fixing acrolinx

Author: Randall-Knapp

learn-pr/wwl/manage-testing-ai-powered-business-solutions/7-knowledge-check.yml

@@ -13,15 +13,15 @@ durationInMinutes: 3
 content: "Choose the best response for each of the following questions."
 quiz:
   questions:
-  - content: "Which statement best explains why traditional software testing is not sufficient for AI-powered business solutions?"
+  - content: "Which statement best explains why traditional software testing isn't sufficient for AI-powered business solutions?"
     choices:
     - content: "AI solutions require more UI automation than traditional apps."
       isCorrect: false
-      explanation: "Incorrect. While UI automation may be part of testing, it does not address the unique challenges of AI systems."
+      explanation: "Incorrect. While UI automation may be part of testing, it doesn't address the unique challenges of AI systems."
     - content: "AI outputs are probabilistic and can vary based on context, data, and input phrasing."
       isCorrect: true
-      explanation: "Correct. AI models produce probabilistic, context-dependent outputs. Two similar inputs can generate different results depending on data grounding, prompt variations, or system state. This variability requires new testing approaches that validate behavior patterns, safety, guardrails, grounding integrity, and consistency—areas traditional deterministic software testing does not fully cover."
-    - content: "AI systems do not require validation of compliance or safety."
+      explanation: "Correct. AI models produce probabilistic, context-dependent outputs. Two similar inputs can generate different results depending on data grounding, prompt variations, or system state. This variability requires new testing approaches that validate behavior patterns, safety, guardrails, grounding integrity, and consistency—areas traditional deterministic software testing doesn't fully cover."
+    - content: "AI systems don't require validation of compliance or safety."
       isCorrect: false
       explanation: "Incorrect. AI systems often require rigorous validation for compliance and safety, especially in regulated industries."
     - content: "AI solutions only need to be tested once before deployment."
@@ -31,10 +31,10 @@ quiz:
     choices:
     - content: "Length of the conversation"
       isCorrect: false
-      explanation: "Incorrect. Conversation length does not measure the quality or alignment of AI outputs with business outcomes."
+      explanation: "Incorrect. Conversation length doesn't measure the quality or alignment of AI outputs with business outcomes."
     - content: "Number of users interacting with the AI"
       isCorrect: false
-      explanation: "Incorrect. While user engagement is important, it does not directly validate the accuracy or relevance of AI outputs."
+      explanation: "Incorrect. While user engagement is important, it doesn't directly validate the accuracy or relevance of AI outputs."
     - content: "Accuracy and relevance of the AI's responses"
       isCorrect: true
       explanation: "Correct. To ensure an AI solution is delivering value, you must confirm that the outputs are accurate, relevant, and aligned with business intent. This directly validates whether the AI is producing correct insights or actions that support real business workflows."
@@ -49,9 +49,9 @@ quiz:
     - content: "Because AI output quality depends on consistent, trusted, and well-timed input data from across integrated systems."
       isCorrect: true
       explanation: "Correct. AI decisions depend on data flowing across multiple apps. Workflow orchestration can break when one app changes, data may be duplicated or transformed, and AI output quality relies on consistent, trusted, well-timed input data. Testing must validate the entire business process to ensure accurate outputs."
-    - content: "Because testing individual apps is not possible with AI solutions."
+    - content: "Because testing individual apps isn't possible with AI solutions."
       isCorrect: false
-      explanation: "Incorrect. Individual app testing is still possible, but it does not validate the end-to-end behavior of AI solutions that span multiple systems."
-    - content: "Because Dynamics 365 apps cannot share data without manual intervention."
+      explanation: "Incorrect. Individual app testing is still possible, but it doesn't validate the end-to-end behavior of AI solutions that span multiple systems."
+    - content: "Because Dynamics 365 apps can't share data without manual intervention."
       isCorrect: false
       explanation: "Incorrect. Dynamics 365 apps integrate through connectors, data sync, and automations. The challenge is validating that these integrations work correctly for AI-driven workflows."

learn-pr/wwl/manage-testing-ai-powered-business-solutions/includes/1-introduction.md

@@ -1,4 +1,4 @@
-This module introduces solution architects to the essential practices required to validate and maintain the quality of AI-powered business solutions across the enterprise. Because AI systems generate probabilistic outputs and rely on dynamic data sources, traditional testing methods are not sufficient. This module equips learners with the frameworks, metrics, and governance needed to ensure AI solutions behave reliably, safely, and in alignment with business goals.
+This module introduces solution architects to the essential practices required to validate and maintain the quality of AI-powered business solutions across the enterprise. Because AI systems generate probabilistic outputs and rely on dynamic data sources, traditional testing methods aren't sufficient. This module equips learners with the frameworks, metrics, and governance needed to ensure AI solutions behave reliably, safely, and in alignment with business goals.
 
 Learners will explore how to design structured testing processes for agents, custom AI models, prompts, and end-to-end multi-application scenarios. Each unit provides practical guidance on defining objectives, creating measurable validation criteria, evaluating safety and compliance, and understanding how data flows across integrated business applications affect AI behavior.
 

learn-pr/wwl/manage-testing-ai-powered-business-solutions/includes/3-create-validation-criteria-custom-ai-models.md

@@ -57,7 +57,7 @@ Below are key metrics that must be included in the evaluation of custom AI Model
 
 ## 3. Define Qualitative Validation Criteria
 
-Qualitative evaluation helps architects identify nuanced issues that numeric metrics cannot capture.
+Qualitative evaluation helps architects identify nuanced issues that numeric metrics can't capture.
 
 ### Criteria Examples
 

learn-pr/wwl/manage-testing-ai-powered-business-solutions/includes/4-validate-effective-copilot-prompt-best-practices.md

@@ -44,7 +44,7 @@ Solution architects validate prompts not as one-off instructions, but as **repea
 
 - **Validate constraints** Tone, length, format, exclusions, role, audience.
 
-- **Evaluate for safety** Ensure the prompt cannot unintentionally trigger sensitive or restricted actions.
+- **Evaluate for safety** Ensure the prompt can't unintentionally trigger sensitive or restricted actions.
 
 - **Run multi-scenario testing** Validate prompt quality across multiple phrasings and user types.
 

learn-pr/wwl/manage-testing-ai-powered-business-solutions/includes/8-summary.md

@@ -1,4 +1,4 @@
-This module equips solution architects with the practices and frameworks required to validate the reliability, safety, and performance of AI-powered business solutions. Learners discover why traditional deterministic testing is not enough for AI systems, which generate variable, context-dependent outputs and require structured evaluation across scenarios, data sources, user inputs, and operational environments.
+This module equips solution architects with the practices and frameworks required to validate the reliability, safety, and performance of AI-powered business solutions. Learners discover why traditional deterministic testing isn't enough for AI systems, which generate variable, context-dependent outputs and require structured evaluation across scenarios, data sources, user inputs, and operational environments.
 
 Throughout the module, architects explore how to design repeatable testing processes for AI agents, custom AI models, prompts, and end-to-end workflows across multiple business applications. The content emphasizes measurable performance indicators such as accuracy, latency, stability, grounding integrity, guardrail compliance, and user experience quality. It also introduces modern validation approaches including scenario-based testing, prompt evaluation, and observability metrics.