Microsoft AZ-400 Designing and Implementing Microsoft DevOps Solutions Exam Dumps and Practice Test Questions Set 4 Q61-80

Visit here for our full Microsoft AZ-400 exam dumps and practice test questions.

Question 61:

 In Azure DevOps, you want to ensure that a pipeline only keeps artifacts for critical releases indefinitely, while other builds are deleted automatically after thirty days. Which feature should you use?

A. Manual artifact marking
B. Separate pipelines for release and non-release builds
C. Retention rules in Azure Pipelines
D. Storing artifacts in external storage

Answer: C. Retention rules in Azure Pipelines

Explanation:

 Azure Pipelines retention rules allow automated management of build and release artifacts based on tags, branches, or pipeline patterns. You can retain important builds, such as releases, indefinitely while automatically cleaning up less critical builds. Manual marking is error-prone, creating separate pipelines adds unnecessary complexity, and storing artifacts externally reduces integration with Azure DevOps pipelines.

Azure Pipelines retention rules provide a robust and automated mechanism for managing build and release artifacts, ensuring that storage is optimized while critical artifacts are preserved. By configuring retention policies, organizations can specify conditions based on tags, branches, or pipeline patterns to determine how long artifacts should be kept. For example, builds marked as releases or tagged as critical can be retained indefinitely, ensuring they remain available for auditing, troubleshooting, or rollback purposes. Meanwhile, less important builds can be automatically cleaned up after a specified period, such as thirty days, freeing up storage space and reducing operational overhead.

Retention rules integrate directly into the Azure DevOps pipeline, providing a first-class method to manage artifacts without introducing additional manual steps. Manual artifact marking is error-prone, as it relies on human judgment and may result in important artifacts being deleted or unnecessary artifacts being retained. Creating separate pipelines to handle different retention requirements increases complexity and makes maintenance more difficult, especially as the number of projects and teams grows. Similarly, storing artifacts in external storage systems may remove them from the native pipeline context, making it harder to manage dependencies, enforce policies, and track artifact usage across builds.

By implementing retention rules, teams can automate cleanup, maintain regulatory compliance, and ensure that important artifacts are consistently available when needed. This capability supports DevOps best practices for continuous integration and delivery by reducing manual intervention, minimizing storage costs, and providing predictable artifact lifecycle management. Retention rules are a key feature for organizations that need to balance storage efficiency with reliability and traceability in their pipeline processes.

Question 62: 

You need to manage large binary assets in a Git repository without bloating repository size. Which strategy is recommended?

A. Git submodules
B. Sparse checkout
C. Git Large File Storage (LFS)
D. Shallow clone

Answer: C. Git Large File Storage (LFS)

Explanation:

 Git LFS stores large files outside the main Git repository and keeps lightweight pointers in the repository. This approach reduces repository size and improves performance. Submodules split repositories but do not manage large binaries, sparse checkout limits files checked out locally but does not reduce history size, and shallow clones reduce commit history but do not address large files.

Git Large File Storage (Git LFS) is a specialized extension for Git designed to efficiently manage large files and binary assets within a repository without negatively impacting performance or repository size. In traditional Git repositories, every version of a file is stored in the repository’s history. Large binaries such as images, videos, datasets, or compiled assets can quickly bloat the repository, making cloning, fetching, and pushing operations slow and cumbersome. Git LFS solves this problem by storing the actual content of large files outside the main Git repository while maintaining lightweight pointer files in the repository itself. This approach allows developers to work with large assets seamlessly without bloating the repository history, ensuring faster operations and better performance.

Other strategies, such as using Git submodules, help organize repositories into smaller components but do not address the storage or management of large files. Sparse checkout allows developers to check out only a subset of files to reduce the local working set, but it does not reduce the repository’s overall history size. Shallow clones limit the depth of commit history retrieved during a clone operation but do not provide any mechanism for managing large assets.

Git LFS is explicitly mentioned in the AZ-400 skills outline as a recommended approach for managing large files in source control. By adopting Git LFS, teams can maintain a lightweight repository, streamline DevOps workflows, reduce network and storage overhead, and ensure that continuous integration and deployment pipelines perform efficiently, even when handling repositories with large assets. It is a critical strategy for maintaining performance, scalability, and reliability in modern DevOps environments.

Question 63: 

Your application uses a database that must remain online during schema updates. Which approach aligns with minimal downtime principles?

A. Dropping and recreating the database
B. Rolling deployment with backward-compatible schema changes
C. Manual updates outside the pipeline
D. Skipping schema changes

Answer: B. Rolling deployment with backward-compatible schema changes

Explanation: 

Rolling deployments with backward-compatible schema changes allow incremental updates while keeping the system online. Dropping and recreating the database introduces downtime and risk of data loss. Manual updates are error-prone, and skipping schema changes may break the application. AZ-400 emphasizes strategies to minimize downtime during database tasks in DevOps pipelines.

Rolling deployments with backward-compatible schema changes are a best practice for updating databases in production environments while ensuring minimal downtime and continuous application availability. This strategy involves applying schema changes incrementally so that existing functionality continues to operate correctly throughout the update process. By designing updates to be backward compatible, new features or modifications can coexist with existing application code, allowing the system to remain online and fully functional. This approach reduces the risk of service interruptions and ensures that end users experience seamless application behavior during updates.

In contrast, dropping and recreating a database is highly disruptive. This approach introduces significant downtime, potential data loss, and the need for extensive recovery planning. Manual updates outside of automated pipelines are also risky, as they are prone to human error, inconsistencies, and missing critical steps, which can lead to application failures or corrupted data. Skipping necessary schema changes altogether can result in application crashes or malfunctioning features due to mismatched expectations between the application code and database structure.

AZ-400 emphasizes implementing strategies to minimize downtime during database tasks in DevOps pipelines. Integrating rolling, backward-compatible updates into automated pipelines ensures that database changes are applied safely, consistently, and audibly, supporting high availability and reliability. This approach aligns with DevOps principles by combining automation, continuous integration, and risk mitigation, enabling organizations to deploy database changes in production with confidence and minimal operational impact.

Question 64: 

You want to track the mean time to resolution (MTTR) for incidents and automatically notify your team in Microsoft Teams. Which combination of Azure DevOps features should you use?

A. Work items + dashboard queries + Teams integration
B. Manual tracking + email notifications
C. Spreadsheets + manual updates
D. Release approvals

Answer: A. Work items + dashboard queries + Teams integration

Explanation:

 Using Azure Boards work items to represent incidents, combined with queries that calculate elapsed time, allows automated MTTR tracking. Dashboard widgets can visualize the data over time, and Teams integration ensures notifications are sent automatically. Manual tracking, spreadsheets, or release approvals do not provide automated, real-time visibility.

Tracking mean time to resolution (MTTR) is essential for understanding how quickly incidents are addressed and resolved in a DevOps environment. Azure Boards provides a structured way to represent incidents using work items, allowing each issue to be tracked from creation to closure. By configuring queries that calculate the elapsed time between states such as “active” and “closed,” teams can automatically measure MTTR for each incident. This provides accurate, repeatable metrics that are essential for monitoring operational performance and identifying areas for process improvement.

To make this information actionable, Azure DevOps dashboards can be configured with widgets that visualize MTTR data over time. These visualizations enable teams to quickly identify trends, detect recurring issues, and measure the effectiveness of incident response processes. Integrating Azure Boards with Microsoft Teams further enhances responsiveness by automatically sending notifications when work items change state, such as when an incident is resolved. This ensures that stakeholders and on-call personnel are immediately aware of resolution events without relying on manual updates.

Manual tracking methods, spreadsheets, or using release approvals for incident management are slow, error-prone, and lack real-time automation. They do not provide continuous visibility into incident response metrics and make it difficult to maintain historical trends. Using Azure Boards combined with dashboards and Teams integration aligns with AZ-400 objectives, providing automated, real-time MTTR tracking, improving operational visibility, and enabling teams to respond to incidents more efficiently while continuously optimizing their DevOps processes.

Question 65: 

Your team wants to minimize merge conflicts and maintain a deployable main branch. Which source control strategy is most suitable?

A. GitFlow
B. Feature + release branch model
C. Trunk-based development
D. Fork + pull request

Answer: C. Trunk-based development

Explanation: 

Trunk-based development emphasizes frequent commits to a shared main branch with very short-lived feature branches. This reduces merge overhead, keeps the main branch deployable at all times, and aligns with continuous integration practices. GitFlow and feature + release branches involve long-lived branches and more complexity. Fork + pull request is more common in open-source projects.

Trunk-based development is a source control strategy that focuses on maintaining a single, shared main branch, often called “trunk” or “main,” and committing changes to it frequently. Developers create very short-lived feature branches or even commit directly to the main branch, which reduces the overhead associated with merging long-lived branches and minimizes the risk of integration conflicts. By keeping the main branch deployable at all times, teams can continuously integrate code changes, run automated tests, and deploy updates with confidence, supporting the core principles of continuous integration and continuous delivery.

This approach encourages smaller, incremental changes, which are easier to review, test, and deploy compared to larger, long-lived branches. Frequent commits allow teams to detect integration issues early, prevent divergence between branches, and maintain a stable and releasable codebase. In contrast, strategies like GitFlow involve long-lived develop, release, and hotfix branches, which introduce more complexity and require careful coordination of merges. Similarly, feature + release branch models can create extended branch lifecycles, increasing the likelihood of conflicts and integration delays. Fork + pull request workflows are commonly used in open-source projects where contributors are external, and the workflow prioritizes code review over rapid integration.

AZ-400 emphasizes understanding trunk-based development as part of source control strategies because it supports faster delivery cycles, reduces merge conflicts, and improves overall software quality. By adopting trunk-based development, organizations can implement automated CI/CD pipelines effectively, maintain a high level of code stability, and enable teams to deliver value to users more reliably and efficiently.

Question 66: 

You need to monitor CPU, memory, and node health for your AKS cluster while tracking distributed application metrics and tracing requests across services. Which combination should you use?

A. VM Insights + Log Analytics
B. Application Insights + VM Insights
C. Container Insights + Application Insights
D. Log Analytics alone

Answer: C. Container Insights + Application Insights

Explanation:

 Container Insights provides infrastructure-level metrics for containers, including CPU, memory, and node health. Application Insights provides application-level telemetry and distributed tracing. VM Insights is for VMs, and Log Analytics alone does not provide actionable monitoring. Combining Container and Application Insights gives full visibility into both infrastructure and applications.

Monitoring containerized workloads in Azure Kubernetes Service (AKS) requires visibility into both infrastructure and application performance to ensure reliability, performance, and efficient troubleshooting. Azure provides two complementary tools for this purpose: Container Insights and Application Insights. Container Insights collects infrastructure-level metrics for containers, including CPU usage, memory consumption, node health, pod status, and other critical system-level telemetry. This allows operations teams to monitor the health of the cluster, detect resource bottlenecks, and identify failing nodes or pods that could impact application availability.

Application Insights, on the other hand, focuses on application-level telemetry. It provides detailed metrics about application performance, request rates, response times, exceptions, and custom events. Additionally, Application Insights supports distributed tracing, enabling teams to track requests as they flow through multiple microservices. This is particularly important for identifying performance bottlenecks or failures in complex, distributed architectures common in microservices deployments.

VM Insights monitors virtual machines but is not designed for containerized workloads, while Log Analytics alone collects raw data but does not provide actionable, pre-aggregated monitoring or distributed tracing. By combining Container Insights for infrastructure telemetry with Application Insights for application-level monitoring, teams gain end-to-end visibility into both the cluster and the running applications. This integrated approach aligns with AZ-400 objectives, enabling teams to configure telemetry collection, analyze performance metrics, detect anomalies, and ensure operational excellence in containerized DevOps environments.

Question 67: 

You want to ensure that every pull request fails if code coverage falls below a specific threshold. Which approach aligns best with shift-left testing principles?

A. Enforcing coverage in PR builds
B. Nightly builds only
C. Manual coverage checks
D. Optional coverage enforcement

Answer: A. Enforcing coverage in PR builds

Explanation:

 Enforcing coverage thresholds during PR builds provides immediate feedback to developers, preventing regressions before merging. Nightly builds or manual checks detect issues too late, reducing CI/CD effectiveness. This aligns with AZ-400 objectives for integrating testing into pipelines.

Enforcing code coverage thresholds during pull request (PR) builds is a key practice in modern DevOps pipelines that supports shift-left testing and ensures high-quality code. By integrating coverage checks directly into PR validation, developers receive immediate feedback if their changes reduce the overall test coverage below a defined threshold. This allows teams to detect regressions, untested code paths, or missing tests before merging code into the main branch, reducing the likelihood of introducing defects into production. Immediate feedback also encourages developers to write comprehensive tests as part of their development workflow, fostering a culture of quality and accountability.

Relying on nightly builds or manual checks to enforce coverage is less effective. Nightly builds detect issues only after code has been integrated, which can delay the identification of regressions and increase the effort required to troubleshoot and fix problems. Manual checks are error-prone, inconsistent, and often neglected, making it difficult to maintain a reliable quality standard across the team.

AZ-400 emphasizes integrating test strategies directly into pipelines, including unit, integration, and functional tests, along with automated coverage verification. By enforcing coverage thresholds in PR builds, organizations can ensure that every code change meets quality standards, maintain confidence in the main branch, and reduce the risk of defects reaching production. This approach supports continuous integration, accelerates development cycles, and aligns with best practices for DevOps quality assurance.

Question 68:

 You are implementing a deployment strategy that allows enabling or disabling features at runtime without redeploying the application. Which approach should you use?

A. Blue-green deployment
B. Feature flags
C. Reinstalling servers
D. Manual configuration scripts

Answer: B. Feature flags

Explanation:

 Feature flags allow dynamic control over application functionality, supporting progressive exposure and fast rollback. Blue-green requires switching environments, reinstalling servers is disruptive, and manual scripts are slow. AZ-400 includes feature flags as part of deployment strategies.

Feature flags are a powerful mechanism in DevOps and continuous delivery that allow teams to enable or disable specific functionality in an application at runtime without requiring a full redeployment. By using feature flags, organizations can release new features progressively to subsets of users, perform A/B testing, or enable experimental capabilities in a controlled manner. This approach supports progressive exposure, allowing teams to validate features in production with minimal risk. If an issue arises, the feature can be quickly disabled through the flag, providing an immediate rollback without impacting the rest of the application or requiring time-consuming deployment operations.

Feature flags also enhance collaboration between development, operations, and product teams by decoupling feature release from code deployment. Developers can integrate incomplete or experimental features into the main branch while keeping them hidden from end users until they are ready for public release. This reduces the complexity of feature branches and long-lived merges, aligning with trunk-based development practices.

Other deployment strategies, such as blue-green deployments, require switching traffic between entire environments, which may involve additional infrastructure and can be slower to rollback in some cases. Reinstalling servers is disruptive, and manual scripts for enabling or disabling features are error-prone and inefficient. Feature flags provide a flexible, automated, and safe mechanism for controlling application behavior dynamically. AZ-400 emphasizes feature flags as part of deployment strategies because they enable safe, gradual releases, reduce operational risk, and support fast rollback, aligning with best practices for progressive delivery and continuous deployment.

Question 69: 

You need to release a new feature to a small subset of users first to validate functionality and monitor metrics. Which deployment strategy should you choose?

A. Blue-green deployment
B. Rolling update
C. Canary release
D. Reinstall servers

Answer: C. Canary release

Explanation: 

Canary releases expose new features to a small subset of users initially, allowing monitoring and early rollback if necessary. Blue-green switches all traffic at once, rolling updates replace instances gradually without selective exposure, and reinstalling servers is disruptive.

Canary releases are a deployment strategy designed to minimize risk when releasing new features or updates to a production environment. In a canary release, the new version of an application is deployed to a small subset of users first, allowing the development and operations teams to monitor performance, detect potential issues, and gather feedback before rolling the changes out to the entire user base. This incremental exposure helps identify bugs, performance regressions, or unexpected behavior in a controlled manner, reducing the likelihood of widespread impact on end users.

This approach differs from blue-green deployments, where all traffic is switched at once to a new environment. While blue-green deployments allow for quick rollback by switching back to the previous environment, they do not provide gradual exposure, which can be beneficial for monitoring new features under real-world conditions. Rolling updates gradually replace application instances with the new version, but this method does not allow selective user targeting and may still expose all users to issues if a problem occurs. Reinstalling servers or performing manual updates is disruptive and prone to errors, making them unsuitable for risk-managed feature releases.

Canary releases are an essential tool in DevOps pipelines that follow progressive delivery practices. They align with the AZ-400 objective of designing deployment strategies that ensure operational stability, reduce downtime, and allow rapid response to issues. By combining monitoring, gradual exposure, and automated rollback mechanisms, canary releases provide a controlled, safe method for releasing features into production while maintaining high service quality and user satisfaction.

Question 70: 

You want to track the duration from when a work item becomes active until it is completed to identify bottlenecks in your process. Which metric should you use?

A. Lead time
B. Cycle time
C. Burndown chart
D. Manual spreadsheet

Answer: B. Cycle time

Explanation:

 Cycle time measures active-to-done duration for work items, highlighting process efficiency and identifying bottlenecks. Lead time measures creation-to-production, burndown charts show remaining work, and manual spreadsheets are error-prone and non-automated.

Cycle time is a key metric in DevOps and agile processes that measures the time it takes for a work item to move from the point it becomes active to when it is completed or marked as done. By tracking cycle time, teams can gain valuable insights into process efficiency and identify bottlenecks in the workflow. Shorter cycle times generally indicate a streamlined process, while longer cycle times may highlight areas where work is delayed, blocked, or requires additional attention. Monitoring cycle time helps organizations continuously improve delivery processes, optimize resource allocation, and increase predictability in releases.

It is important to distinguish cycle time from lead time. Lead time measures the duration from when a work item is created until it is delivered to production. While lead time provides a broader view of the overall process, cycle time specifically focuses on the active period when the team is actually working on the item. Other tools, such as burndown charts, show the remaining work over a sprint or iteration but do not provide insight into elapsed time for individual items. Manual tracking using spreadsheets is prone to errors, inconsistent recording, and lacks automation, making it difficult to generate accurate metrics for continuous improvement.

In Azure DevOps, cycle time can be tracked using widgets, queries, or custom dashboards, allowing teams to visualize trends and take action on bottlenecks. This aligns with AZ-400 objectives, which emphasize measuring cycle time and lead time to drive process efficiency, enhance predictability, and support data-driven decision-making in DevOps practices. Continuous monitoring of cycle time empowers teams to optimize workflows, reduce delays, and deliver higher-quality software faster.

Question 71: 

You are managing internal and external code packages for multiple projects. You need a fully integrated Azure DevOps solution that supports NuGet, npm, Maven, Python, and Universal Packages. Which service should you use?

A. Azure Container Registry (ACR)
B. GitHub Packages
C. Azure Artifacts
D. Azure Blob Storage

Answer: C. Azure Artifacts

Explanation:

 Azure Artifacts supports multiple package types, allows creating feeds, upstream sources, and retention policies, and integrates directly with Azure DevOps pipelines. ACR is for containers, GitHub Packages is less integrated, and Blob Storage lacks package management semantics.

Azure Artifacts is a fully managed package management service provided by Microsoft that allows teams to store, share, and manage packages used in their development workflows. It supports multiple package types, including NuGet, npm, Maven, Python, and Universal Packages, making it a versatile solution for organizations that use diverse programming languages and frameworks. Teams can create feeds to host internal packages, configure upstream sources to pull packages from external public repositories, and define retention policies to manage package lifecycle automatically. This level of control ensures that only approved packages are used, supports dependency versioning, and helps maintain consistent build environments across teams.

One of the key advantages of Azure Artifacts is its seamless integration with Azure DevOps pipelines. Developers can easily restore packages during builds, publish new package versions, and enforce policies for security and compliance, all within the same DevOps ecosystem. In contrast, Azure Container Registry (ACR) is specialized for storing container images rather than general-purpose code packages. GitHub Packages can serve as an alternative, but it is less tightly integrated with Azure DevOps pipelines, requiring additional configuration for CI/CD workflows. Azure Blob Storage is a general-purpose object storage service and lacks native package management semantics such as feeds, versioning, and retention rules.

Using Azure Artifacts allows organizations to maintain a controlled, automated, and consistent approach to managing dependencies, supporting DevOps best practices. This aligns with AZ-400 objectives under “Design and implement a package management strategy,” ensuring reliability, security, and efficiency in development pipelines. By leveraging feeds, upstream sources, and retention policies, teams can reduce build failures due to missing or incompatible packages, streamline collaboration, and improve overall software delivery velocity.

Question 72: 

Your team is upgrading an AKS node pool with minimal service disruption. Which approach aligns with AZ-400 best practices?

A. Recreate the cluster from scratch
B. Directly patch nodes manually
C. Rolling node pool upgrades
D. Delete all pods and recreate them

Answer: C. Rolling node pool upgrades

Explanation:

 Rolling upgrades replace nodes incrementally while Kubernetes reschedules pods, minimizing downtime. Recreating the cluster or patching nodes manually is disruptive and error-prone. AZ-400 emphasizes safe AKS upgrades integrated into DevOps pipelines.

Rolling upgrades in Azure Kubernetes Service (AKS) are a best practice for updating cluster node pools while minimizing downtime and maintaining service availability. In a rolling upgrade, nodes in a node pool are updated incrementally rather than all at once. Kubernetes automatically reschedules pods to healthy nodes during the upgrade process, ensuring that workloads remain operational and end users experience minimal disruption. This approach is particularly important in production environments where high availability is critical and downtime can have significant business impact.

Recreating the entire cluster from scratch or manually patching nodes is disruptive and error-prone. Recreating a cluster requires redeploying all workloads and configurations, which can lead to extended downtime and increased operational risk. Manual node patching may result in inconsistencies across the cluster, introduce human errors, and complicate rollback in case of failures. In contrast, rolling upgrades leverage Kubernetes’ orchestration capabilities to handle the scheduling, health checks, and gradual replacement of nodes automatically, providing a safer and more predictable upgrade process.

AZ-400 emphasizes integrating safe upgrade strategies into DevOps pipelines to ensure that AKS clusters can be updated efficiently and reliably. By automating rolling upgrades within the CI/CD workflow, teams can maintain consistent environments, reduce operational overhead, and adhere to best practices for high availability. This strategy supports continuous delivery, ensures minimal service disruption, and allows teams to manage infrastructure changes in a controlled, automated manner, aligning with modern DevOps principles.

Question 73: 

You want to visualize the number of work items in each state over time to detect bottlenecks. Which tool should you use?

A. Cycle time widget
B. Burndown chart
C. Cumulative Flow Diagram (CFD)
D. Spreadsheet

Answer: C. Cumulative Flow Diagram (CFD)

Explanation:

 CFDs show the number of work items in each state over time, highlighting bottlenecks. Cycle time measures item duration, burndown charts show remaining work, and spreadsheets are manual and error-prone. AZ-400 emphasizes CFDs for process monitoring.

Cumulative Flow Diagrams (CFDs) are a valuable visualization tool in agile and DevOps environments for monitoring the flow of work items through various stages of a process. A CFD tracks the number of work items in each state over time, such as New, Active, In Progress, Testing, and Done. By examining the chart, teams can identify bottlenecks, detect stages where work is accumulating, and observe trends in workflow efficiency. For example, if the band representing the Testing state is widening over time, it indicates that tasks are backing up in that stage, signaling a potential delay or inefficiency that needs attention.

Cycle time, in contrast, measures the duration it takes for an individual work item to move from active to done, providing insights into process efficiency at the item level. Burndown charts show the amount of remaining work in a sprint or iteration, but they do not indicate where items are accumulating or highlight specific workflow bottlenecks. Manual tracking using spreadsheets is not only error-prone but also lacks real-time visualization, making it difficult to monitor trends or respond proactively to delays.

In Azure DevOps, CFDs can be generated from queries on work items and displayed as dashboard widgets. AZ-400 emphasizes using CFDs for process monitoring, as they enable teams to detect bottlenecks, optimize workflows, and make data-driven decisions. By regularly reviewing CFDs, organizations can improve process efficiency, maintain predictable delivery schedules, and support continuous improvement initiatives in DevOps practices.

Question 74:

 You want to automate security checks for dependencies in your CI pipeline. Which approach is most appropriate?

A. Manual scanning after deployment
B. Nightly scans only
C. Automated dependency scanning in PR or CI builds
D. Ignoring dependency vulnerabilities

Answer: C. Automated dependency scanning in PR or CI builds

Explanation: 

Automated dependency scanning detects vulnerabilities early, supporting a shift-left security approach. Manual or nightly scans delay detection and increase risk. AZ-400 emphasizes automating security and compliance scanning for dependencies, code, and secrets.

Automated dependency scanning is a critical practice in modern DevOps pipelines to ensure that vulnerabilities in libraries, packages, and external dependencies are detected as early as possible. By integrating dependency scanning into pull request or continuous integration (CI) builds, development teams can identify insecure or outdated dependencies before they are merged into the main branch. This proactive approach aligns with the shift-left security principle, which emphasizes incorporating security measures early in the software development lifecycle rather than addressing them later during production or post-release. Early detection reduces the risk of introducing vulnerabilities into production systems, minimizes potential security incidents, and lowers remediation costs.

Relying on manual scans or performing dependency checks only during nightly builds is less effective. Manual scans are error-prone, inconsistent, and can be skipped due to human oversight, while nightly scans detect issues too late, potentially allowing vulnerable code to be merged and deployed. Automated scanning ensures consistency and reliability, providing immediate feedback to developers and enforcing compliance with security policies.

AZ-400 emphasizes automating security and compliance scanning, covering dependencies, code quality, and secrets management, as part of a robust DevOps strategy. By integrating automated dependency scanning into CI/CD pipelines, organizations can maintain secure software delivery practices, ensure regulatory compliance, and reduce operational risk. This approach fosters a culture of security awareness, enables faster and safer deployments, and aligns with DevOps best practices for continuous security and quality assurance.

Question 75:

 You need to deploy a new version of an application without downtime, with the ability to roll back immediately if issues occur. Which deployment strategy should you use?

A. Rolling update
B. Blue-green deployment
C. Canary deployment
D. Reinstalling servers

Answer: B. Blue-green deployment

Explanation: 

Blue-green deployments maintain two identical environments. Traffic can be switched back to the stable environment if the new release fails. Rolling updates provide gradual updates but may cause partial disruption, canary releases roll out gradually to subsets of users, and reinstalling servers is disruptive.

Blue-green deployment is a deployment strategy designed to minimize downtime and reduce risk when releasing new versions of an application. In this approach, two identical environments are maintained: one active (blue) that serves production traffic, and one idle or staging environment (green) where the new version is deployed and tested. Once the new version is validated in the green environment, traffic is switched from the blue environment to green, making it the new production environment. If any issues are detected after the switch, traffic can be quickly reverted to the previous stable environment, ensuring minimal impact on users.

This method provides an effective way to achieve zero-downtime deployments and enables safe rollbacks in case of failures. Rolling updates, while useful for updating services gradually, may cause partial disruptions as different instances are updated at different times, which can lead to inconsistencies during the update process. Canary releases expose new features to a small subset of users initially, which helps monitor performance and gather feedback but does not immediately provide a complete rollback of all users. Reinstalling servers or performing manual deployments is disruptive, time-consuming, and prone to human error, making them less suitable for continuous delivery.

AZ-400 emphasizes designing and implementing deployment strategies such as blue-green, canary, or feature flags to ensure operational stability, reduce downtime, and mitigate risk. By adopting blue-green deployments, organizations can maintain high availability, enhance release confidence, and support modern DevOps practices that prioritize automation, reliability, and safe delivery of new features to production.

Question 76: 

Your team wants to enforce a consistent retention policy for build artifacts while keeping critical releases indefinitely. Which feature of Azure Pipelines supports this?

A. Manual artifact retention
B. External storage
C. Retention rules
D. Separate pipelines

Answer: C. Retention rules

Explanation: 

Retention rules automate artifact management based on tags, branches, or patterns. This allows retaining critical builds while cleaning up others. Manual marking is error-prone, external storage lacks integration, and separate pipelines add complexity.

Retention rules in Azure Pipelines provide a structured and automated approach for managing build and release artifacts, ensuring that storage is used efficiently while important artifacts remain available. These rules allow teams to define conditions based on tags, branches, pipeline names, or patterns to determine which artifacts should be retained and for how long. For example, critical builds such as release candidates or production deployments can be retained indefinitely, ensuring that they remain accessible for auditing, troubleshooting, or rollback purposes. Meanwhile, less critical builds can be automatically cleaned up after a defined period, such as thirty days, freeing storage and reducing maintenance overhead.

Automating artifact retention eliminates the risks associated with manual marking. Manually identifying which builds to keep or delete is error-prone and inconsistent, leading to potential loss of important artifacts or retention of unnecessary data that consumes storage. Relying on external storage for artifact management reduces integration with the pipeline, making it harder to track dependencies, enforce policies, and maintain version control. Creating separate pipelines to handle artifact retention adds unnecessary complexity and overhead, making the process more difficult to manage and maintain.

By leveraging retention rules, teams can implement predictable, reliable, and automated artifact lifecycle management. This aligns with AZ-400 objectives, which emphasize designing retention strategies for pipeline artifacts and dependencies. Retention rules support continuous integration and delivery best practices by maintaining essential artifacts, reducing manual intervention, optimizing storage use, and providing traceability and compliance in software delivery workflows. Proper implementation ensures that teams can focus on delivering value while maintaining operational efficiency and artifact governance.

Monitoring containerized workloads in Azure Kubernetes Service (AKS) requires visibility into both infrastructure and application performance to ensure high availability, reliability, and operational efficiency. Container Insights provides infrastructure-level telemetry for AKS clusters, including metrics on nodes, pods, CPU usage, memory consumption, and overall cluster health. These metrics allow operations teams to monitor resource utilization, identify potential bottlenecks, detect failing nodes or pods, and take proactive actions to maintain cluster stability. By having insight into the health and performance of the underlying infrastructure, teams can ensure that workloads are running optimally and troubleshoot issues before they impact end users.

Application Insights complements this by providing application-level telemetry and distributed tracing. It collects detailed data on application performance, request rates, response times, exceptions, and custom events. Distributed tracing enables teams to follow requests across multiple microservices, identify performance bottlenecks, and troubleshoot issues in complex, distributed architectures. This level of observability ensures that both infrastructure and application behaviors are monitored in real time.

While VM Insights provides monitoring for virtual machines, it is not designed for containerized workloads, and Log Analytics alone collects raw logs and metrics without the pre-configured dashboards, telemetry, or tracing necessary for actionable insights. Combining Container Insights with Application Insights gives organizations full end-to-end observability, aligning with AZ-400 objectives to configure telemetry and distributed tracing. This integrated monitoring approach enables teams to optimize performance, maintain reliability, and support proactive operational management in DevOps pipelines.

Question 77:

 You want to monitor both infrastructure and application performance for containerized workloads in AKS. Which combination provides comprehensive insights?

A. VM Insights + Log Analytics
B. Container Insights + Application Insights
C. Application Insights alone
D. Log Analytics alone

Answer: B. Container Insights + Application Insights

Explanation: 

Container Insights provides metrics for nodes, pods, CPU, and memory. Application Insights tracks application-level telemetry and distributed tracing. This combination gives full observability. VM Insights is for VMs, and Log Analytics alone does not provide actionable insights.

Monitoring containerized workloads in Azure Kubernetes Service (AKS) requires visibility into both infrastructure and application performance to ensure high availability, reliability, and operational efficiency. Container Insights provides infrastructure-level telemetry for AKS clusters, including metrics on nodes, pods, CPU usage, memory consumption, and overall cluster health. These metrics allow operations teams to monitor resource utilization, identify potential bottlenecks, detect failing nodes or pods, and take proactive actions to maintain cluster stability. By having insight into the health and performance of the underlying infrastructure, teams can ensure that workloads are running optimally and troubleshoot issues before they impact end users.

Application Insights complements this by providing application-level telemetry and distributed tracing. It collects detailed data on application performance, request rates, response times, exceptions, and custom events. Distributed tracing enables teams to follow requests across multiple microservices, identify performance bottlenecks, and troubleshoot issues in complex, distributed architectures. This level of observability ensures that both infrastructure and application behaviors are monitored in real time.

While VM Insights provides monitoring for virtual machines, it is not designed for containerized workloads, and Log Analytics alone collects raw logs and metrics without the pre-configured dashboards, telemetry, or tracing necessary for actionable insights. Combining Container Insights with Application Insights gives organizations full end-to-end observability, aligning with AZ-400 objectives to configure telemetry and distributed tracing. This integrated monitoring approach enables teams to optimize performance, maintain reliability, and support proactive operational management in DevOps pipelines.

Question 78:

 Your team wants to validate a new feature with a small subset of users before full rollout. Which strategy is best suited?

A. Blue-green deployment
B. Rolling update
C. Canary release
D. Manual deployment

Answer: C. Canary release

Explanation:

 Canary releases expose features to a small subset of users first, allowing monitoring and early rollback. Blue-green switches all traffic at once, rolling updates gradually replace instances but cannot control user exposure selectively, and manual deployment is error-prone.

Question 79: 

You want to enforce that new code does not reduce test coverage during pull requests. Which approach aligns with shift-left DevOps practices?

Nightly build coverage only
B. Manual coverage checks
C. PR build coverage enforcement
D. Optional coverage metrics

Answer: C. PR build coverage enforcement

Explanation:

 Enforcing coverage in PR builds provides immediate feedback, preventing regressions. Nightly or manual checks detect issues too late. AZ-400 emphasizes integrating test results into pipelines.

Question 80:

 You are implementing feature control that allows enabling or disabling functionality at runtime for progressive exposure and fast rollback. Which approach should you use?

A. Blue-green deployment
B. Feature flags
C. Reinstalling servers
D. Manual configuration

Answer: B. Feature flags

Explanation:

 Feature flags allow dynamic control of features without redeploying, supporting progressive exposure and quick rollback. Blue-green deployments require switching environments, reinstalling servers is disruptive, and manual configuration is slow. AZ-400 includes feature flags as part of deployment strategies.