SAFe-DevOps Sample Questions Answers

After conducting current-state mapping in the context of Value Stream Mapping, a team should be able to identify and describe the biggest bottlenecks in the delivery pipeline. Current-state mapping is a process of visualizing the entire flow of a product or service from start to finish, highlighting each step and identifying where delays or inefficiencies occur. By understanding where these bottlenecks are, teams can then work on strategies to improve the overall flow, reduce lead times, and enhance efficiency. This step is crucial for identifying areas for improvement and setting the stage for future actions to optimize the Value Stream.

According to the SAFe DevOps Practitioner 6.0 study guide and handbook, dynamic code analysis is a technical practice that incorporates build-time identification of security vulnerabilities in the code. This means that the code is scanned for potential security risks during the compilation or execution phase, rather than during the testing phase. The handbook states that "Dynamic code analysis is a technique that identifies security vulnerabilities in the code during compilation or execution."1 Therefore, dynamic code analysis helps teams detect and fix security issues early in the development process.

Two activities performed as part of defining the hypothesis in Continuous Exploration are identifying metrics based on leading indicators and defining the minimum viable product. Continuous Exploration (CE) is an aspect of the Continuous Delivery Pipeline that drives innovation and fosters alignment on what should be built by continually exploring the market and customer needs, defining a vision, roadmap, and set of features for a solution that addresses those needs. CE is based on applying customer-centric and design thinking to understand and create alignment on new development opportunities, while recognizing that all such ideas are hypotheses that need to be validated. CE involves four activities: hypothesize, collaborate and research, synthesize, and validate. The hypothesize activity describes the practices for generating ideas and the measurements needed to validate them with customers. The hypothesize activity involves the following practices:

• Identifying metrics based on leading indicators – Leading indicators are metrics that measure the expected outcomes and benefits of the solution, such as customer satisfaction, retention, engagement, and revenue. Leading indicators help to evaluate the validity of the hypotheses and assumptions about the customer value proposition, and to guide the decision making and prioritization of the features. Leading indicators are also known as key performance indicators (KPIs) or objectives and key results (OKRs).
• Defining the minimum viable product (MVP) – An MVP is a version of the solution that has just enough features to test the hypotheses and assumptions about the customer value proposition, and to elicit feedback from the customer. An MVP is not a fully functional or polished product, but rather a learning vehicle that allows the enterprise to validate the problem-solution fit and the product-market fit. An MVP helps to reduce the uncertainty and risk of developing the wrong solution, and to accelerate the learning and discovery process11

In addition to Continuous Integration, two other aspects of the CDP are Continuous Deployment and Continuous Exploration. Continuous Deployment automates the migration of new functionality from a staging environment to production, where it is made available for release. Continuous Deployment enables the ability to release value at any time, in a sustainable way. Continuous Exploration focuses on creating alignment on what needs to be built, using design thinking and hypothesis-driven development. Continuous Exploration enables the discovery of the most valuable solutions for the customers and the enterprise

One of the recommended ways to architect for operations is to implement feature toggles, which are mechanisms that allow developers to turn features on or off without redeploying the code. Feature toggles enable faster and safer deployments, as well as experimentation and testing in production. They also allow the operations team to control when and how new features are released to the end users, based on business needs and market demand. Feature toggles can also be used to mitigate risks and roll back changes in case of errors or failures

The Release on Demand aspect enables the key business objective of delivering business value to customers. Release on Demand is the process by which features deployed into production are released incrementally or immediately to customers based on market demand. It is the final aspect of the Continuous Delivery Pipeline (CDP) in SAFe DevOps, which enables the delivery of value to the end user as fast as possible, based on market demand. The decision of what and when to release is a critical economic driver that requires careful consideration of the customer needs, market rhythms, and economic outcomes. Release on Demand is decoupled from the Continuous Deployment activity, which automates the migration of new functionality from a staging environment to production, where it is made available for release. Release on Demand involves four activities: release, stabilize and operate, measure, and learn. These activities help to deliver the solution to end users, ensure the solution is working well from a functional and nonfunctional perspective, quantify the value delivered by the solution, and collect feedback and prepare for the next loop through the CDP3

The statement that is true about DevOps is that it enables low-risk releases and fast recovery with fast fix-forward. DevOps is a mindset, culture, and set of technical practices that supports the integration, automation, and collaboration needed to effectively develop and operate a solution. DevOps aims to deliver value to customers whenever there is a business need, by applying the principles of continuous delivery, continuous integration, continuous deployment, and release on demand. DevOps also embraces a culture of learning and experimentation, where failures are opportunities for improvement and feedback. DevOps enables low-risk releases by breaking down large and complex changes into smaller and more frequent batches, which are easier to test, deploy, and rollback if needed. DevOps also enables fast recovery by implementing practices such as monitoring, alerting, incident response, and disaster recovery, which help to detect and resolve issues quickly, minimize the impact of failures, and restore normal operations as soon as possible. DevOps also supports the fast fix-forward approach, which means fixing errors in production by deploying new code, rather than rolling back to a previous state. This approach reduces the risk of introducing new errors, preserves the value of the new functionality, and accelerates the learning cycle

According to the SAFe DevOps Practitioner 6.0 study guide and handbook, specific improvements to the Continuous Delivery Pipeline are identified in Future-state Value Stream Mapping. This activity involves envisioning and designing the ideal state of the value stream, based on the current state and the desired outcomes. The handbook states that "Future-state Value Stream Mapping is a way of defining how you want your value stream to look like in the future."1 Therefore, Future-state Value Stream Mapping helps teams identify and prioritize the areas that need improvement in their Continuous Delivery Pipeline.

According to the SAFe DevOps Practitioner 6.0 study guide1, one of the main challenges of a DevOps transformation is when various groups in the organization have different directions and goals. This can create silos, conflicts, and inefficiencies that hinder the collaboration and integration needed for continuous delivery. For example, some groups may prioritize speed over quality, while others may prioritize security over innovation. Some groups may resist change and adopt outdated practices, while others may embrace new technologies and methodologies. These differences can lead to misalignment, duplication, and waste of resources that slow down the delivery pipeline and reduce the value to customers. Therefore, having a common vision, culture, and set of technical practices that support the SAFe CALMR approach to DevOps is essential for overcoming this challenge.

According to the SAFe DevOps Practitioner 6.0 study guide and handbook, the Release activity is triggered by a business decision to go live. This means that the Solution is ready to be delivered to end users and customers, based on the validation and testing done in the previous activities. The handbook states that "The Release activity is triggered by a business decision to go live."1 Therefore, this activity involves releasing value to customers on demand, while also measuring and learning from the feedback.

When preparing a DevOps backlog, prioritizing features using WSJF includes two factors: cost of delay and duration/job size. WSJF stands for Weighted Shortest Job First, which is a prioritization model used to sequence work for maximum economic benefit. WSJF is estimated as the relative cost of delay divided by the relative job duration. Cost of delay is the money lost by delaying or not doing a job for a specific time. It is a measure of the economic value of a job over time. Job duration is the time it takes to complete a job. Jobs that can deliver the most value in the shortest duration provide the best economic return. WSJF also considers other factors that contribute to the cost of delay, such as user and business value, time criticality, and risk reduction and/or opportunity enablement

In the Scaled Agile Framework (SAFe), after features are defined and designed during Continuous Exploration, they move into Continuous Integration. Here, they are further broken down into smaller, implementable parts known as user stories. These stories are then developed, tested, and integrated into the existing codebase. This process ensures that new features are developed in a systematic and efficient manner, with regular integration and testing to maintain quality and readiness for release.

According to the SAFe DevOps Practitioner 6.0 study guide and handbook, Weighted Shortest Job First (WSJF) is applied to backlogs to identify relative economic value. This means that the WSJF algorithm uses factors such as user and business value, time criticality, risk reduction and/or opportunity enablement, and job size to rank the jobs in the backlog based on their economic benefit. The handbook states that "WSJF is estimated as the relative cost of delay divided by the relative job duration."1 Therefore, WSJF helps teams prioritize their backlog items for continuous delivery.

Mapping the value stream helps accomplish two actions: to gain insight into organizational efficiency and to understand how the flow of value can be improved. A value stream is the series of steps that an organization uses to implement solutions that provide a continuous flow of value to a customer. Mapping the value stream involves identifying the steps, people, inputs, outputs, tools, and metrics involved in delivering value from concept to cash. By mapping the value stream, the organization can gain insight into the current state of the delivery process, such as the lead time, cycle time, throughput, quality, and waste. This insight can help the organization identify bottlenecks, dependencies, handoffs, delays, and inefficiencies that affect the flow of value. Mapping the value stream also helps the organization understand how the flow of value can be improved by applying the principles and practices of DevOps, such as culture, automation, lean flow, measurement, and recovery. By improving the flow of value, the organization can increase customer satisfaction, reduce costs, accelerate time-to-market, and enhance business agility

 According to the SAFe DevOps Practitioner 6.0 study guide and handbook, Continuous Deployment is a practice that helps developers deploy their code into production. This means that Continuous Deployment involves moving changes from the staging environment to the production environment, without requiring manual intervention or approval. The handbook states that "Continuous Deployment is the practice of moving changes from the staging environment to the production environment, without requiring manual intervention or approval."1 Therefore, Continuous Deployment enables teams to deliver value to customers faster and more reliably.

The %C&A metric measures the percent of time downstream customers receive work that is usable as-is in the Continuous Delivery Pipeline. The %C&A metric is a measure of the quality of the work output from a process step. It indicates the percentage of time that the downstream customers receive work that is acceptable as is, without any rework or errors. To calculate the %C&A metric, the people responsible for the next step need to inspect the work output from the previous step and determine whether it meets the quality standards and expectations. They also need to provide feedback to the previous step on the defects or issues they find and how they affect the value stream. By measuring the %C&A metric, the enterprise can gain insight into the current state of the delivery process, such as the lead time, cycle time, throughput, quality, and waste. This insight can help the enterprise identify bottlenecks, dependencies, handoffs, delays, and inefficiencies that affect the flow of value. The %C&A metric can also help the enterprise understand how the flow of value can be improved by applying the principles and practices of DevOps, such as culture, automation, lean flow, measurement, and recovery. By improving the %C&A metric, the enterprise can increase customer satisfaction, reduce costs, accelerate time-to-market, and enhance business agility

According to the SAFe DevOps Practitioner 6.0 study guide1, root cause analysis is a technique that helps identify and eliminate the underlying causes of problems or defects in the Solution. Root cause analysis can be used to address impediments to continuous delivery by finding and fixing the root causes of issues such as delays, rework, waste, and errors. Root cause analysis can be performed during various phases of the PI, such as during the Iteration Retrospective, during the management review and problem-solving portion of PI Planning, or during Inspect & Adapt (I&A). However, the most effective time to use root cause analysis is during I&A, which is a significant event held at the end of each PI, where the current state of the Solution is demonstrated and evaluated. Teams then reflect and identify improvement backlog items via a structured problem-solving workshop. The result is a set of improvement backlog items that go into the ART Backlog for the next PI Planning event. Therefore, root cause analysis can help teams address impediments to continuous delivery by improving their performance and quality in the next PI.

The correct answer is B. Compile. The Build activity of the Continuous Delivery Pipeline is responsible for creating deployable binaries and merging development branches into the trunk. The Build activity includes the following practices:

• Compile – Compile is the process of transforming the source code into executable code that can run on a specific platform or environment. Compile involves checking the syntax, semantics, and dependencies of the code, and resolving any errors or conflicts. Compile also involves linking the code with other libraries or components that are required for the functionality of the solution.
• Package – Package is the process of bundling the compiled code and other resources into a format that can be easily deployed to the target environment. Package involves creating archives, containers, or installers that contain the necessary files and metadata for the deployment process. Package also involves applying versioning and configuration information to the packages, to ensure traceability and consistency.
• Verify – Verify is the process of ensuring that the code and packages meet the quality standards and security checks, and that they are ready for deployment and release. Verify involves applying automated tools and processes, such as static code analysis, code coverage, code review, and code quality metrics, to detect and resolve any issues or vulnerabilities in the code and packages. Verify also involves ensuring compliance with the coding standards and guidelines, and documenting the results and feedback of the verification process12

The Respond activity in the SAFe DevOps Health Radar is about proactively detecting and resolving production issues before they cause business disruption. It requires two skills: cross-team collaboration and version control. Cross-team collaboration enables faster resolution of incidents by involving the right people and teams. Version control enables tracking changes and restoring previous versions of the code or configuration if needed

The Boundaries and Limitations portion of the DevOps Transformation Canvas is used to capture improvement items that require the support of other people or teams. These are items that are outside the scope or control of the current team or value stream, but are necessary for achieving the DevOps vision and goals. They help to identify dependencies, risks, and stakeholders that need to be engaged or aligned

Trunk-based development is a version control management practice where all developers work on the same trunk of shared code. The trunk is always in a releasable state, which means that at least once a day, developers must integrate their changes to the trunk. This is accomplished through short-lived feature branches related to project tasks. Trunk-based development is a common practice among DevOps teams and part of the DevOps lifecycle since it streamlines merging and integration phases. It also enables continuous integration, which is the practice of merging all development versions of a code base several times a day. Trunk-based development has several benefits, such as:

• It reduces the complexity and conflicts of merging long-lived branches
• It improves the quality and consistency of the code by enforcing frequent testing and validation
• It accelerates the delivery and deployment of new functionality by minimizing the transaction cost and risk
• It fosters a culture of collaboration and transparency among developers 

The correct order of activities in the Continuous Integration aspect is: Develop, Build, Test end-to-end, Stage. Continuous Integration (CI) is an aspect of the Continuous Delivery Pipeline that automates the development, testing, integration, and validation of new functionality in preparation for deployment and release. CI is the second aspect in the four-part Continuous Delivery Pipeline of Continuous Exploration (CE), Continuous Integration (CI), Continuous Deployment (CD), and Release on Demand. CI consists of four activities, as shown in Figure 1:

• Develop – This activity involves implementing stories by refining features from the ART Backlog, coding, testing, and committing the work product into the source control system. Testing in this activity tends to focus on unit and story-level testing and often requires test doubles to replicate other components or subsystems that are not readily available or easily tested.
• Build – This activity involves creating deployable binaries and merging development branches into the trunk. Building in this activity includes compiling, linking, packaging, and verifying the code and components. Building also involves applying code quality and security checks, such as static code analysis, code coverage, and code review.
• Test end-to-end – This activity involves validating the solution end-to-end, including the functional and nonfunctional aspects, such as performance, usability, reliability, and security. Testing in this activity requires integrating the code and components with other subsystems and services, and using test environments that resemble the production environment as much as possible. Testing also involves applying automated testing tools and frameworks, such as regression testing, integration testing, system testing, and acceptance testing.
• Stage – This activity involves hosting and validating solutions in a staging environment before production. Staging in this activity includes deploying the solution to a pre-production environment that mimics the production environment in terms of hardware, software, configuration, and data. Staging also involves applying final checks and verifications, such as smoke testing, exploratory testing, and user acceptance testing910

1: https://www.lean.org/the-lean-post/articles/understanding-the-fundamentals-of-value-stream-mapping/  2: https://www.gembaacademy.com/school-of-lean/value-stream-mapping/adapting-value-stream-mapping-for-office-and-service-environments/what-is-the-c-a-percentage-complete-accurate-metric  3: https://scaledagileframework.com/guidance-applied-innovation-accounting-in-safe/  4: https://support.scaledagile.com/s/article/Exam-Study-Guide-SDP-6-0-SAFe-for-DevOps  5: https://www.redhat.com/en/topics/devops/what-is-blue-green-deployment  6: https://docs.cloudfoundry.org/devguide/deploy-apps/blue-green.html  7: https://scaledagileframework.com/guidance-applied-innovation-accounting-in-safe/  8: https://support.scaledagile.com/s/article/Exam-Study-Guide-SDP-6-0-SAFe-for-DevOps  9: https://scaledagileframework.com/continuous-integration/  10: https://support.scaledagile.com/s/article/Exam-Study-Guide-SDP-6-0-SAFe-for-DevOps

The reason why it is important to take a structured approach to analyze problems in the delivery pipeline is that it helps ensure that actual causes of problems are addressed, rather than symptoms. A structured approach to problem analysis involves following a systematic and logical process to identify, define, and understand the root causes of a problem, and to propose and evaluate possible solutions. A structured approach can help avoid jumping to conclusions, making assumptions, or applying band-aid fixes that do not solve the underlying issues. A structured approach can also help involve the relevant stakeholders, collect and analyze data, prioritize and categorize problems, and communicate and document the findings and recommendations. Some examples of structured approaches to problem analysis are the 5 Whys, the fishbone diagram, the Pareto chart, and the PDCA cycle

According to the SAFe DevOps Practitioner 6.0 study guide1, Lean UX is used in Continuous Exploration to create a platform to continuously explore. Continuous Exploration is the process of creating alignment on what needs to be built, synthesizing Solution ideas into ART Backlog Features, and utilizing WSJF to prioritize a DevOps backlog. Lean UX is a team-based approach to building better products by focusing less on the theoretically ideal design and more on iterative learning, overall user experience, and customer outcomes. Lean UX design extends the traditional UX role beyond merely executing design elements and anticipating how users might interact with a system. Instead, it encourages a far more comprehensive view of why a Feature exists, the functionality required to implement it, and the benefits it delivers. By getting immediate feedback to understand if the system will meet the fundamental business objectives, Lean UX provides a closed-loop method for defining and measuring value. Therefore, Lean UX is used in Continuous Exploration to create a platform that enables teams to experiment with different hypotheses, validate their assumptions, and learn from their failures.

According to the SAFe DevOps Practitioner 6.0 study guide1, one of the organizational anti-patterns that DevOps helps to address is teams that are isolated and working within functional areas (e.g., business, operations, and technology). This anti-pattern creates silos, conflicts, and inefficiencies that hinder the collaboration and integration needed for continuous delivery. For example, some teams may prioritize speed over quality, while others may prioritize security over innovation. Some teams may resist change and adopt outdated practices, while others may embrace new technologies and methodologies. These differences can lead to misalignment, duplication, and waste of resources that slow down the delivery pipeline and reduce the value to customers. Therefore, DevOps helps to break down the functional silos and enable cross-functional teams that work together to deliver value to customers faster and more reliably.

The correct answer is D. Flow, quality, and value should be measured in a CALMR approach to DevOps. The CALMR approach to DevOps is a mindset that guides the ARTs toward achieving continuous value delivery by enhancing culture, automation, lean flow, measurement, and recovery. Measurement is one of the key elements of the CALMR approach, as it provides data and feedback to monitor and improve the performance and outcomes of the solution delivery process. Measurement involves collecting and analyzing metrics that reflect the flow, quality, and value of the solution, such as:

• Flow metrics – Flow metrics measure the efficiency and reliability of the delivery process, such as lead time, cycle time, throughput, work in progress, and flow efficiency. Flow metrics help to identify and eliminate waste and bottlenecks in the value stream, and to optimize the flow of value to the customer.
• Quality metrics – Quality metrics measure the functionality and stability of the solution, such as defect rate, defect density, defect leakage, mean time to detect, mean time to resolve, and test coverage. Quality metrics help to ensure that the solution meets the functional and nonfunctional requirements and standards, and to detect and resolve any issues or defects as early as possible.
• Value metrics – Value metrics measure the outcomes and benefits of the solution, such as customer satisfaction, retention, engagement, revenue, and net promoter score. Value metrics help to evaluate the validity of the hypotheses and assumptions about the customer value proposition, and to guide the decision making and prioritization of the features12