Scaled Agile SAFe-DevOps - SAFe DevOps Practitioner Exam SDP (6.0)

According to the SAFe DevOps Practitioner 6.0 study guide and handbook, recovery is an element of CALMR. This means that recovery is the ability to restore the Solution to a desired state after a failure or incident. The handbook states that "Recovery is the ability to restore the Solution to a desired state after a failure or incident."1 Therefore, recovery is one of the key aspects of DevOps excellence that enables continuous delivery.

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.

According to the SAFe DevOps Practitioner 6.0 study guide and handbook, Value Stream Mapping helps reveal bottlenecks to the flow of value in the Continuous Delivery Pipeline. This means that Value Stream Mapping helps teams identify and eliminate any activities or processes that slow down or stop the delivery of value to customers. The handbook states that "Value Stream Mapping helps reveal bottlenecks to the flow of value in the Continuous Delivery Pipeline."1 Therefore, Value Stream Mapping helps teams optimize their value stream and improve their delivery performance.

Refactoring is the activity of improving the internal structure or operation of a code or component without changing its external behavior. The goal of software development is the continuous delivery of business value to users and stakeholders. Constantly changing technology and evolving business objectives make maintaining and continuously increasing business value difficult. Refactoring enables an emergent design, ensuring the system continues to meet future business needs. Refactors are a particular type of Enabler story in SAFe, and, like any other Story, they must be estimable, verifiable, and valuable, as well as accepted by the Product Owner. SAFe emphasizes the importance of keeping all work visible, including refactoring. Like user value work, refactoring must be planned for, estimated, and prioritized. Therefore, developers should create and estimate refactoring Stories in the Team Backlog, and work on them according to their WSJF1

Two areas that should be monitored in the Release on Demand aspect to support DevOps and Continuous Delivery are the build status and the deployment cycle time. The build status is the measure of whether the code and components can be successfully compiled, linked, packaged, and verified into deployable binaries. The build status indicates the quality and consistency of the code and the readiness for deployment. Monitoring the build status helps to support the Release on Demand aspect in SAFe by providing valuable information for the following purposes:

To identify and fix any errors or defects that may prevent the code from being deployed or released

To ensure that the code meets the quality standards and security checks, such as static code analysis, code coverage, and code review

To verify that the code and components are integrated and merged correctly into the trunk

To track the progress and status of the features and capabilities that are being developed and delivered

The deployment cycle time is the measure of how long it takes to deploy the code and components from the source control system to the production environment. The deployment cycle time indicates the efficiency and reliability of the deployment process and the speed of delivery. Monitoring the deployment cycle time helps to support the Release on Demand aspect in SAFe by providing valuable information for the following purposes:

To optimize the deployment process and reduce the lead time and variability

To automate the deployment process and eliminate manual steps and errors

To align the deployment process with the market demand and release strategy

To evaluate the impact and value of the deployed features and capabilities 7

According to the SAFe DevOps Practitioner 6.0 study guide1, the Continuous Delivery Pipeline (CDP) represents the workflows, activities, and automation needed to guide new functionality from ideation to an on-demand release of value. The CDP consists of four aspects: Continuous Exploration (CE), Continuous Integration (CI), Continuous Deployment (CD), and Release on Demand. The CDP enables organizations to deliver new functionality to users far more frequently than traditional processes. The first step in the CDP is Continuous Exploration, which focuses on creating alignment on what needs to be built. In CE, design thinking ensures the enterprise understands the market problem or customer need and the solution required to meet that need. It starts with a hypothesis of something that will provide value to customers. Therefore, an idea marks the beginning of the CDP in CE.

Weighted Shortest Job First (WSJF) is a prioritization model used to sequence work for maximum economic benefit. In SAFe, WSJF is estimated as the relative cost of delay divided by the relative job duration. In a flow-based system, priorities must be continuously updated to provide the best economic outcomes. In other words, job sequencing produces the best results rather than prioritization based on a theoretical return on investment for each job. To that end, SAFe applies WSJF to prioritize backlogs. Backlogs are continuously prioritized based on a WSJF algorithm that uses relative user and business value, time criticality, risk reduction and/or opportunity enablement, and job size. WSJF also conveniently and automatically ignores sunk costs, a fundamental principle of Lean economics

The statement that describes the Lean startup lifecycle is: Define the hypothesis, build a minimum viable product (MVP), continuously evaluate the MVP while implementing additional Features until WSJF determines work can stop. The Lean startup lifecycle is a highly iterative build-measure-learn cycle that has proven to be effective in optimizing the economic value of strategic investments. It consists of three learning milestones: MVP, tune the engine, and pivot or persevere. The MVP is the smallest possible experiment that allows the enterprise to test the assumptions and hypotheses of an epic or a large initiative. The tune the engine phase involves quickly adjusting and moving towards the goal based on the data and feedback gathered from the MVP. The pivot or persevere phase involves deciding whether to deliver additional value or move on to something more valuable based on the validated learning. The Lean startup lifecycle is supported by the SAFe Lean Startup Cycle, which provides guidance and tools for applying the Lean startup principles in the context of SAFe. The SAFe Lean Startup Cycle includes the following steps:

Define the hypothesis – This step involves creating an epic hypothesis statement that defines the initiative, its expected benefits, and its leading indicators.

Build the MVP – This step involves defining, building, and deploying the MVP that tests the hypothesis and delivers the minimum amount of value to the customer.

Evaluate the MVP – This step involves measuring and analyzing the results of the MVP against the leading indicators and the hypothesis.

Implement additional Features – This step involves developing and delivering additional features or capabilities that enhance the value proposition of the initiative, based on the learning from the MVP.

WSJF determines work can stop – This step involves using the Weighted Shortest Job First (WSJF) prioritization method to determine whether the initiative has delivered enough value to the customer and the enterprise, or whether it needs more investment or termination3

According to the SAFe DevOps Practitioner 6.0 study guide1, hypothesis evaluation is important when analyzing data from monitoring systems in the Release on Demand aspect because it helps link objective production data to the hypothesis being tested. Hypothesis evaluation is the process of measuring and comparing the actual outcomes of a hypothesis with the expected outcomes based on customer feedback and business value. Hypothesis evaluation helps DevOps teams to validate or invalidate their hypotheses, learn from their mistakes, and improve their solutions. By linking objective production data to the hypothesis being tested, DevOps teams can see how well their solutions are delivering value to customers and achieving their goals.

According to the SAFe DevOps Practitioner 6.0 study guide and handbook, when development and operations do not collaborate early in the pipeline, surprises are found in deployment that lead to significant rework and delay. This means that the teams may not have a shared understanding of the quality, performance, and security requirements of the Solution, and may encounter issues that were not anticipated or tested. The handbook states that "When development and operations do not collaborate early in the pipeline, surprises are found in deployment that lead to significant rework and delay."1 Therefore, it is important for teams to work together throughout the value stream to ensure continuous delivery.