The Open Group OGEA-103 TOGAF Enterprise Architecture Combined Part 1 and Part 2 Exam Dumps and Practice Test Questions Set 8 Q106-120

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Question 106

Which solution best enforces automated compliance in Azure DevOps pipelines by preventing non‑compliant resources from being deployed?

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

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.

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 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.

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 107

Which practice best supports continuous deployment 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 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.

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 deployment 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 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 108

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

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 109

Which solution best enforces quality gates in Azure DevOps pipelines by ensuring builds and tests run before code merges?

A) Enable branch protection with required reviewers only
B) Configure status checks with build validation policies
C) Use YAML pipelines triggered on the main branch only
D) Require signed commits via Git hooks

Answer: B)

Explanation:

Branch protection with required reviewers ensures peer review, which is valuable for catching logical errors and enforcing coding standards. However, reviewers cannot always detect issues that only surface during compilation or automated testing. Without automated validation, code may still break builds after merging.

YAML pipelines triggered on the main branch only provide validation after changes are merged. This is reactive, meaning broken builds can enter the repository before being detected. While useful for monitoring, this approach does not prevent issues from reaching the main branch.

Signed commits via Git hooks improve authenticity by verifying commit authorship. This strengthens accountability but does not enforce builds or tests. Signed commits ensure trust in identity but not in code quality.

Configuring status checks with build validation policies is the correct solution. Build validation ensures that every pull request triggers automated builds and tests. If validation fails, the pull request cannot be merged. This enforces quality gates by requiring successful builds and passing tests before integration. Status checks provide immediate feedback, maintain stability, and prevent broken code from reaching the main branch.

The reasoning for selecting build validation policies is that continuous integration requires automated, consistent validation of every change. Status checks enforce discipline, provide rapid feedback, and prevent defects from propagating. Other methods either delay validation or focus on security and reviews without automation, making build validation the best choice.

Question 110

Which practice best supports continuous delivery 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 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.

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 111

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

Answer: A)

Explanation:

Question 112

Which solution best enforces quality gates in Azure DevOps pipelines by ensuring builds and tests run before code merges?

Answer: B)

Explanation:

Question 113

Which practice best supports continuous delivery 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 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.

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.

Question 114

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

Answer: A)

Explanation:

Question 115

Which solution best enforces automated compliance in Azure DevOps pipelines by preventing non‑compliant resources from being deployed?

Answer: B)

Explanation:

Azure Monitor alerts play an important role in providing observability into the health and performance of resources deployed in an Azure environment. By tracking metrics and analyzing logs, these alerts enable teams to detect anomalies, unusual patterns, or policy violations after resources have been created and are operational. For example, an alert can notify administrators when CPU utilization exceeds a certain threshold, when a storage account lacks proper encryption, or when a virtual machine is deployed outside approved regions. While this monitoring capability is valuable for maintaining visibility and responding to incidents, it is inherently reactive. Issues are identified only after resources exist and potentially impact workloads or violate compliance requirements. In other words, Azure Monitor alerts cannot prevent the creation of non-compliant resources; they can only flag problems after the fact. Organizations aiming for proactive governance need tools and processes that prevent policy violations from occurring in the first place, rather than relying on detection and remediation post-deployment. Relying solely on monitoring leaves a window of risk where resources may operate in violation of organizational standards, which can lead to security gaps, regulatory noncompliance, or operational inefficiencies.

Manual reviews before each release are another approach that some organizations consider for enforcing compliance. In this method, a team member or a compliance officer examines the planned changes or resource configurations prior to deployment. The goal is to verify that all resources meet predefined organizational standards, such as naming conventions, tagging requirements, permitted locations, and security configurations. While this approach can catch some violations, it has significant limitations. Human reviews are slow and cannot scale effectively across large or distributed teams. In fast-paced DevOps environments, where deployments occur frequently and often automatically through pipelines, manual checks become a bottleneck. Furthermore, relying on human judgment introduces inconsistencies and the risk of oversight. Even a well-trained reviewer may miss subtle misconfigurations or interpret standards differently from the intended policy. As a result, manual reviews provide some level of quality control but do not guarantee that all deployments comply with organizational rules, and they are not suitable for automated, large-scale cloud environments.

Documenting compliance standards in a shared wiki or internal knowledge base offers another layer of support for governance. Documentation is useful for raising awareness, providing reference material, and guiding developers on best practices and organizational rules. It ensures that team members understand what constitutes compliant infrastructure, including resource types, tagging requirements, naming conventions, and security policies. However, documentation by itself does not enforce compliance. Developers may overlook guidance, misinterpret instructions, or apply standards inconsistently. When multiple teams operate across different projects, maintaining consistent adherence to documented standards becomes increasingly difficult. Additionally, as cloud environments evolve rapidly, documentation can quickly become outdated if not continuously maintained. While documentation is essential for knowledge sharing and education, it cannot actively prevent non-compliant resources from being created, leaving organizations vulnerable to policy violations that could otherwise have been avoided.

Applying Azure Policy integrated with pipeline workflows offers a proactive and automated solution to enforce compliance. Azure Policy allows organizations to define rules that govern the creation and configuration of resources. These policies can audit existing resources, deny non-compliant resource creation, or even automatically remediate certain issues. For instance, a policy might restrict virtual machines to specific regions, enforce that all storage accounts have encryption enabled, require the presence of defined tags, or ensure that resource names follow a standardized convention. By integrating these policies with CI/CD pipelines, organizations can ensure that compliance checks occur automatically during the deployment process. This means that non-compliant resources are prevented from being deployed, rather than detected only after they exist. The integration ensures that every deployment follows the defined governance standards, eliminating human error and inconsistency. Policies can also be versioned and managed centrally, providing a single source of truth for organizational compliance rules. This approach scales across multiple teams and environments, making it suitable for large organizations and modern DevOps practices.

The reasoning for selecting Azure Policy integrated with pipelines is rooted in the need for proactive, automated, and consistent compliance enforcement. While monitoring through Azure Monitor alerts provides visibility, it does not prevent violations; it only identifies issues after deployment. Manual reviews may catch some violations, but they are slow, inconsistent, and difficult to scale. Documentation helps educate and guide developers, but it cannot enforce rules or prevent non-compliant resource creation. In contrast, Azure Policy enforces compliance rules automatically, ensuring that only resources that meet organizational standards are deployed. Integration with pipelines makes the process seamless, aligning compliance enforcement with DevOps workflows and minimizing human intervention. This approach reduces operational risk, improves governance, and supports regulatory requirements by systematically preventing non-compliant deployments. Organizations adopting Azure Policy in this manner can achieve both scalability and consistency, ensuring that infrastructure adheres to corporate standards across all environments, from development to production.

Using Azure Policy with pipeline integration not only prevents violations but also provides auditability and traceability. Administrators can review which policies are applied, which resources were evaluated, and what actions were taken for any non-compliant resource. This capability is valuable for internal audits, regulatory compliance reporting, and maintaining accountability within teams. Moreover, policies can be updated and deployed centrally, ensuring that new standards or changes to compliance requirements are consistently applied across all environments. This eliminates the need to manually update multiple documentation sources or rely on ad-hoc reviews. By embedding compliance enforcement into the deployment process, organizations create a system where adherence to standards is guaranteed by design, rather than relying on post-deployment detection or human oversight.

Question 116

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

Answer: B)

Explanation:

Question 117

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

Answer: A)

Explanation:

Infrastructure management in modern cloud environments requires accuracy, repeatability, and the ability to adapt quickly to changing business demands. When organizations rely on manual processes to configure resources, they introduce significant operational risks. Manually creating resources in the Azure portal forces administrators to click through multiple screens, fill out numerous fields, and make subjective decisions at each step. These human-driven processes inevitably lead to inconsistencies because people often interpret instructions differently or make accidental mistakes. Even a small oversight, such as selecting the wrong SKU or region, can lead to severe consequences, including performance issues, unexpected costs, or failed deployments. Manual work also becomes extremely difficult to scale when managing multiple teams, multiple regions, and multiple environments such as development, test, staging, and production. The lack of automation increases the likelihood of drift, where environments gradually become different from one another, making debugging and troubleshooting significantly harder.

Ad-hoc CLI commands appear more efficient at first because they provide scriptable control over Azure resources. However, these commands follow an imperative model, meaning they describe the specific steps needed to create or modify resources. This imperative nature brings its own challenges. Since the infrastructure configuration is scattered across scripts and commands executed at different times, it becomes difficult to reconstruct the full state of an environment. Teams may run slightly different versions of scripts, miss a command, or update resources inconsistently. This approach does enable some level of automation, but it still lacks the predictability required for large-scale cloud management. The absence of a complete declarative definition means that the desired state of the infrastructure is not captured in a single source of truth. This makes it hard to enforce uniformity across environments or validate that the deployed resources truly match architectural expectations. It also increases the risk of configuration drift because imperative commands focus more on the actions taken than the final desired state of the system.

Documenting infrastructure setup in a shared wiki can help teams understand how environments should be configured, but documentation alone cannot enforce correct implementation. People reading the documentation must interpret and follow the instructions precisely. Even when the documentation is clear and accurate, human error remains a major factor. Instructions may be skipped, misunderstood, or outdated. Furthermore, documentation tends to lag behind actual implementation because teams often update systems faster than they update written references. This misalignment leads to confusion and inconsistencies, especially when new team members rely on older documentation. While documentation is valuable for communication and training, it cannot provide the automated consistency that modern cloud architectures require. There is no validation mechanism, no automated enforcement, and no way to ensure that instructions are being followed uniformly across all environments.

In contrast, Azure Resource Manager templates provide a declarative, automated, and version-controlled way to manage infrastructure. ARM templates describe the desired state of Azure resources in a structured and repeatable format. Instead of detailing the exact steps required to build an environment, these templates declare what the final configuration should look like. This declarative approach ensures that the infrastructure is always provisioned according to a consistent and predictable blueprint. If a resource already exists in the desired state, no changes occur. If differences exist, the template brings the environment back into alignment. This automated enforcement of the desired state helps eliminate configuration drift, ensures repeatability, and strengthens stability across deployments.

Storing ARM templates in source control adds another critical advantage. Version control systems track changes, support branching strategies, and provide visibility into the evolution of infrastructure definitions. Every change becomes auditable and reviewable, enabling teams to implement governance practices such as pull requests, peer reviews, and automated validation checks. This aligns infrastructure management with the same disciplined practices applied to application code, creating a unified DevOps workflow. Historical versions of templates can also be restored at any time, supporting rapid rollback during incidents or failed deployments.

ARM templates fully integrate with CI/CD pipelines, enabling automated deployments across all environments. Pipelines can validate templates, enforce policies, deploy changes, and verify successful provisioning without human intervention. Parameter files allow teams to customize deployments for different environments while maintaining a common template structure. This eliminates duplication, reduces maintenance overhead, and increases flexibility. Teams can define variations such as resource names, sizes, regions, or scaling configurations through parameters while preserving the consistent underlying architecture expressed in the template.

The structured and automated nature of ARM templates ensures that infrastructure deployments are predictable, scalable, and aligned with organizational standards. They reduce the risk of misconfigurations, eliminate manual errors, enhance collaboration, and support long-term maintainability. By centralizing infrastructure definitions in a single authoritative location and integrating them with automated pipelines, teams gain tighter control over their cloud environments. This makes ARM templates the most reliable and effective method for managing Azure infrastructure compared to manual portal work, ad-hoc CLI commands, or wiki-based instructions.

Question 118

Which solution best enforces automated compliance in Azure DevOps pipelines by preventing non‑compliant resources from being deployed?

Answer: B)

Explanation:

Question 119

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

Answer: B)

Explanation:

Question 120

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 common approach for teams just starting with cloud deployments or for performing quick experiments. However, this method is highly error-prone and inconsistent because it relies entirely on human input. Each step in the portal requires careful attention, and even small mistakes in selecting options, configuring settings, or linking resources can result in misconfigurations. These misconfigurations may not always be immediately obvious, and they can cause failures in dependent systems or reduce overall reliability. Furthermore, manual portal configurations are difficult to reproduce exactly across multiple environments, such as development, testing, and production. This lack of repeatability is a major challenge, particularly in enterprise environments where multiple teams are working on different projects. Manual processes also do not scale well; as the number of resources grows, keeping track of all the settings becomes overwhelming. Each time a new environment is needed, administrators must manually configure everything again, which consumes time and increases the risk of inconsistencies. Over time, environments configured manually tend to drift, meaning that the actual state of deployed resources diverges from the intended state, creating operational challenges and increasing maintenance costs.

Applying ad-hoc CLI commands during deployments is another method teams often use to automate some aspects of infrastructure management. Command-line tools allow for faster deployment compared to manual portal configuration and provide a level of automation that reduces repetitive effort. However, CLI commands are inherently imperative. This means they describe exactly how to perform a set of actions rather than defining what the desired final state should be. Because of this, maintaining consistency across multiple deployments can be challenging. For example, if a team executes a sequence of CLI commands in development and attempts to repeat them in production, slight differences in command execution or timing can produce different results. Additionally, imperative approaches like CLI commands make it difficult to manage drift between environments. There is no guarantee that a repeated deployment will create identical infrastructure unless every command is executed precisely the same way each time. Over time, this approach increases the risk of errors, reduces reliability, and makes auditing and versioning of infrastructure difficult. While CLI commands offer some automation, they lack the declarative structure necessary for fully automated, reproducible, and consistent deployments.

Documenting infrastructure setup in a shared wiki or knowledge base is another method teams often adopt. Documentation guides how resources should be configured, serving as a reference for team members who need to deploy or maintain infrastructure. While documentation is valuable for knowledge sharing and onboarding new team members, it does not enforce consistency. Developers must manually follow the instructions in the wiki, which introduces opportunities for human error. If a step is misinterpreted or skipped, the resulting environment may differ from the intended configuration. Moreover, documentation can become outdated as infrastructure evolves. Maintaining accurate, up-to-date documentation requires additional effort, and if the documentation is not synchronized with actual deployments, it loses its effectiveness. Documentation alone does not provide automation; it only describes the process. Therefore, while documentation is useful for knowledge transfer and procedural guidance, it does not prevent misconfigurations, enforce consistency, or provide a mechanism for auditing or version control of the infrastructure.

Using Azure Resource Manager (ARM) templates stored in source control provides a robust solution that addresses the limitations of manual, CLI-based, and documentation-driven approaches. ARM templates allow infrastructure to be defined declaratively. This means that instead of specifying step-by-step commands, the template defines the desired state of resources, including configurations, dependencies, and relationships. When deployed, the Azure platform ensures that the actual state of resources matches the declared state in the template. This approach provides strong guarantees of consistency and repeatability, ensuring that development, testing, and production environments can be deployed in an identical manner. By storing ARM templates in source control, teams gain versioning, traceability, and auditing capabilities. Changes to the infrastructure are tracked over time, and previous versions can be reviewed or rolled back if necessary. This supports collaborative development practices, where multiple team members can contribute to infrastructure definitions while maintaining control over changes. Additionally, ARM templates support parameterization, allowing customization of deployments for different scenarios without modifying the core template. For example, different sizes of virtual machines, regions, or storage accounts can be specified using parameters while keeping the overall structure and logic consistent. Integration with pipelines further enhances the value of ARM templates. Continuous integration and continuous deployment (CI/CD) pipelines can automatically deploy templates to various environments, enforcing consistency and reducing manual intervention. The combination of declarative definitions, automation, source control integration, and pipeline deployment aligns perfectly with DevOps principles, emphasizing repeatability, traceability, and scalability.

Selecting ARM templates as the method for defining and deploying infrastructure reflects the core principles of infrastructure as code. Infrastructure as code requires that resources be defined declaratively, automated, and versioned so that deployments are predictable, consistent, and repeatable. By using ARM templates, organizations eliminate much of the risk associated with human error in manual processes, the inconsistency of ad-hoc CLI commands, and the potential misalignment of documentation-based approaches. The templates provide a single source of truth for infrastructure definitions, enabling teams to confidently deploy resources with minimal intervention. Automation through pipelines ensures that deployments can scale across environments and that any changes are systematically reviewed, tested, and applied. Overall, ARM templates provide a framework that enforces consistency, enables auditability, and supports long-term operational efficiency, making them the optimal choice for teams practicing modern DevOps and managing cloud infrastructure at scale.

The reasoning behind prioritizing ARM templates over other methods is that they uniquely combine declarative definitions, automation, and integration with modern development workflows. While manual configuration, CLI commands, and documentation each offer certain benefits, they fail to provide the same level of repeatability, enforcement, and scalability. ARM templates mitigate the risks of human error, environment drift, and inconsistent deployments while enabling parameterization, source control, and automated pipeline integration. They embody the principles of infrastructure as code, supporting a disciplined, efficient, and reliable approach to cloud resource management that scales with organizational needs.

This extended explanation now provides a comprehensive understanding of why ARM templates are preferred, with a detailed discussion of the shortcomings of other approaches and the advantages ARM templates bring to automated, consistent, and maintainable infrastructure deployments.

The Open Group OGEA-103 TOGAF Enterprise Architecture Combined Part 1 and Part 2 Exam Dumps and Practice Test Questions Set 8 Q106-120

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