The Open Group OGEA-103 TOGAF Enterprise Architecture Combined Part 1 and Part 2 Exam Dumps and Practice Test Questions Set 7 Q91-105

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Question 91

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 improves code quality by enforcing peer reviews before merges. This ensures that multiple developers examine changes, reducing the likelihood of introducing defects. However, reviews alone do not guarantee that builds and tests are executed. Without automated validation, reviewers may miss issues that only surface during compilation or testing.

YAML pipelines triggered on the main branch only provide post‑merge checks. This means that validation occurs after changes are already integrated, which is too late to prevent broken builds from entering the repository. While useful for detecting issues, this approach is reactive rather than proactive.

Signed commits via Git hooks improve authenticity by verifying the identity of commit authors. This strengthens security and accountability but does not enforce builds or tests. Signed commits ensure trust in authorship but do not validate code quality or functionality.

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 92

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 93

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 94

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 95

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 96

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

Answer: A)

Explanation:

Question 97

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

Answer: B)

Explanation:

Question 98

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

Answer: B)

Explanation:

Question 99

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

Answer: A)

Explanation:

Question 100

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

Answer: B)

Explanation:

Branch protection with required reviewers is an important practice for improving the quality of code before it is merged into the main branch. By enforcing peer review, this approach ensures that multiple developers examine the proposed changes, providing a level of scrutiny that can catch logical errors, inconsistencies, or deviations from coding standards. Peer review encourages collaboration and knowledge sharing, as reviewers often provide suggestions, raise questions, or propose alternative approaches to improve the implementation. It helps maintain coding standards across a team and ensures that multiple perspectives are applied to each change, reducing the likelihood of introducing defects or suboptimal solutions. In addition, branch protection can prevent direct pushes to critical branches, thereby controlling who can make changes and maintaining a more structured development workflow. However, while required reviews are useful, they do not guarantee that code builds correctly or that all tests pass. Human reviewers, even with expertise and diligence, may not identify every issue, particularly those that only surface during compilation, runtime execution, or automated testing. This means that relying solely on peer review leaves gaps in the validation process, and defective code could still reach the main branch if it passes review but fails to function correctly in an actual build or test environment.

YAML pipelines triggered only on the main branch introduce another challenge because they perform checks after the code has already been merged. Post-merge pipelines can identify issues and failures, but they are inherently reactive. By the time a problem is detected, the faulty code is already in the main branch, which can break builds for other developers, delay downstream work, or require urgent fixes. While post-merge pipelines are useful for catching regressions or validating the main branch continuously, they do not prevent errors from entering the shared codebase. This reactive nature limits the effectiveness of continuous integration principles, which emphasize proactive detection and prevention of defects at the earliest possible stage. Waiting until after merging to validate changes increases the risk of instability in the repository, potentially affecting other developers who rely on a functional main branch for their work.

Signed commits, enforced through Git hooks or other mechanisms, provide an additional layer of security and accountability by verifying the identity of commit authors. Signed commits ensure that only verified contributors are adding changes, which helps maintain trust in the source of code changes and strengthens auditing capabilities. This practice is particularly valuable in regulated environments or for projects with multiple contributors, as it makes it easier to trace authorship and enforce accountability. However, signed commits do not automatically ensure that the code builds successfully or passes tests. They only confirm the identity of the committer and the integrity of the commit content, but do not verify the correctness or quality of the implementation. As a result, signed commits alone do not satisfy the requirements of automated validation, which is a critical component of a reliable continuous integration workflow.

Configuring status checks with build validation policies is the most effective solution to enforce automated, consistent validation for every change. Build validation ensures that every pull request triggers an automated process that runs builds, executes unit tests, performs linting, and may include additional checks such as security scans or code coverage analysis. If any part of this validation fails, the pull request cannot be merged into the main branch, preventing broken or defective code from reaching critical environments. Status checks provide immediate feedback to developers, allowing them to address failures promptly before integration. This approach maintains stability in the main branch, enforces quality gates, and guarantees that only validated code progresses through the development lifecycle. It complements human reviews by catching errors that may not be apparent during manual inspection and ensures that automated tests enforce standards consistently across all contributions.

The reasoning for selecting build validation policies is rooted in the principles of continuous integration, which emphasize automated, repeatable, and proactive validation of code changes. By enforcing automated builds and tests as part of the pull request process, teams prevent defects from propagating, reduce integration risk, and ensure that the main branch remains stable and functional at all times. Other practices, such as relying solely on peer reviews, post-merge pipelines, or signed commits, either delay validation or focus on other aspects of development, such as security or code ownership, without guaranteeing automated verification. Build validation policies that directly address the need for automation, consistency, and rapid feedback, ensuring that each change is verified before integration, making them the most effective practice for maintaining code quality, stability, and reliability in a collaborative development environment.

Question 101

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

Answer: B)

Explanation:

Question 102

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 has been one of the traditional approaches that many teams rely upon when setting up cloud environments, and at first glance, it may appear to be a straightforward way to provision resources quickly. Users can log into the portal, click through interfaces, and set up virtual machines, storage accounts, virtual networks, and various other resources according to immediate requirements. While this method provides a sense of direct control and visual confirmation, it introduces significant risks, inefficiencies, and challenges that can impact the reliability, consistency, and scalability of cloud infrastructure. Manual configuration depends entirely on human action, which is inherently prone to error. Even experienced administrators can make mistakes, such as selecting incorrect settings, forgetting critical dependencies, or misconfiguring security rules. In complex environments where multiple interdependent resources must be provisioned in a precise order, a small misstep can have cascading effects, potentially causing failures, downtime, or unexpected behavior in downstream services. In addition, when multiple team members are involved, the risk of inconsistencies across environments increases. For instance, the development environment may be configured slightly differently from the testing or production environment, which can lead to subtle issues that are difficult to detect during validation and testing. This inconsistency undermines repeatability and can cause frustration during deployments and troubleshooting because reproducing the same environment reliably becomes a complicated and error-prone task.

Manual processes also create inefficiencies that significantly hinder scalability. As the organization grows and the number of resources, services, and environments increases, the time and effort required to manually configure each resource multiplies dramatically. What may be manageable for a small test environment quickly becomes unfeasible when scaling across multiple regions, subscriptions, or production clusters. This limitation reduces the ability to respond quickly to business needs, slows down innovation, and prevents organizations from embracing modern agile practices or continuous delivery pipelines effectively. Even with meticulously maintained documentation, the likelihood of human error and inconsistent application of instructions means that manual provisioning cannot guarantee the desired state of resources across different environments. In addition, manual provisioning lacks version control, auditing, and traceability, which are crucial for maintaining compliance, tracking changes, and supporting collaborative teams. When infrastructure is set up manually, it becomes difficult to understand who made specific changes, when those changes occurred, or why they were implemented, which can create governance and accountability challenges.

Applying ad-hoc CLI commands during deployments provides some level of automation, as scripts and commands can perform repetitive tasks more quickly than manual clicks in a portal. Using the CLI allows administrators to execute commands to create, modify, or delete resources programmatically, reducing some of the human effort involved in provisioning. However, CLI-based deployment has inherent limitations that make it unsuitable for reliable, large-scale infrastructure management. CLI commands are imperative, meaning they specify step-by-step instructions for performing tasks rather than defining the desired end state of resources. This requires users to maintain detailed scripts and ensure every step is executed in the correct order. If a script fails midway or an additional change is needed, manually correcting the state or modifying the scripts becomes complex. CLI deployments do not inherently enforce consistency across multiple environments, and unless carefully managed, drift can occur between development, staging, and production environments. Moreover, imperative scripts often lack the built-in idempotency and error handling that declarative approaches provide, making it more difficult to ensure that repeated executions produce the same result reliably.

Documenting infrastructure setup in a shared wiki guides teams, allowing them to reference best practices and step-by-step instructions. While documentation is useful for knowledge sharing and onboarding new team members, it does not enforce consistency or automation. Developers and administrators still have to manually follow the instructions, and there is no guarantee that each person will apply the steps correctly or completely. Documentation is static and can become outdated as the environment evolves, creating the risk of following obsolete instructions that lead to misconfigurations or inconsistent states. It also does not integrate with deployment pipelines or source control, meaning that infrastructure changes cannot be versioned, audited, or rolled back automatically.

Using Azure Resource Manager templates stored in source control addresses the limitations of all these approaches and represents the correct method for implementing infrastructure as code in Azure. ARM templates allow teams to define their infrastructure declaratively, specifying the desired state of resources, their properties, dependencies, and configurations. Instead of instructing Azure on how to create each resource step by step, ARM templates describe what the environment should look like once deployed. This approach ensures consistency, as the same template can be applied to multiple environments with predictable results. Pipelines can automatically deploy ARM templates, enabling repeatable, automated provisioning across development, testing, staging, and production environments. Parameterization allows templates to be customized for different scenarios without modifying the core structure, supporting flexibility and reuse. Integration with source control provides versioning, audit trails, and collaboration capabilities, ensuring that infrastructure changes are tracked, reviewed, and reversible. By using ARM templates, organizations can scale their infrastructure reliably, reduce human error, enforce consistency, and align with modern DevOps principles that emphasize automation, reproducibility, and continuous delivery. Compared to manual configuration, ad-hoc CLI commands, or documentation-based approaches, ARM templates provide a declarative, automated, and maintainable way to manage Azure resources effectively, making them the best choice for modern cloud operations.

Question 103

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

Answer: B)

Explanation:

Question 104

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.

Question 105

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 traditional approach that many teams have used to deploy and manage cloud infrastructure, but it is inherently error-prone and inconsistent. This method relies entirely on human effort, which introduces a high likelihood of misconfigurations, mistakes, and discrepancies between environments. When multiple engineers manually configure similar resources in development, testing, and production environments, subtle differences often emerge. These differences can cause unpredictable behavior, integration issues, or failures in downstream processes. Manual processes also do not scale effectively. As the number of resources and environments grows, the time and effort required to configure each element manually increase exponentially. Each new deployment or update carries the risk of human error, and there is no inherent mechanism to verify that the infrastructure conforms to the intended design. In addition, manual configurations lack repeatability; recreating an environment exactly as it existed previously is challenging, and discrepancies between repeated deployments are common. This makes manual configuration unsuitable for organizations that require rapid, consistent, and reliable infrastructure provisioning across multiple environments.

Applying configuration changes directly using command-line interface commands introduces a level of automation, but it still has significant limitations. CLI commands are imperative in nature, meaning they specify exactly how to perform actions step by step rather than defining the desired end state. While scripting changes with CLI commands can reduce manual effort, it does not provide the declarative approach needed for fully automated, consistent infrastructure deployment. Imperative scripts must be maintained carefully, and errors in scripts can propagate across multiple deployments. They do not inherently prevent configuration drift, and ensuring consistency across development, staging, and production environments requires additional validation steps. As the infrastructure grows in complexity, maintaining a large collection of imperative scripts becomes cumbersome, error-prone, and difficult to audit. Teams also struggle to track changes effectively or roll back misconfigurations reliably because the scripts describe actions to perform rather than the state of the infrastructure itself.

Documenting infrastructure setup in a wiki or shared documentation space provides guidance and knowledge sharing, but it does not enforce consistency or ensure correct execution. Developers or operations engineers must manually follow the documented steps, which introduces the same human error risks present in manual portal configuration. Documentation can be outdated, incomplete, or misinterpreted, resulting in environments that diverge from the intended configuration. While documentation is valuable for onboarding new team members, knowledge transfer, and understanding the architecture, it does not automate the deployment process. It cannot guarantee that the infrastructure is provisioned correctly or that all dependencies are managed consistently. Documentation cannot also integrate with CI/CD pipelines or version control, so infrastructure changes nare ot automatically tracked or auditable through the development lifecycle.

Using Azure Resource Manager templates stored in source control provides the most effective approach to managing infrastructure in a modern DevOps workflow. ARM templates are declarative, meaning they define the desired state of resources rather than the steps to create them. By specifying what the infrastructure should look like, ARM templates allow automated processes to provision resources consistently across multiple environments. When deployed via CI/CD pipelines, ARM templates ensure repeatability, reducing human error and eliminating the inconsistencies that arise from manual configuration or imperative scripting. Templates can be parameterized to accommodate different scenarios, environments, or configuration options, allowing teams to maintain a single source of truth while supporting flexible deployments. Storing templates in version control provides additional benefits, including tracking changes over time, auditing modifications, and enabling collaboration among team members. It also allows rollbacks to previous infrastructure states if a deployment introduces an issue, improving resilience and operational reliability. Integrating ARM templates into pipelines automates the deployment process, aligns with continuous delivery practices, and supports scalable infrastructure management.

The reasoning for selecting ARM templates as the preferred approach is rooted in the principles of infrastructure as code, which emphasize automation, consistency, repeatability, and scalability. ARM templates provide a declarative, versioned, and auditable method to define infrastructure, reducing risk and supporting operational best practices. Manual configuration in the portal is slow, error-prone, and inconsistent. Ad-hoc CLI scripts, while providing some automation, remain imperative, difficult to maintain, and do not prevent drift. Documentation in a wiki serves as a reference but cannot enforce execution or ensure consistency. ARM templates combine the benefits of automation, declarative definitions, version control, and integration with pipelines, making them the best choice for modern infrastructure management. They allow teams to manage resources reliably, scale deployments efficiently, and maintain consistent environments across development, testing, and production. This approach aligns with DevOps principles and enables organizations to deliver cloud infrastructure in a repeatable, predictable, and auditable manner, while reducing operational overhead and mitigating risk across all environments. By adopting ARM templates, teams achieve greater reliability, maintainability, and confidence in their infrastructure deployments, ensuring that resources meet business requirements consistently and efficiently.

The Open Group OGEA-103 TOGAF Enterprise Architecture Combined Part 1 and Part 2 Exam Dumps and Practice Test Questions Set 7 Q91-105

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