SAFe-DevOps SAFe DevOps Practitioner Exam SDP (6.0) Questions and Answers

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.

Agile Teams are responsible for ensuring quality is built into the code in SAFe. SAFe is a framework for scaling agile practices across the enterprise, based on the principles of Lean, Agile, and DevOps. SAFe organizes the enterprise into Agile Release Trains (ARTs), which are teams of Agile Teams that deliver value in a regular cadence. Agile Teams are the fundamental building blocks of SAFe, and they are cross-functional, self-organizing, and self-managing teams that deliver value in short iterations. Agile Teams are responsible for ensuring quality is built into the code in SAFe, by applying the following practices:

• Test-first – Test-first is a practice that involves writing tests before writing code, to ensure that the code meets the requirements and standards, and does not introduce any defects or vulnerabilities. Test-first helps to improve the design and maintainability of the code, and to accelerate the feedback and validation process. Test-first can be implemented using various techniques, such as Test-Driven Development (TDD), Behavior-Driven Development (BDD), or Acceptance Test-Driven Development (ATDD).
• Built-in quality – Built-in quality is a practice that involves applying quality standards and checks throughout the solution lifecycle, rather than inspecting quality after the fact. Built-in quality helps to prevent defects from escaping to downstream stages or customers, and to reduce the cost and risk of rework and waste. Built-in quality can be achieved using various methods, such as code quality and security analysis, code review, pair programming, refactoring, and continuous testing.
• Continuous testing – Continuous testing is a practice that involves automating and executing tests at every stage of the Continuous Delivery Pipeline, to verify that the solution meets the functional and nonfunctional requirements and standards, and to detect and resolve any issues or defects as early as possible. Continuous testing helps to ensure the reliability and performance of the solution, and to support the delivery and deployment of value to the customer. Continuous testing can be performed using various tools and frameworks, such as unit testing, integration testing, system testing, acceptance testing, performance testing, and security testing

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.

One of the measurable benefits of adopting DevOps practices and principles is that it results in faster lead time and more frequent deployments. Lead time is the time it takes from the moment a change is requested until it is delivered to the customer. Frequent deployments are the number of times a change is deployed to production. Both of these metrics indicate the speed and efficiency of the DevOps process, as well as the ability to respond to customer needs and feedback. According to the State of DevOps Report 2020, high-performing DevOps teams have 208 times faster lead times and 106 times more frequent deployments than low-performing teams

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.

According to the SAFe DevOps Practitioner 6.0 study guide and handbook, test data management is one reason for test data management. This means that test data management helps teams ensure that the application under test has a consistent and reliable state, by automating the provisioning of data from various sources and formats. The handbook states that "Test data management is the process of automating provisioning of data to an application in order to set it to a known state before testing begins."1 Therefore, test data management enables teams to perform end-to-end testing with confidence and accuracy.

Two of the technical practices that focus on Built-in Quality are pair work and test-driven development. Pair work is a collaborative technique where two developers work together on the same code, one writing the code and the other reviewing it. Pair work improves the quality of the code by reducing defects, increasing knowledge sharing, and enhancing creativity and problem-solving. Test-driven development (TDD) is a practice where developers write automated tests before writing the code, and then refactor the code until it passes the tests. TDD ensures that the code meets the requirements and specifications, as well as improves the design, readability, and maintainability of the code

The lead time for a particular step in a value stream is the time from when the work is ready from the previous step to work completion. It includes both the process time and the wait time at that step. Lead time is an important metric to measure the efficiency and flow of a value stream, as it indicates how long it takes for a unit of work to move through a step. Reducing lead time can help eliminate waste, improve throughput, and increase customer satisfaction1

According to the SAFe DevOps Practitioner 6.0 study guide and handbook, working in isolation on long-lived code branches can have negative consequences for the Continuous Delivery Pipeline. This includes making the trunk/main branch less stable and more prone to conflicts and errors. The handbook states that "Working in isolation on long-lived code branches can make the trunk/main branch less stable and more prone to conflicts and errors."1 Therefore, it is recommended to use short-lived feature branches and merge them frequently into the trunk/main branch.

The purpose of the blue/green deployment pattern is to deploy between an inactive and active environment. The blue/green deployment pattern is a change management strategy for software releases that reduces the downtime and risk of deploying new versions of software. The blue/green deployment pattern involves having two identical but separate environments: one is the active environment (blue) that serves the user traffic, and the other is the inactive environment (green) that hosts the new version of the software. The deployment process consists of switching the user traffic from the blue environment to the green environment, after verifying that the new version is working properly. The blue environment can then be kept as a backup or updated to become the next green environment. The blue/green deployment pattern has several benefits, such as:

• It allows for fast and reliable rollback, in case of any issues or errors in the new version, by simply switching back to the blue environment.
• It eliminates the need for complex and error-prone migration scripts, as the green environment can be prepared and tested in advance, without affecting the blue environment.
• It enables testing and experimentation of the new version with a subset of users, by directing some user traffic to the green environment, before switching completely.
• It facilitates continuous delivery and deployment, by automating the switching process and reducing the transaction cost and risk of moving changes to production

The primary purpose of creating an automated test suite is to enable continuous testing in SAFe DevOps. Continuous testing is the process of executing automated tests as part of the software delivery pipeline to obtain immediate feedback on the business risks associated with a software release candidate. Continuous testing supports the other aspects of the Continuous Delivery Pipeline (CDP) by providing fast and frequent feedback on the quality and compliance of the solution. Continuous testing also reduces the cost and effort of manual testing and increases the confidence and reliability of the solution. Continuous testing requires a test automation strategy that covers all types of tests, such as unit, integration, functional, performance, security, and user acceptance tests

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.

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.

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

The primary benefit of value stream mapping is that it provides insight into organizational efficiency and value flow. Value stream mapping is a lean technique to analyze, design, and manage the flow of materials and information required to bring a product or service to a customer. It uses a system of standard symbols to depict various work streams and information flows, and to identify value-adding and non-value-adding activities. Value stream mapping helps the organization to 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. Value stream mapping 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

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

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

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

The recommended way to prioritize improvement items for the DevOps transformation is to use Weighted Shortest Job First (WSJF). WSJF is a prioritization model that considers the cost of delay, job size, and economic value of each improvement item. It helps to identify the most urgent and valuable items that can be delivered in the shortest amount of time

The correct answer is B. Flow Efficiency measures the percentage of time spent on value-added activities in the Value Stream. A Value Stream is a sequence of activities that deliver value to the customer, from concept to cash. Value-added activities are those that directly contribute to the customer value proposition, such as coding, testing, and deploying. Non-value-added activities are those that do not directly contribute to the customer value proposition, such as waiting, handoffs, rework, and approvals. Flow Efficiency is calculated by dividing the total time spent on value-added activities by the total flow time, which is the time elapsed from when a work item enters the Value Stream to when it is delivered to the customer. Flow Efficiency is a metric that helps to identify and eliminate waste and delays in the Value Stream, and to optimize the flow of value to the customer. Flow Efficiency can be improved by applying the principles and practices of DevOps, such as reducing batch size, limiting work in progress, implementing pull systems, managing queue lengths, reducing handoffs, implementing Work in Process (WIP) visualization, and implementing Work in Process (WIP) feedback

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

Continuous Deployment (CD) is an aspect of the Continuous Delivery Pipeline that automates the migration of new functionality from a staging environment to production, where it is made available for release. CD is a technical practice that enables the ability to release value at any time, in a sustainable way. CD is not the same as Release on Demand, which is the business decision to make the new functionality available to end users. Release on Demand is the final aspect of the Continuous Delivery Pipeline, which enables the delivery of value to the end user as fast as possible, based on market demand

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.

The Continuous Exploration aspect in SAFe primarily supports the key stakeholder objective of aligning business and technology. This phase focuses on exploring market needs and defining solutions that deliver the most significant customer and business value within the constraints of technology and feasibility. It helps ensure that the development efforts are in sync with business goals and market demands, thereby fostering alignment between various stakeholders..