Fix blocking review feedback for Module 10

Author: msdavidram

learn-pr/wwl/design-alm-process-ai-powered-business-solutions/includes/1-introduction.md

@@ -1,4 +1,4 @@
-AI powered business solutions introduce new layers of complexity that extend far beyond traditional software lifecycle practices. Models evolve, data shifts, prompts change behavior, and AI agents adapt based on context. As a result, organizations must adopt a disciplined, end-to-end application lifecycle management (ALM) approach.
+AI-powered business solutions introduce new layers of complexity that extend far beyond traditional software lifecycle practices. Models evolve, data shifts, prompts change behavior, and AI agents adapt based on context. As a result, organizations must adopt a disciplined, end-to-end application lifecycle management (ALM) approach.
 This approach governs application code, datasets, prompts, connectors, and model configurations. This module gives solution architects the framework they need to establish that discipline across the entire AI solution stack.
 
 In this module, learners explore how to design ALM processes that keep AI components governed, reproducible, secure, and monitored from development through retirement across multiple Microsoft technologies. This module explains how AI data, Copilot Studio assets, Microsoft Foundry agents, custom AI models, and Dynamics 365 AI features move through structured environments.

learn-pr/wwl/design-alm-process-ai-powered-business-solutions/includes/2-design-alm-process-data-used-ai-models-agents.md

@@ -12,7 +12,7 @@ By the end of this unit, learners are be able to:
 
 * Specify **data residency and movement** policies for Copilot and agent features.
 
-* Operationalize **quality, lineage, and drift** checks with go/nogo criteria and rollback plans.
+* Operationalize **quality, lineage, and drift** checks with go/no-go criteria and rollback plans.
 
 * Establish a **RACI** and change workflows for datasets, knowledge sources, and evaluation telemetry.
 
@@ -46,7 +46,7 @@ By the end of this unit, learners are be able to:
 
 * **Professional visual—Environment & data flow (text diagram)**<br>Dev (Red data → feature builds) → Test (Repro runs, evaluation sets) → Pre-Prod (Gold candidates) → Prod (Gold only)<br>Controls at each hop: validation → approval → immutable snapshot → catalog update
 
-## The AI data ALM process (endtoend)
+## The AI data ALM process (end-to-end)
 
 ### Phase A—Plan & Catalog
 
@@ -82,7 +82,7 @@ By the end of this unit, learners are be able to:
 
 * Perform **privacy, security, and compliance** reviews (DLP, RAI, export controls).
 
-* Execute **canary runs** using masked/representative Prod like data.
+* Execute **canary runs** using masked/representative Prod-like data.
 
 * _Canary testing is a low-risk deployment strategy.
 It releases new code to a small, isolated subset of users or servers to identify issues before a full rollout._
@@ -129,13 +129,13 @@ It releases new code to a small, isolated subset of users or servers to identify
 
 ## Region, residency, and cross-border movement
 
-Document **where** prompts/outputs may be processed for Copilot and Power Platform features, and when **cross region capacity** is required.
+Document **where** prompts/outputs may be processed for Copilot and Power Platform features, and when **cross-region capacity** is required.
 
-In regulated scenarios, set the default to **inregion** and require explicit approval to enable **overflow processing**.
+In regulated scenarios, set the default to **in-region** and require explicit approval to enable **overflow processing**.
 
 Align **mailbox region** (for activity data) and environment geo with your policy; define exceptions and purge schedules.
 
-Professional visual—Residency decision tree (text)<br>Inregion capacity available? → Yes: keep local.<br>No: Is overflow allowed for this workload tier? → If yes, enable cross region under admin control; else block feature or defer.
+Professional visual—Residency decision tree (text)<br>In-region capacity available? → Yes: keep local.<br>No: Is overflow allowed for this workload tier? → If yes, enable cross-region under admin control; else block feature or defer.
 
 ## Roles and RACI
 
@@ -160,7 +160,7 @@ The below RACI chart is a representative sample for an organization. It's up to
 
 Maintain **baseline metrics** per release: latency p95, success %, token/€ per task, safety flags/M runs.
 
-Compare live to baseline; if drift exceeds thresholds, **autoopen an incident**, route to data owner, and pause affected actions.
+Compare live to baseline; if drift exceeds thresholds, **auto-open an incident**, route to data owner, and pause affected actions.
 
 Rerun evaluation suites nightly/weekly against **golden sets**; store time series for audit.
 

learn-pr/wwl/design-alm-process-ai-powered-business-solutions/includes/3-design-alm-process-copilot-studio-agents-connectors-actions.md

@@ -1,6 +1,6 @@
 ## Overview
 
-This unit guides solution architects through designing an Application Lifecycle Management (ALM) process tailored for **Copilot Studio agents, connectors, and custom actions**. A welldefined ALM strategy ensures that Copilotbased solutions are **consistent, secure, governed, versioncontrolled, and ready for enterprise scale** across development, testing, and production environments.
+This unit guides solution architects through designing an Application Lifecycle Management (ALM) process tailored for **Copilot Studio agents, connectors, and custom actions**. A well-defined ALM strategy ensures that Copilot-based solutions are **consistent, secure, governed, version-controlled, and ready for enterprise scale** across development, testing, and production environments.
 
 Architects learn how to structure solution components into managed lifecycles, establish promotion and approval workflows, manage data and environment boundaries, and integrate development tooling—enabling a predictable, repeatable process that aligns with enterprise governance.
 
@@ -44,7 +44,7 @@ Sustainable lifecycle for updates and deprecations
 
 No direct editing in production
 
-Enforce rolebased access at each tier
+Enforce role-based access at each tier
 
 Managed solutions only in Test and Prod
 
@@ -76,15 +76,15 @@ Build actions and prompts
 
 Add knowledge sources in Dev only
 
-Test agent workflows with edgecase prompts
+Test agent workflows with edge-case prompts
 
 ### 3.2 Testing Stage
 
 Validate reasoning quality and output patterns
 
 Ensure grounding is reliable and compliant
 
-Evaluate eventtriggered actions
+Evaluate event-triggered actions
 
 Run regression tests on all agent topics
 
@@ -148,7 +148,7 @@ Use Visual Studio Code for Copilot connector development where applicable
 
 ### Adopt release cadence
 
-Monthly or sprintbased release cycles
+Monthly or sprint-based release cycles
 
 Emergency patch process for critical fixes
 
@@ -164,11 +164,11 @@ ALM must align with enterprise governance rules:
 
 DLP policy enforcement
 
-Environmentspecific connector rules
+Environment-specific connector rules
 
 Data residency restrictions
 
-Enterpriseapproved knowledge sources
+Enterprise-approved knowledge sources
 
 Review gate for safety, quality, and ethical risk
 

learn-pr/wwl/design-alm-process-ai-powered-business-solutions/includes/4-design-alm-process-microsoft-foundry-agents.md

@@ -1,8 +1,8 @@
 ## Overview
 
-This unit equips solution architects with the knowledge to design a complete Application Lifecycle Management (ALM) process for **Microsoft Foundry agents**. Foundry introduces a structured, enterprisegrade model for creating, governing, deploying, and maintaining agents across environments. A robust ALM approach ensures controlled development, quality assurance, predictable deployment, and secure runtime operations.
+This unit equips solution architects with the knowledge to design a complete Application Lifecycle Management (ALM) process for **Microsoft Foundry agents**. Foundry introduces a structured, enterprise-grade model for creating, governing, deploying, and maintaining agents across environments. A robust ALM approach ensures controlled development, quality assurance, predictable deployment, and secure runtime operations.
 
-Solution architects must implement ALM processes that standardize agent creation, maintain configuration discipline, control data and model behavior, and integrate operational governance to ensure longterm reliability, compliance, and performance.
+Solution architects must implement ALM processes that standardize agent creation, maintain configuration discipline, control data and model behavior, and integrate operational governance to ensure long-term reliability, compliance, and performance.
 
 ## ALM foundations for Microsoft Foundry agents
 
@@ -26,7 +26,7 @@ The Foundry control plane enables centralized oversight of agent definitions, se
 
 ## Environment strategy
 
-A multienvironment model is essential for isolating agent development from production workloads.
+A multi-environment model is essential for isolating agent development from production workloads.
 
 ### Recommended environment tiers
 
@@ -40,7 +40,7 @@ A multienvironment model is essential for isolating agent development from produ
 
 Microsoft Foundry agents consist of modular components that must be governed and versioned.
 
-* **Key ALMmanaged components**
+* **Key ALM-managed components**
 
 * **Agent logic and orchestration**
 
@@ -102,7 +102,7 @@ Foundry agents rely on data retrieved from controlled environments. ALM must enf
 
 * **Usage of approved data sources**
 
-* **Rolebased access controls (RBAC)**
+* **Role-based access controls (RBAC)**
 
 * **Separation of production secrets from development artifacts**
 
@@ -130,7 +130,7 @@ A centralized governance strategy ensures consistency and reduces operational ri
 
 * Release calendars and change freezes
 
-* Productionready validation templates
+* Production-ready validation templates
 
 ## Monitoring, feedback, and continuous improvement
 

learn-pr/wwl/design-alm-process-ai-powered-business-solutions/includes/5-design-alm-process-custom-ai-models.md

@@ -1,24 +1,24 @@
 ## Overview
 
-This unit provides solution architects with a structured, enterprisegrade Application Lifecycle Management (ALM) process for custom AI models. It outlines how to manage model creation, evaluation, deployment, governance, and retirement across environments. The goal is to ensure that custom AI models remain reliable, compliant, traceable, and adaptable to changes in business data, requirements, and AI technologies.
+This unit provides solution architects with a structured, enterprise-grade Application Lifecycle Management (ALM) process for custom AI models. It outlines how to manage model creation, evaluation, deployment, governance, and retirement across environments. The goal is to ensure that custom AI models remain reliable, compliant, traceable, and adaptable to changes in business data, requirements, and AI technologies.
 
-Custom AI models introduce unique ALM challenges such as data drift, model drift, regulatory alignment, and highimpact deployment risks. This unit provides architects with an actionable framework for governing model evolution from ideation through retirement.
+Custom AI models introduce unique ALM challenges such as data drift, model drift, regulatory alignment, and high-impact deployment risks. This unit provides architects with an actionable framework for governing model evolution from ideation through retirement.
 
 ## 1. ALM Foundations for Custom AI Models
 
 ### A strong ALM process ensures:
 
 **Consistency**: Every model follows documented development, testing, validation, and deployment steps.
 
-**Compliance**: Sensitive data, PII, and industryspecific requirements are protected and governed across model iterations.
+**Compliance**: Sensitive data, PII, and industry-specific requirements are protected and governed across model iterations.
 
 **Repeatability**: Models can be retrained and redeployed predictably, with clear version histories and evaluation criteria.
 
 **Operational Readiness**: Runtime monitoring, governance logs, and rollback plans ensure business resilience.
 
 ## 2. Environment Strategy for Model Development
 
-Establishing a multienvironment design prevents configuration drift and ensures safe promotions.
+Establishing a multi-environment design prevents configuration drift and ensures safe promotions.
 
 ### Recommended Environments
 
@@ -154,11 +154,11 @@ Transparent audit trails for every deployment.
 
 ### Effective monitoring requires:
 
-**Realtime KPIs**: accuracy, latency, cost, throughput, task success.
+**Real-time KPIs**: accuracy, latency, cost, throughput, task success.
 
 **Drift detection**: changes in input data distribution or output quality.
 
-**Safety monitoring**: inappropriate or policyviolating outputs.
+**Safety monitoring**: inappropriate or policy-violating outputs.
 
 **User behavior analysis**: reduction in reprompts, consistent satisfaction trends.
 

learn-pr/wwl/design-alm-process-ai-powered-business-solutions/includes/6-design-alm-process-ai-dynamics-365-apps-finance-supply-chain.md

@@ -1,6 +1,6 @@
 ## Overview
 
-This unit teaches solution architects how to design a complete Application Lifecycle Management (ALM) process for AI components used in Microsoft Dynamics 365 Finance and Dynamics 365 Supply Chain Management.<br>AI capabilities in these applications—such as predictions, anomaly detection, document understanding, knowledge retrieval, and Copilotdriven assistance—require controlled ALM practices to ensure data quality, compliance, security, and operational reliability.
+This unit teaches solution architects how to design a complete Application Lifecycle Management (ALM) process for AI components used in Microsoft Dynamics 365 Finance and Dynamics 365 Supply Chain Management.<br>AI capabilities in these applications—such as predictions, anomaly detection, document understanding, knowledge retrieval, and Copilot-driven assistance—require controlled ALM practices to ensure data quality, compliance, security, and operational reliability.
 
 ### A robust ALM process helps teams
 
@@ -14,7 +14,7 @@ This unit teaches solution architects how to design a complete Application Lifec
 
 ## ALM Foundations for AI in Dynamics 365 Finance and Supply Chain
 
-AI features in these workloads operate across ERP data, process automation, and modeldriven decision logic. Designing ALM requires a layered approach combining:
+AI features in these workloads operate across ERP data, process automation, and model-driven decision logic. Designing ALM requires a layered approach combining:
 
 ### AI Assets to Govern
 
@@ -40,7 +40,7 @@ AI features in these workloads operate across ERP data, process automation, and
 
 * Maintain **repeatable deployment patterns** through managed solutions or deployment pipelines.
 
-* Support **endtoend traceability** across model development, tuning, deployment, and retirement.
+* Support **end-to-end traceability** across model development, tuning, deployment, and retirement.
 
 ## Environment Strategy
 
@@ -49,7 +49,7 @@ AI features in these workloads operate across ERP data, process automation, and
 | **Environment** | **Purpose** |
 |---|---|
 | **DEV** | Build and iterate AI models, prompts, orchestration logic, and integrations. |
-| **TEST** | Validate with safe, anonymized productionlike data. Perform regression checks. |
+| **TEST** | Validate with safe, anonymized production-like data. Perform regression checks. |
 | **PROD** | Execute approved AI capabilities in live financial and supply chain workloads. |
 
 ### Key Requirements
@@ -106,7 +106,7 @@ Define:
 
 * Align terminology with Finance and Supply Chain metadata (e.g., vendor, ledger, work order, production order).
 
-* Validate prompt behavior through scenariobased testing.
+* Validate prompt behavior through scenario-based testing.
 
 ## ALM Promotion Gates
 
@@ -176,7 +176,7 @@ Criteria:
 
 * DLP rules for AI actions
 
-* Guardrails for businesscritical processes (e.g., posting journals, approving purchase orders)
+* Guardrails for business-critical processes (e.g., posting journals, approving purchase orders)
 
 ### Continuous Improvement Loop
 

learn-pr/wwl/design-alm-process-ai-powered-business-solutions/includes/7-design-alm-process-ai-dynamics-365-apps-customer-experience-service.md

@@ -18,11 +18,11 @@ AI capabilities embedded in Dynamics 365 Customer Service and Customer Engagemen
 
 * Provide safe deployment paths with rollback capability
 
-* Support multiapp orchestration (Customer Service, Sales Insights, Customer Insights, Field Service)
+* Support multi-app orchestration (Customer Service, Sales Insights, Customer Insights, Field Service)
 
 * Ensure compliance, responsible AI behavior, and auditability
 
-* Enable continuous improvement with telemetry driven tuning
+* Enable continuous improvement with telemetry-driven tuning
 
 ### AI assets to include in ALM
 
@@ -72,7 +72,7 @@ AI capabilities embedded in Dynamics 365 Customer Service and Customer Engagemen
 
 ### Step 3—Develop and Configure AI Logic
 
-* Build prompts for summarization, classification, reply suggestions, or next best actions.
+* Build prompts for summarization, classification, reply suggestions, or next-best actions.
 
 * Configure Copilot behaviors, action rules, context definitions, and conversation boosters.
 
@@ -82,15 +82,15 @@ AI capabilities embedded in Dynamics 365 Customer Service and Customer Engagemen
 
 * Manage agents, prompts, flows, and data contracts inside solution files.
 
-* Use version controlled repositories to track changes.
+* Use version-controlled repositories to track changes.
 
 * Mark releases with semantic versioning (e.g., v1.3.2).
 
 ### Step 5—Validate Behavior Across Environments
 
 * Evaluate completeness, factual accuracy, tone, and compliance.
 
-* Run multiturn conversation scenarios to detect logic flaws.
+* Run multi-turn conversation scenarios to detect logic flaws.
 
 * Test with real case data (anonymized).
 
@@ -104,7 +104,7 @@ AI capabilities embedded in Dynamics 365 Customer Service and Customer Engagemen
 
 * Deploy to PROD using approved release pipelines (Azure DevOps or GitHub Actions).
 
-* Validate initial postdeployment performance.
+* Validate initial post-deployment performance.
 
 ### Step 7—Monitor, Tune, and Iterate
 
@@ -130,7 +130,7 @@ AI capabilities embedded in Dynamics 365 Customer Service and Customer Engagemen
 
 * Ensure prompts can't trigger disclosure of sensitive customer information.
 
-* Enforce least privilege data access for AI features.
+* Enforce least-privilege data access for AI features.
 
 * Apply data loss prevention (DLP) policies and sensitivity labels across environments.