameterization, enabling templates to be reused across development, testing, staging, and production environments while adapting to environment-specific needs. This approach supports version control because templates are treated as code, enabling auditing, tracking changes, and rolling back to previous versions when necessary. Automation through ARM templates also improves scalability, reduces operational errors, and enhances collaboration between development and operations teams, aligning with DevOps principles and modern cloud management practices.

The reasoning for selecting ARM templates is rooted in the principles of infrastructure as code, which requires declarative, automated, and version-controlled definitions of infrastructure. By using ARM templates in pipelines, organizations achieve consistency, repeatability, and scalability, while minimizing the risk of errors associated with manual or CLI-based configuration. Manual portal configurations, CLI commands, and wiki-based documentation all have significant limitations in terms of reliability, automation, and enforceability. ARM templates provide the best solution for managing Azure resources efficiently and reliably, ensuring that infrastructure is deployed accurately, consistently, and in alignment with enterprise and DevOps practices. This method is the optimal choice for teams seeking to manage complex, scalable, and repeatable cloud environments while reducing human error and operational drift.

The Open Group OGEA-103 TOGAF Enterprise Architecture Combined Part 1 and Part 2 Exam Dumps and Practice Test Questions Set 6 Q76-90

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