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While making revisions and modifications to the SAFe Agile Product Manager (APM 5.1) (SAFe-APM) practice exam, our team takes reports from over 90,000 professionals worldwide to make the SAFe Agile Product Manager (APM 5.1) (SAFe-APM) exam questions foolproof. To make you capable of preparing for the Scaled Agile SAFe-APM exam smoothly, we provide actual Scaled Agile SAFe-APM exam dumps.

Scaled Agile SAFe-APM Exam Syllabus Topics:

Topic	Details
Topic 1	Defining Product Strategy and Vision: This topic includes explaining the strategic purpose of the product, creating a compelling product vision, designing value propositions and business models. Moreover, it delves into mapping out the customer journey, defining the whole product and solution context, and designing the platform, API, and data strategy to support the overall product strategy.
Topic 2	Driving Strategy with Market Segmentation: This topic covers the process of market segmentation, including explaining how to create market segments through market research, determining the value of each market segment, and assessing the fit of the market.
Topic 3	Continuously Exploring Markets and Users: It explains how to do market research in the context of Continuous Exploration, find the right hypothesis and ask the right questions, and applying data-driven practices to answer questions. Furthermore, it covers planning your research and using research games for customer understanding.
Topic 4	Analyzing Your Role as a Product Manager in the Lean Enterprise: Product Management's role and responsibilities, and Product Management stakeholders and collaborators are discussed in this topic. It also delves into explaining Design Thinking for Product Management.
Topic 5	Delivering Value: This topic focuses on visualizing the development flow using a Program Kanban, estimating and forecasting the backlog, prioritizing the Program Backlog. Moreover, it explains how to create alignment through PI Planning, and execute the PI.
Topic 6	Creating Innovation in the Value Stream: This topic includes describing the value stream and approaches to product innovation, using metrics for the guidance of improvement efforts. It discusses how to manage innovations through Epics, fund sustainable innovation, and apply rapid experimentation methods. Lastly it delves into evaluating hypothesis outcomes to determine whether to pivot, stop, or continue.

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Scaled Agile SAFe Agile Product Manager (APM 5.1) Sample Questions (Q26-Q31):

NEW QUESTION # 26
The Architectural Runway is a key part of which aspect of the Design Thinking model?

A. Desirable
B. Sustainable
C. Viable
D. Feasible

Answer: D

Explanation:
The Architectural Runway is a key part of the Feasible aspect of the Design Thinking model. The Design Thinking model is a framework that guides the creation of innovative solutions that are desirable, viable, feasible, and sustainable. The Feasible aspect focuses on the technical and operational aspects of the solution, such as the architecture, infrastructure, integration, performance, and security. The Architectural Runway is a concept that describes the existing code, components, and technical infrastructure that enable the development and delivery of near-term features without excessive redesign and delay. The Architectural Runway supports the Feasible aspect by providing the necessary technical foundation and alignment for developing business initiatives and implementing new features and capabilities.
Reference:
Architectural Runway - Scaled Agile Framework
Design Thinking - Scaled Agile Framework

NEW QUESTION # 27
What criterion must be satisfied before a Feature can flow from the analysis state to the backlog state on an ART Kanban?

A. The Feature is decomposed into Stories
B. Teams define the build and test Solution
C. The Feature is integrated and deployed
D. The benefit hypothesis and acceptance criteria are defined

Answer: D

Explanation:
The benefit hypothesis and acceptance criteria are the criteria that must be satisfied before a Feature can flow from the analysis state to the backlog state on an ART Kanban. The benefit hypothesis and acceptance criteria are part of the definition of a Feature, which represents a solution functionality that delivers business value and fulfills a stakeholder need. The benefit hypothesis states the expected outcome and benefit of implementing the Feature, while the acceptance criteria define the conditions that must be met for the Feature to be accepted by the stakeholders. The benefit hypothesis and acceptance criteria help to clarify the scope, value, and quality of the Feature, and to guide the implementation and validation of the Feature.
References:
* Features and Capabilities - Scaled Agile Framework
* ART and Solution Train Backlogs - Scaled Agile Framework
* Which two criteria must be satisfied before a Feature flows from ...

NEW QUESTION # 28
Which type of product might face a chasm in its adoption?

A. Mature product
B. Innovative product
C. Growth product
D. Decommissioned product

Answer: B

Explanation:
The chasm is the gap in the technology adoption lifecycle between the early adopters and the early majority. The early adopters are enthusiasts who are open to trying new technologies, whereas the early majority is more pragmatic and cautious. The chasm occurs due to the differing expectations and requirements of these two groups. An innovative product is a product that introduces a new or significantly improved technology, functionality, or design that creates a new market or disrupts an existing one. An innovative product might face a chasm in its adoption, because it may not appeal to the mainstream customers who value reliability, compatibility, and convenience over novelty and differentiation. Therefore, an innovative product needs to cross the chasm by finding a niche market, establishing a clear value proposition, and creating a strong word-of-mouth.
Reference:
Crossing the Chasm in the Technology Adoption Life Cycle: This article from Business to You explains the concept and purpose of the technology adoption lifecycle and the chasm, and how they can help marketers understand and target different customer segments.
How to Cross the Chasm & Scale Your SaaS: This article from Userpilot provides a practical guide on how to cross the chasm and scale a SaaS product, with examples and tips on finding product-market fit, defining a beachhead market, and creating a whole product.

NEW QUESTION # 29
Which statement is true about designing a data strategy?

A. It is best when architected in the analyzing state within the ART Kanban
B. It requires collaboration between Product Managers and their Customers
C. Data monetization vastly increases the short-term value of products
D. It should be approached as a one-time project

Answer: B

Explanation:
Designing a data strategy is the process of defining the vision, goals, principles, and governance of data and analytics in an organization. A data strategy should align with the business strategy and enable the delivery of value from data and AI initiatives. Designing a data strategy requires collaboration between Product Managers and their Customers, as they are the ones who understand the needs, problems, and opportunities of the market and the users. Product Managers and Customers should work together to identify the data sources, use cases, metrics, and feedback loops that will inform the data strategy and drive the data-driven decision making.
Reference:
Design your data strategy in six steps | IBM
How to build and deliver an effective data strategy: part 1 - Microsoft UK Blog Crafting Your Data Strategy - data.org Designing Data Strategies - Development Gateway: An IREX Venture

NEW QUESTION # 30
Which statement is true about designing a data strategy?

A. It is best when architected in the analyzing state within the ART Kanban
B. It requires collaboration between Product Managers and their Customers
C. Data monetization vastly increases the short-term value of products
D. It should be approached as a one-time project

Answer: B

Explanation:
Designing a data strategy is the process of defining the vision, goals, principles, and governance of data and analytics in an organization. A data strategy should align with the business strategy and enable the delivery of value from data and AI initiatives. Designing a data strategy requires collaboration between Product Managers and their Customers, as they are the ones who understand the needs, problems, and opportunities of the market and the users. Product Managers and Customers should work together to identify the data sources, use cases, metrics, and feedback loops that will inform the data strategy and drive the data-driven decision making.
References:
* Design your data strategy in six steps | IBM
* How to build and deliver an effective data strategy: part 1 - Microsoft UK Blog
* Crafting Your Data Strategy - data.org
* Designing Data Strategies - Development Gateway: An IREX Venture

NEW QUESTION # 31
......

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HOT SAFE-APM NEW DUMPS PPT | HIGH PASS-RATE SCALED AGILE SAFE-APM TEST KING: SAFE AGILE PRODUCT MANAGER (APM 5.1)

Hot SAFe-APM New Dumps Ppt | High Pass-Rate Scaled Agile SAFe-APM Test King: SAFe Agile Product Manager (APM 5.1)