Official Exam Details: (AB-100)
AB-100
70%
127 Verified Questions
120 Minutes
Exam Format & Question Types
This certification exam features multiple-choice questions (single and multi-select), interactive Hotspots, Hot Areas, and Drag-and-Drop scenario configurations designed to test real-world technical competency.
AB-100: Cloud AI & Data Architecture Professional – Study Guide & Master Blueprint
Welcome, aspiring Cloud AI & Data Architecture Professionals! This comprehensive study guide and architectural master blueprint is designed to equip you with the knowledge and strategies required to conquer the AB-100 certification exam. This credential validates your expertise in designing, implementing, and optimizing robust, scalable, and secure cloud-native solutions for artificial intelligence, machine learning, and big data workloads. Mastering AB-100 signifies your ability to translate complex business requirements into tangible, high-performance cloud architectures.
1. Exam Overview & Profile
- Target Audience: Cloud Architects, Data Scientists, Machine Learning Engineers, Solutions Architects, and DevOps Professionals with hands-on experience in designing and deploying AI/ML and data processing solutions on cloud platforms. Candidates should possess a strong understanding of core cloud services and AI/ML lifecycle management.
- Question Count: Approximately 55-65 scenario-based questions.
- Duration: 120 minutes (plus 30 minutes for non-native English speakers, if applicable).
- Passing Score Context: While the exact passing score varies by exam version, candidates typically need to achieve a score of 700 out of 1000 to pass. This score reflects a strong proficiency across all exam domains.
2. Deep-Dive Domain Breakdown
Domain 1: Core AI/ML Cloud Service Integration (25%)
- Managed ML Platforms: Understand the capabilities of fully managed ML services for model training, deployment, and MLOps. Focus on feature stores, experiment tracking, model registries, and endpoint management.
- Pre-trained AI Services: Architect solutions leveraging Vision AI, Natural Language Processing (NLP), Speech-to-Text, and Time Series Forecasting services for rapid application development.
- Compute & Accelerator Selection: Differentiate between GPU, TPU, and custom accelerator instances for various training and inference workloads. Optimize cost and performance based on model complexity and data volume.
Domain 2: Data Engineering & Storage for AI (30%)
- Big Data Storage: Design data lakes (object storage), data warehouses, and data marts for structured, semi-structured, and unstructured data. Focus on data partitioning, lifecycle policies, and access patterns.
- Data Processing & Orchestration: Implement ETL/ELT pipelines using managed big data processing frameworks (e.g., Spark, Flink) and workflow orchestrators. Understand streaming data ingestion patterns (e.g., Kafka-like services).
- Data Governance & Cataloging: Architect solutions for data discovery, lineage tracking, metadata management, and quality assurance to ensure trusted data for AI models.
Domain 3: AI Model Lifecycle & MLOps (25%)
- Model Training & Optimization: Select appropriate distributed training strategies, hyperparameter tuning services, and data augmentation techniques. Understand model versioning and artifact management.
- MLOps Pipeline Design: Implement CI/CD for ML models, including automated testing, drift detection, and continuous retraining. Focus on reproducibility and traceability.
- Model Deployment & Monitoring: Architect real-time inference endpoints, batch prediction jobs, and edge deployments. Design robust monitoring for model performance, data quality, and explainability (XAI).
Domain 4: Security, Compliance, & Cost Optimization (20%)
- Identity & Access Management (IAM): Implement fine-grained access controls for all AI/Data services, including service accounts, roles, and resource-based policies.
- Data Security & Privacy: Architect solutions for data encryption (at rest and in transit), data masking, tokenization, and secure data sharing. Understand compliance requirements (e.g., GDPR, HIPAA) for AI workloads.
- Network Security: Design secure network architectures for AI/ML pipelines and endpoints using Virtual Private Clouds (VPCs), private endpoints, service controls, and firewalls.
- Cost Management: Optimize cloud spend for compute, storage, and managed AI services using reserved instances, spot instances, auto-scaling, and lifecycle policies.
3. High-Yield Exam Tips & Scenario Traps
- RAG vs. Fine-tuning: A critical distinction. Know when to recommend Retrieval-Augmented Generation (RAG) for incorporating new, external knowledge without retraining vs. fine-tuning for adapting a model’s specific behavior, style, or task capability with labeled data. RAG is generally preferred for dynamic, frequently updating knowledge bases; fine-tuning for specialized domain adaptation.
- Security Boundaries: Always prioritize least privilege and defense-in-depth. Understand network segmentation (VPC subnets, security groups, network ACLs) and private connectivity options (VPC Endpoints, Private Link) for sensitive data and ML endpoints. Public exposure should be a last resort with robust compensating controls.
- Serverless vs. Provisioned: Evaluate the trade-offs. Serverless (e.g., managed inference services, batch processing functions) offers scalability, reduced operational overhead, and pay-per-use, ideal for variable or bursty workloads. Provisioned instances (e.g., dedicated VMs/containers) provide more control, higher performance for specific hardware, and consistent baselines, suited for long-running, resource-intensive training.
- Data Locality & Residency: Consider the impact of data movement on latency, cost, and compliance. Design architectures that minimize cross-region or cross-cloud data transfers where possible.
- Cost Optimization First: In any architectural decision, always consider the cost implications. Look for opportunities to leverage cheaper storage tiers, instance types, and automated scaling.
4. Frequently Asked Questions (FAQ)
Q1: What is the best strategy to prepare for AB-100?
A1: A multi-faceted approach is most effective. Start with hands-on experience building AI/ML and data pipelines on a cloud platform. Supplement this with official documentation, whitepapers, and solution architectures. Utilize practice exams to identify knowledge gaps and reinforce concepts. Focus heavily on scenario-based questions that require applying architectural best practices.
Q2: What kind of questions can I expect on the AB-100 exam?
A2: The exam primarily features multiple-choice and multiple-select questions presented within realistic architectural scenarios. You will be asked to choose the most appropriate service, design pattern, or optimization strategy given specific requirements related to performance, cost, security, scalability, and operational efficiency. Expect questions that test your ability to weigh trade-offs between different solutions.
Q3: Is coding knowledge required for AB-100?
A3: While AB-100 is an architectural exam, a conceptual understanding of how data scientists and ML engineers interact with cloud services via SDKs, APIs, and frameworks (e.g., TensorFlow, PyTorch, Spark) is highly beneficial. You won’t be writing code, but understanding the implications of different programming paradigms or model types on your architecture is crucial.
Q4: How important are “Cloud-agnostic” principles versus specific cloud provider implementations?
A4: AB-100 emphasizes core architectural principles for AI/Data that are broadly applicable across leading cloud providers. While you won’t be tested on the specific naming conventions of every service from every cloud, understanding the underlying patterns (e.g., managed object storage vs. block storage, streaming data ingestion, managed Kubernetes for ML) is essential. Focus on the “why” and “how” of architectural choices, which transcends specific vendor implementations.
Free AB-100 Sample Practice Questions
Test your knowledge with these sample scenario questions complete with verified explanations:
General Practice Questions
QUESTION 1 [HOT_AREA]
A company has a Microsoft Power Platform environment.
You need to build two agents named Agent1 and Agent2. The solution must meet the following requirements:
* Agent1 must be extendable by using the Semantic Kernel and must connect to multiple business apps and APIs.
* Agent2 must connect directly to data stored in Microsoft Dataverse and must be embeddable in a Microsoft Power Apps canvas app.
What should you use to build each agent? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Click to View Verified Solution & Explanation
✅ **Microsoft Copilot Studio **
Reasoning: Microsoft Copilot Studio offers robust extensibility via Semantic Kernel for advanced agent capabilities and custom code. It supports connecting to diverse business apps and APIs using custom connectors and Power Automate, fulfilling Agent1’s requirements.
✅ **Copilot in Power Apps **
Reasoning: Copilot in Power Apps provides native functionality to interact with Microsoft Dataverse directly. The Copilot control is specifically designed for seamless embedding within Microsoft Power Apps canvas apps, meeting Agent2’s requirements.
QUESTION 2
A company has a Microsoft Copilot Studio agent that uses custom connectors to interact with enterprise APIs.
You need to recommend an application lifecycle management (ALM) process to ensure that the connectors are deployed consistently across development, test, and production environments and meet governance and traceability requirements.
What should you recommend?
- A. Deploy the APIs as Azure Functions.
- B. Export and import the connectors between the environments as unmanaged solutions.
- C. Maintain connector definitions in environment variables.
- D. Manage the connectors as solution components and deploy the components by using ALM pipelines.
Click to View Verified Solution & Explanation
Correct Option: D
Reasoning: Custom connectors are solution-aware components in Power Platform. Managing them within solutions and deploying these solutions using ALM pipelines (e.g., Azure DevOps, GitHub Actions) ensures consistent, automated, and traceable deployments of managed solutions across development, test, and production environments, meeting governance needs. ❌ Why the other choices are incorrect:
- Option A is incorrect: Deploying APIs as Azure Functions addresses the backend implementation, not the ALM process for the custom connector definition within Copilot Studio itself.
- Option B is incorrect: Exporting/importing unmanaged solutions for production ALM is poor practice. It lacks proper version control, auditability, and leads to environment drift, failing governance and consistency requirements. Managed solutions are preferred.
- Option C is incorrect: Environment variables store configuration settings (like API keys or base URLs) that change per environment, not the entire custom connector definition (schema, actions). The definition resides in the solution component.
QUESTION 3 [DRAG_DROP]
A company has a Microsoft Foundry project that uses a single agent and a single prompt to complete a series of tasks.
- The agent encounters the following issues:
- It frequently produces incomplete results.
- It struggles with domain-specific reasoning.
- Agent response times are remarkably slow.
You need to recommend a solution to improve the overall performance and accuracy of the agent.
What should you include in the recommendation? To answer, drag the appropriate actions to the correct requirements. Each action may be used once, more than once, or not at all.
Question Interface:
Click to View Verified Solution & Explanation
✅ **Move to a multi-agent architecture **
Reasoning: A single agent struggles with complex, multi-step tasks, leading to slow response times. A multi-agent system can distribute tasks, specialize agents for different parts of the workflow, and potentially process information in parallel, significantly improving overall task completion performance and efficiency.
✅ **Add a grounding data source **
Reasoning: Grounding data sources (e.g., RAG) provide external, up-to-date, and domain-specific information. This directly addresses incomplete results and poor domain-specific
Reasoning: by supplying the agent with factual context, thereby substantially enhancing the accuracy and completeness of its outputs.
QUESTION 4
A company uses multiple Microsoft Copilot Studio agents across different channels.
You need to recommend a monitoring solution that provides comprehensive telemetry data and performance insights for the agents.
What should you include in the recommendation?
- A. Application Insights
- B. Azure Advisor
- C. Azure DevOps
- D. Microsoft Dynamics 365 Customer Voice
Click to View Verified Solution & Explanation
Correct Option: A
Reasoning: Application Insights, a feature of Azure Monitor, provides comprehensive application performance monitoring (APM). It collects detailed telemetry like conversation logs, topic triggers, sentiment analysis, errors, and performance metrics for Copilot Studio agents. This enables deep insights into agent behavior, usage patterns, and operational health across channels. ❌ Why the other choices are incorrect:
- Option B is incorrect: Azure Advisor offers high-level recommendations for optimizing Azure resources (cost, security, performance) but does not provide granular, real-time telemetry or detailed performance insights for live agent interactions.
- Option C is incorrect: Azure DevOps is a CI/CD and project management platform. While useful for agent development and deployment, it does not offer runtime monitoring, telemetry collection, or or performance analytics for deployed Copilot Studio agents.
- Option D is incorrect: Microsoft Dynamics 365 Customer Voice is a feedback management solution for collecting survey data. It gathers customer satisfaction feedback but does not provide technical telemetry or performance insights into the operational aspects of Copilot Studio agents.
QUESTION 5
You are designing a low-code Al business solution by using Microsoft Copilot Studio.
The solution must include an agent that automates tasks by simulating user interactions across third-party apps and websites, such as clicking
buttons, entering text, and extracting information from screens.
You need to recommend what to include in the agent.
What should you recommend?
- A. Copilot skills
- B. Computer Use in Copilot Studio
- C. a natural language understanding + (NLU+) model in Copilot Studio
- D. Model Context Protocol (MCP)
Click to View Verified Solution & Explanation
Correct Option: B
Reasoning: "Computer Use" refers to Robotic Process Automation (RPA) capabilities, typically integrated via Power Automate Desktop. These enable agents to simulate user interactions like clicking buttons, entering text, and extracting information directly from third-party apps and websites, precisely matching the described automation requirement. ❌ Why the other choices are incorrect:
- Option A is incorrect: Copilot skills extend Copilot’s capabilities through custom functions or APIs for high-level actions and data retrieval, not direct simulation of user interface interactions.
- Option C is incorrect: A natural language understanding (NLU+) model processes and interprets user input, vital for conversational AI, but does not perform the actual automation of simulating UI interactions.
- Option D is incorrect: Model Context Protocol (MCP) is not a recognized or relevant component within Microsoft Copilot Studio or Power Platform for enabling the described task automation via UI simulation.
QUESTION 6 [HOT_AREA]
A company uses Azure OpenAl models that use grounding data from Microsoft Fabric for agents. The models are fine-tuned by using proprietary datasets.
You need to design a governance solution that meets the following requirements:
You need to design a govemance solution that meets the following requirements:
- Restricts access to the grounding data to only assigned roles
- Restricts model fine-tuning to only the Al engineering team
What should you include in the design? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Click to View Verified Solution & Explanation
Reasoning: Microsoft Purview provides unified data governance, enabling the definition and enforcement of access policies on data sources like Microsoft Fabric. This directly addresses restricting access to grounding data to specific assigned roles.
✅ **Role-based access control (RBAC) in Microsoft Foundry **
Reasoning: Azure Role-Based Access Control (RBAC) is the standard authorization system in Azure to manage who has what access to Azure resources. It allows assigning permissions for operations like model fine-tuning to specific security principals (e.g., the AI engineering team).
QUESTION 7
A company has Microsoft Foundry agents that generate responses by using Azure OpenAl resources. The agents are deployed to both the United States and Europe.
A company mandate states that the agents and their grounding data must adhere to data residency and movement regulations.
You need to recommend a govemance solution for the agents.
What should you include in the recommendation?
- A. Microsoft Purview
- B. Azure Monitor
- C. Microsoft Defender for Cloud
- D. Azure Policy
Click to View Verified Solution & Explanation
Correct Option: D
Reasoning: Azure Policy enforces organizational standards and assesses compliance. It can directly control resource deployment locations, ensuring agents and grounding data reside in specific regions (US or Europe) to meet data residency and movement regulations. ❌ Why the other choices are incorrect:
- Option A is incorrect: Microsoft Purview provides data governance, discovery, and classification. While it identifies sensitive data, it doesn’t primarily enforce infrastructure-level data residency rules on resource deployment.
- Option B is incorrect: Azure Monitor focuses on collecting, analyzing, and acting on telemetry for operational health and performance. It is not a governance solution for data residency enforcement.
- Option C is incorrect: Microsoft Defender for Cloud offers cloud security posture management and threat protection. Its primary function is security, not enforcing data residency regulations for resource placement.
QUESTION 8
A company has a Microsoft Dynamics 365 Sales environment that has Microsoft Copilot enabled.
You need to customize Copilot by tailoring how opportunity summaries are generated or how they are presented to users.
Solution: You configure Al Builder lead scoring models to influence opportunity summaries. Does this meet the goal?
- A. Yes
- B. No
Click to View Verified Solution & Explanation
Correct Option: B
Reasoning: AI Builder lead scoring models evaluate the likelihood of a lead converting, operating on the Lead entity. Copilot’s opportunity summaries are generated from data within the Opportunity entity. Lead scoring models do not directly customize or influence the content or generation logic of opportunity summaries. ❌ Why the other choices are incorrect:
- Option A is incorrect: Configuring AI Builder lead scoring models is unrelated to customizing how Copilot generates or presents summaries for opportunities. Lead scoring is a distinct function focused on lead qualification.
QUESTION 9
A company has a canvas app named Appl in a Microsoft Power Platform environment named Envl.
Envl uses a customer-managed key for data encryption. Appl connects to multiple data sources to retrieve and update customer and order
information.
You need to recommend a solution to add Microsoft Copilot components to Appl. The solution must NOT modify the current security or encryption configurations of Envl.
What should you include in the recommendation?
- A. Duplicate Appl and republish the app in Envl.
- B. Move Appl to a new environment that uses Microsoft-managed keys.
- C. Modify the data sources of Appl to make them compatible with Copilot.
- D. Enable Copilot features for Envl.
Click to View Verified Solution & Explanation
Correct Option: B
Reasoning: Microsoft Copilot components typically require environments encrypted with Microsoft-managed keys. Moving Appl to a new environment using Microsoft-managed keys allows Copilot integration without modifying the existing Envl’s customer-managed key (CMK) configuration, fulfilling the constraint. ❌ Why the other choices are incorrect:
- Option A is incorrect: Duplicating Appl within Envl does not change Envl’s CMK encryption, which remains incompatible with Copilot without violating the security constraint.
- Option C is incorrect: Modifying data sources addresses data compatibility, but it does not resolve the fundamental environment-level encryption key requirement for Copilot features.
- Option D is incorrect: Enabling Copilot features directly for Envl would either be blocked due to its CMK configuration or would necessitate modifying Envl’s security/encryption settings, violating the core constraint.
Introduction
This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.
To answer the case study questions, you will need to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study.
A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it.
To start the case study
To display the first question in this case study, select the “Next” button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions. When you are ready to answer a question, select the “Question” button to return to the question.
Background
Fabrikam, Inc., is a global consumer goods company that is undergoing a digital transformation initiative to migrate its entire infrastructure to the Microsoft cloud. As a key element of this cloud migration, the company will implement Microsoft Dynamics 365 Sales, moving away from the current on-premises proprietary technologies used by its business-to-business (B2B) sales team.
As part of the cloud migration, Fabrikam will adopt an AI-first approach to its business solutions and implement AI solutions, wherever possible, to streamline operations.
Problem Statements
Fabrikam’s infrastructure currently relies on various on-premises systems that require sales executives to use corporate computers with physical keyboards to access business information during customer interactions. Mobile phones cannot be used for these purposes, as the systems depend on keyboard input. As a result, the sales executives spend a lot of time using keyboards to search for data on several disparate systems and file servers, rather than focusing on the customers. This affects the customer experience.
Fabrikam stakeholders are concerned that users will be hesitant to adopt AI. If the AI initiatives are NOT adopted, cost savings will never be realized. Additionally, funding for future AI initiatives will depend on demonstrating an increase in AI adoption month over month. As the AI agent initiative for the sales team will be the first for Fabrikam, the rapid adoption of the agent is a high priority.
Planned Initiatives –
General
Fabrikam management has prioritized AI-driven projects to improve efficiency, customer engagement, and responsible AI adoption. The current application infrastructure is on-premises and must be migrated to the cloud to support the adoption of these technologies.
Infrastructure Migration –
Fabrikam plans to migrate from its current on-premises infrastructure to a completely cloud-based topology; this will include user authentication, the security framework, and, primarily, the adoption of the services by end users.
All the data from the different systems will be consolidated into a single data source – a common data model that will use a Microsoft Dataverse environment as a single source of truth (SSOT) for the sales team.
Sales Cycle Enablement
To achieve the company’s objectives, Fabrikam intends to implement the following strategies to enhance the sales cycle:Use low-code development to create a single AI agent that has Dataverse as its core component.
Ensure that sales managers can access unanswered correspondence from prospects and intervene as appropriate. Replace the previous proprietary software with Dynamics 365 Sales to track sales cycles and customer interactions.
Have the sales executives use Dynamics 365 Sales to track interactions for open opportunities and send follow-up communications to prospects.
Have the sales executives use handsfree headsets to interact with an AI agent when they have questions about internal policies or customer data.
Requirements
Infrastructure Migration
Fabrikam has identified the following infrastructure migration requirements:
Azure must be used for all future infrastructure workloads. The company must follow Microsoft-recommended methodologies for infrastructure migration to the cloud.
Any created AI agents must have their return on investment (ROI) calculated to ensure that the solution will save the company money.
Sales Cycle Enablement
Fabrikam has identified the following requirements for sales cycle enablement:
The final AI agent must follow Microsoft recommendations for a conversational user experience.
A designated checklist must be reviewed to ensure that the AI agent follows Microsoft deployment recommendations for a compliant solution.
Detailed telemetry must be logged for the first created AI agent to help troubleshoot and optimize the agent during the initial AI agent adoption process.
Unexpected AI agent actions must end in an escalation to a live representative. For example, a sales executive must be rerouted to a representative if the agent cannot answer a question after two failed attempts.
The return on investment (ROI) of switching from the current process to the future process is required for stakeholder sign off. The sales team must use Dynamics 365 Sales to correspond with prospects more quickly and efficiently than currently.
Sales managers must report on the adoption of the AI agent to key Fabrikam stakeholders on a monthly basis. Any sensitive information, such as user IDs and names, shared via the AI agent must be tracked for future auditing.
QUESTION 10 [HOT_AREA]
Scenario: Fabrikam Inc.
Which framework should you use to meet the AI agent requirements for the sales cycle enablement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Click to View Verified Solution & Explanation
Reasoning: The case study requires the AI agent to follow deployment recommendations for a compliant solution and track sensitive information for auditing. The ALM Accelerator for Microsoft Power Platform provides tools and processes for effective Application Lifecycle Management, crucial for controlled deployments, governance, and meeting compliance best practices for Copilot Studio.
✅ **Success by Design **
Reasoning: Fabrikam requires the AI agent to follow recommendations for a conversational user experience and prioritizes rapid adoption due to user hesitancy. Success by Design is a Microsoft methodology that provides prescriptive guidance for designing and delivering Power Platform solutions that drive adoption and ensure a positive, effective user experience, directly addressing the conversational UX and adoption needs.
General Practice Questions
QUESTION 11
A company has an AI business solution.
You need to extend the solution so that Microsoft 365 Copilot can invoke external logic hosted in Azure services.
What should you include in the solution?
- A. Microsoft Copilot Studio skills
- B. Microsoft Power Platform connectors
- C. custom engine agents
Click to View Verified Solution & Explanation
Correct Option: B
✅ **Microsoft Power Platform connectors **
Reasoning: Microsoft Power Platform connectors are the standard, direct mechanism for M365 Copilot plugins (built via Copilot Studio) to connect with and invoke external logic hosted in Azure services. They provide pre-built or custom integration with various APIs. ❌ Why the other choices are incorrect:
- Option A is incorrect: Microsoft Copilot Studio “skills” (now often referred to as plugins or topics) define what Copilot can do. However, these skills utilize Power Platform connectors to actually establish the connection and invoke the external logic.
- Option C is incorrect: “Custom engine agents” is a vague term not specific to the designated method for M365 Copilot to directly integrate and invoke external Azure services. Power Platform connectors are the established integration pathway.
Reference: https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/extend-m365-copilot
QUESTION 12 [HOT_AREA]
You need to design a multi-agent solution that will include a custom agent. The solution must meet the following requirements:
- Define the rules and constraints that the agent must follow.
- Automate a backend process that involves data movement between services and runs independently of the agent’s reasoning steps.
What should you include in the design for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Click to View Verified Solution & Explanation
✅ **topics **
Reasoning: For an agent, especially a conversational agent, “Conversation topics” define the specific rules, triggers, and logical flows for how the agent interacts with users. These topics establish the boundaries and constraints of the agent’s conversational capabilities and behavior.
✅ **Power Automate cloud flow **
Reasoning: Microsoft Power Automate cloud flows are the ideal tool for automating backend processes, integrating different services, and moving data. They can be invoked by an agent but execute independently, fulfilling the requirement for a separate, automated backend process.
QUESTION 13
A company plans to deploy a Microsoft Copilot Studio agent that will analyze historical business data to predict customer behavior.
The data is currently stored in an Azure SQL database, flat files, APIs, and logs.
You need to organize the data into a format that can be used as a knowledge source in Copilot Studio.
What should you include in the solution?
- A. Azure AI Search
- B. Azure Data Lake Storage
- C. Azure Cosmos DB
- D. Azure Translator in Foundry Tools
Click to View Verified Solution & Explanation
Correct Option: A
✅ **Azure AI Search **
Reasoning: Azure AI Search indexes diverse data (Azure SQL, flat files, APIs, logs) into a unified, searchable knowledge source. It enables semantic search and retrieval, essential for Copilot Studio agents to access relevant information for analyzing historical data and predicting customer behavior. ❌ Why the other choices are incorrect:
- Option B is incorrect: Azure Data Lake Storage stores raw data efficiently but lacks the indexing, search, and retrieval capabilities (like semantic search) needed for a knowledge source directly consumable by an AI agent.
- Option C is incorrect: Azure Cosmos DB is a NoSQL database for transactional workloads. It doesn’t inherently provide the advanced indexing and search features required for an AI agent’s knowledge source to analyze diverse data types for predictions.
- Option D is incorrect: Azure Translator focuses on language translation. “Foundry Tools” is not a standard Azure service for organizing data into a knowledge source for AI agents. This option is irrelevant to the problem.
QUESTION 14
A company has a Microsoft Copilot Studio agent that uses custom connectors to interact with enterprise APIs.
You need to recommend an application lifecycle management (ALM) process to ensure that the connectors are deployed consistently across development, test, and production environments and meet governance and traceability requirements.
What should you recommend?
- A. Deploy the APIs as Azure Functions.
- B. Manage the connectors as solution components and deploy the components by using ALM pipelines.
- C. Maintain connector definitions in environment variables.
- D. Export and import the connectors between the environments as unmanaged solutions.
Click to View Verified Solution & Explanation
Correct Option: B
Reasoning: Custom connectors are Power Platform solution components. ALM pipelines (e.g., Azure DevOps, GitHub Actions) automate the deployment of these solutions across development, test, and production environments. This ensures consistent configuration, version control, traceability, and robust governance, which are crucial for reliable application lifecycle management. ❌ Why the other choices are incorrect:
- Option A is incorrect: Deploying APIs as Azure Functions addresses the backend service, not the ALM process for the custom connectors themselves within Copilot Studio. The question focuses on connector deployment consistency and governance.
- Option C is incorrect: Environment variables are used for managing configuration parameters (like base URLs or API keys) within connectors across environments. They do not define or manage the entire custom connector’s structure or its ALM process.
- Option D is incorrect: Exporting and importing unmanaged solutions manually is a poor ALM practice. Unmanaged solutions allow direct modification in target environments, breaking consistency, traceability, and proper version control. ALM pipelines typically deploy managed solutions for production.
QUESTION 15
A company has Microsoft Foundry agents that generate responses by using Azure OpenAI resources. The agents are deployed to both the United States and Europe.
A company mandate states that the agents and their grounding data must adhere to data residency and movement regulations.
You need to recommend a governance solution for the agents.
What should you include in the recommendation?
- A. Microsoft Defender for Cloud
- B. Azure Policy
- C. Azure Monitor
- D. Microsoft Purview
Click to View Verified Solution & Explanation
Correct Option: B
Reasoning: Azure Policy allows defining rules that restrict resource creation or deployment to specific geographic regions. This directly enforces data residency and movement regulations for Azure OpenAI resources and associated grounding data used by agents, ensuring compliance at scale across US and European deployments. ❌ Why the other choices are incorrect:
- Option A is incorrect: Microsoft Defender for Cloud focuses on security posture management and threat protection, not primarily on enforcing data residency rules for resource deployment.
- Option C is incorrect: Azure Monitor is for collecting and analyzing telemetry, providing operational visibility. It doesn’t enforce resource placement or data movement rules for compliance.
- Option D is incorrect: Microsoft Purview provides data governance, discovery, and classification. While it identifies sensitive data, it doesn’t directly enforce the physical location of Azure resources to meet residency mandates.
Introduction
This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.
To answer the case study questions, you will need to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study.
A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it.
To start the case study
To display the first question in this case study, select the “Next” button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions. When you are ready to answer a question, select the “Question” button to return to the question.
Background
Fabrikam, Inc., is a global consumer goods company that is undergoing a digital transformation initiative to migrate its entire infrastructure to the Microsoft cloud. As a key element of this cloud migration, the company will implement Microsoft Dynamics 365 Sales, moving away from the current on-premises proprietary technologies used by its business-to-business (B2B) sales team.
As part of the cloud migration, Fabrikam will adopt an AI-first approach to its business solutions and implement AI solutions, wherever possible, to streamline operations.
Problem Statements
Fabrikam’s infrastructure currently relies on various on-premises systems that require sales executives to use corporate computers with physical keyboards to access business information during customer interactions. Mobile phones cannot be used for these purposes, as the systems depend on keyboard input. As a result, the sales executives spend a lot of time using keyboards to search for data on several disparate systems and file servers, rather than focusing on the customers. This affects the customer experience.
Fabrikam stakeholders are concerned that users will be hesitant to adopt AI. If the AI initiatives are NOT adopted, cost savings will never be realized. Additionally, funding for future AI initiatives will depend on demonstrating an increase in AI adoption month over month. As the AI agent initiative for the sales team will be the first for Fabrikam, the rapid adoption of the agent is a high priority.
Planned Initiatives –
General
Fabrikam management has prioritized AI-driven projects to improve efficiency, customer engagement, and responsible AI adoption. The current application infrastructure is on-premises and must be migrated to the cloud to support the adoption of these technologies.
Infrastructure Migration –
Fabrikam plans to migrate from its current on-premises infrastructure to a completely cloud-based topology; this will include user authentication, the security framework, and, primarily, the adoption of the services by end users.
All the data from the different systems will be consolidated into a single data source – a common data model that will use a Microsoft Dataverse environment as a single source of truth (SSOT) for the sales team.
Sales Cycle Enablement
To achieve the company’s objectives, Fabrikam intends to implement the following strategies to enhance the sales cycle:Use low-code development to create a single AI agent that has Dataverse as its core component.
Ensure that sales managers can access unanswered correspondence from prospects and intervene as appropriate. Replace the previous proprietary software with Dynamics 365 Sales to track sales cycles and customer interactions.
Have the sales executives use Dynamics 365 Sales to track interactions for open opportunities and send follow-up communications to prospects.
Have the sales executives use handsfree headsets to interact with an AI agent when they have questions about internal policies or customer data.
Requirements
Infrastructure Migration
Fabrikam has identified the following infrastructure migration requirements:
Azure must be used for all future infrastructure workloads. The company must follow Microsoft-recommended methodologies for infrastructure migration to the cloud.
Any created AI agents must have their return on investment (ROI) calculated to ensure that the solution will save the company money.
Sales Cycle Enablement
Fabrikam has identified the following requirements for sales cycle enablement:
The final AI agent must follow Microsoft recommendations for a conversational user experience.
A designated checklist must be reviewed to ensure that the AI agent follows Microsoft deployment recommendations for a compliant solution.
Detailed telemetry must be logged for the first created AI agent to help troubleshoot and optimize the agent during the initial AI agent adoption process.
Unexpected AI agent actions must end in an escalation to a live representative. For example, a sales executive must be rerouted to a representative if the agent cannot answer a question after two failed attempts.
The return on investment (ROI) of switching from the current process to the future process is required for stakeholder sign off. The sales team must use Dynamics 365 Sales to correspond with prospects more quickly and efficiently than currently.
Sales managers must report on the adoption of the AI agent to key Fabrikam stakeholders on a monthly basis. Any sensitive information, such as user IDs and names, shared via the AI agent must be tracked for future auditing.
QUESTION 16
Scenario: Fabrikam Inc.
Which tool should you recommend to address the sensitive information concerns in the sales process?
- A. the Analytics tab in Microsoft Copilot Studio
- B. Model Context Protocol (MCP)
- C. Application Insights
- D. Microsoft Foundry Tracing UI
- E. Monitoring in Microsoft Foundry
Click to View Verified Solution & Explanation
Correct Option: C
✅
Reasoning: Application Insights, part of Azure Monitor, collects application telemetry including custom events and logs. It allows developers to instrument code to specifically track and log interactions with sensitive data, providing granular visibility and audit trails necessary to address sensitive information concerns within the sales process applications. ❌ Why the other choices are incorrect:
- Option A is incorrect: The Analytics tab in Microsoft Copilot Studio focuses on bot performance, usage, and user engagement, not comprehensive sensitive data monitoring across an entire sales process application.
- Option B is incorrect: Model Context Protocol (MCP) is a conceptual framework or specification for managing AI model context, not a specific tool for monitoring sensitive information.
- Option D is incorrect: Microsoft Foundry Tracing UI is primarily for debugging and understanding data flow within the Microsoft Foundry platform, not a general solution for sensitive information concerns in a broader sales process.
- Option E is incorrect: Monitoring in Microsoft Foundry focuses on the health and performance of processes within the Foundry platform itself, rather than broad application-level sensitive data auditing.
Reference: https://learn.microsoft.com/en-us/azure/azure-monitor/app/app-insights-overview
QUESTION 17 [HOT_AREA]
Scenario: Fabrikam Inc.
You are designing an AI strategy for Microsoft Dynamics 365 finance and operations apps. You are evaluating the use of Microsoft Copilot Studio to provide in-app help and guidance based on generative AI general knowledge.
You need to recommend which knowledge sources to include in the generative help and guidance agent. The solution must minimize the risk of generating inaccurate responses.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Click to View Verified Solution & Explanation
Reasoning: To provide accurate, domain-specific in-app help for Microsoft Dynamics 365 finance and operations apps, the agent must have access to specific, validated information about the application. Uploading custom knowledge sources (e.g., official documentation, internal guides) ensures the generative AI is grounded in relevant and correct data, directly minimizing the risk of inaccurate responses.
✅ AI general knowledge: Must be enabled for the agent
Reasoning: The problem statement specifies the agent will provide help "based on generative AI general knowledge." This implies leveraging the core capabilities of a generative AI model, which inherently includes broad general knowledge. To utilize this generative AI functionality at all, it must be enabled. While raw general knowledge can be inaccurate for specific tasks, its enablement is necessary for the agent type described. Accuracy is then managed by grounding this enabled generative AI with custom, uploaded knowledge.
General Practice Questions
QUESTION 18
A company has an AI solution that uses Azure OpenAI models.
You need to recommend a governance solution that monitors and audits changes to model configurations and data usage. The solution must minimize administrative effort.
What should you include in the recommendation?
- A. Azure Monitor
- B. Azure Stream Analytics
- C. Azure API Management
- D. Azure Policy
- E. Microsoft Purview
Click to View Verified Solution & Explanation
Correct Option: E
Reasoning: Microsoft Purview is a unified data governance solution offering data discovery, classification, lineage, and auditing. It effectively monitors and audits data usage and changes to data-related configurations, crucial for an AI solution, while minimizing administrative effort through a centralized platform. ❌ Why the other choices are incorrect:
- Option A is incorrect: Azure Monitor focuses on operational metrics, logs, and alerts for infrastructure and applications, not comprehensive data governance or auditing of data usage from a compliance perspective.
- Option B is incorrect: Azure Stream Analytics is a real-time analytics engine for processing data streams, not a governance solution for auditing configurations or historical data usage.
- Option C is incorrect: Azure API Management manages API access and usage, but it is not a holistic governance solution for model configurations or data usage across the entire data estate.
- Option D is incorrect: Azure Policy enforces organizational standards and assesses compliance for Azure resources and configurations. While it can audit configuration changes, it lacks the comprehensive data governance and data usage auditing capabilities of Purview.
QUESTION 19 (Multi-Select)
A company has Microsoft 365 Copilot agents.
You need to design a security solution for the agents. The solution must meet the following requirements:
Identify and mitigate potential risks that relate to AI use.
Protect AI apps and the sensitive data processed or generated by the agents.
Support responsible AI governance by retaining and logging interactions, detecting policy violations, and investigating incidents.
Which two components should you include in the design? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. Microsoft Purview
- B. Azure AI Content Safety
- C. role-based access control (RBAC) in Microsoft Foundry
- D. Microsoft Defender
Click to View Verified Solution & Explanation
Correct Option: A,D
Reasoning: Microsoft Purview provides critical capabilities for data governance, information protection (MIP, DLP), and compliance (Audit, Communication Compliance, eDiscovery). It protects sensitive data processed by AI agents, facilitates retaining and logging interactions, detects policy violations, and supports investigations by enforcing data policies and recording activities, directly meeting governance and data protection requirements.
✅ **Microsoft Defender **
Reasoning: Microsoft Defender offers comprehensive threat protection across Microsoft 365 services, endpoints, identities, and cloud apps (e.g., Defender for Cloud Apps). It identifies and mitigates security risks, protects AI apps from threats, detects anomalies, and supports incident investigation by monitoring suspicious activities and securing the environment where AI agents operate, fulfilling risk mitigation and app protection needs. ❌ Why the other choices are incorrect:
- Option B is incorrect: Azure AI Content Safety primarily focuses on identifying and filtering harmful content in AI inputs/outputs. While important for responsible AI, it does not provide the broad data protection, logging, and comprehensive incident investigation capabilities for general policy violations and security incidents that Purview or Defender offer.
- Option C is incorrect: Role-based access control (RBAC) is a fundamental security principle. However, "Microsoft Foundry" is a data industrial platform and not directly relevant to securing Microsoft 365 Copilot agents within the specified context. The question implies an M365-centric solution.
QUESTION 20
You are designing a Microsoft Copilot Studio agent that uses a custom Microsoft Foundry model to generate responses.
You need to ensure that the agent can securely connect to and invoke the custom model during user interactions.
What should you include in the design?
- A. Configure the agent to use classic orchestration.
- B. Create a connection to Microsoft Foundry in the agent.
- C. Add the Microsoft Foundry model as a Copilot Studio skill.
- D. Create a custom engine agent.
Click to View Verified Solution & Explanation
Correct Option: B
Reasoning: To securely invoke an external custom model, Copilot Studio agents require a defined connection. Creating a connection to Microsoft Foundry enables authentication and direct API calls, allowing the agent to securely send requests and receive responses from the custom model during interactions. ❌ Why the other choices are incorrect:
- Option A is incorrect: Classic orchestration defines topic flow and interaction logic within Copilot Studio, but it does not establish the fundamental connection mechanism to external systems or custom models.
- Option C is incorrect: While Copilot Studio can use "skills" for complex integrations, the most direct and common method for invoking a custom model hosted on an external platform is typically through a dedicated connection (e.g., custom connector, HTTP action) for secure API interaction, not abstracting it as a "skill."
- Option D is incorrect: "Custom engine agent" is not a recognized feature or design component within Microsoft Copilot Studio for integrating external models. Copilot Studio itself provides the agent engine; external integrations are managed via connections, plugins, or skills.
QUESTION 21 (Multi-Select)
A company has an AI solution built by using Microsoft Copilot Studio and Power Platform. The solution is used by the company’s sales, marketing, and customer service teams.
You are performing a return on AI investment (ROAI) analysis to evaluate the impact of the solution.
You need to identify which measurable business drivers to include in the analysis.
Which two business drivers should you identify? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
- A. the reduced average case resolution time
- B. market capitalization
- C. economic market predictability
- D. increased employee productivity
- E. brand awareness
Click to View Verified Solution & Explanation
Correct Option: A,D
✅ **the reduced average case resolution time **
Reasoning: Copilot Studio directly enables faster customer service interactions and self-service, making reduced average case resolution time a primary, measurable ROAI driver for the customer service team.
✅ **increased employee productivity **
Reasoning: Power Platform and Copilot Studio automate tasks and streamline workflows across sales, marketing, and customer service teams, leading to a direct and measurable increase in overall employee productivity. ❌ Why the other choices are incorrect:
- Option B is incorrect: Market capitalization is a broad financial metric, too indirect and influenced by too many external factors to be a direct, attributable business driver for this specific AI solution’s ROAI analysis.
- Option C is incorrect: Economic market predictability is an external macroeconomic factor, not a direct measurable impact or driver of an internal AI solution’s operational performance.
- Option E is incorrect: While the solution can indirectly support marketing, brand awareness is a broader marketing outcome, not a direct or primary measurable business driver of the AI solution itself compared to operational efficiency gains.
QUESTION 22 [HOT_AREA]
You are designing a testing solution for a Microsoft Copilot Studio agent that integrates with Microsoft Dynamics 365 Customer Service and Dynamics 365 Sales.
You need to design end-to-end scenarios to test the agent’s ability to perform the following actions:
Coordinate tasks and data interactions across both Dynamics 365 apps.
Interpret user input and provide contextually relevant outputs.
Which test scenario and metric should you include in the design? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Click to View Verified Solution & Explanation
Reasoning: The goal is to test the agent’s ability to "Coordinate tasks and data interactions across both Dynamics 365 apps." An end-to-end test must involve scenarios that span both applications to validate this core integration and coordination functionality.
✅ **Track the successful completion of cross-app tasks. **
Reasoning: This metric directly measures whether the agent successfully achieved the specified cross-application tasks, thus validating its coordination capabilities and its effectiveness in interpreting user input and providing relevant outputs to complete multi-app workflows.
QUESTION 23
You are creating validation criteria for a custom generative AI model that produces business reports based on internal enterprise data.
You need to assess whether the model’s outputs are appropriate and meaningful for the business reports.
Which metric should you use?
- A. the number of active users interacting with the model
- B. alignment of the output to domain-specific tasks
- C. the average system resource usage during inference
- D. the model training duration
Click to View Verified Solution & Explanation
Correct Option: B
Reasoning: Assessing "alignment of the output to domain-specific tasks" directly evaluates if the generative AI model’s reports are relevant, accurate, and suitable for their intended business purpose, thus confirming their appropriateness and meaningfulness. ❌ Why the other choices are incorrect:
- Option A is incorrect: The number of active users is a usage metric, not an indicator of the quality, appropriateness, or meaningfulness of the model’s generated content.
- Option C is incorrect: Average system resource usage is an operational/performance metric related to infrastructure cost and efficiency, not the semantic quality or relevance of the AI’s output.
- Option D is incorrect: Model training duration is a development metric that doesn’t directly measure the appropriateness or meaningfulness of the generated business reports.
Introduction
This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.
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Background
Fabrikam, Inc., is a global consumer goods company that is undergoing a digital transformation initiative to migrate its entire infrastructure to the Microsoft cloud. As a key element of this cloud migration, the company will implement Microsoft Dynamics 365 Sales, moving away from the current on-premises proprietary technologies used by its business-to-business (B2B) sales team.
As part of the cloud migration, Fabrikam will adopt an AI-first approach to its business solutions and implement AI solutions, wherever possible, to streamline operations.
Problem Statements
Fabrikam’s infrastructure currently relies on various on-premises systems that require sales executives to use corporate computers with physical keyboards to access business information during customer interactions. Mobile phones cannot be used for these purposes, as the systems depend on keyboard input. As a result, the sales executives spend a lot of time using keyboards to search for data on several disparate systems and file servers, rather than focusing on the customers. This affects the customer experience.
Fabrikam stakeholders are concerned that users will be hesitant to adopt AI. If the AI initiatives are NOT adopted, cost savings will never be realized. Additionally, funding for future AI initiatives will depend on demonstrating an increase in AI adoption month over month. As the AI agent initiative for the sales team will be the first for Fabrikam, the rapid adoption of the agent is a high priority.
Planned Initiatives –
General
Fabrikam management has prioritized AI-driven projects to improve efficiency, customer engagement, and responsible AI adoption. The current application infrastructure is on-premises and must be migrated to the cloud to support the adoption of these technologies.
Infrastructure Migration –
Fabrikam plans to migrate from its current on-premises infrastructure to a completely cloud-based topology; this will include user authentication, the security framework, and, primarily, the adoption of the services by end users.
All the data from the different systems will be consolidated into a single data source – a common data model that will use a Microsoft Dataverse environment as a single source of truth (SSOT) for the sales team.
Sales Cycle Enablement
To achieve the company’s objectives, Fabrikam intends to implement the following strategies to enhance the sales cycle:Use low-code development to create a single AI agent that has Dataverse as its core component.
Ensure that sales managers can access unanswered correspondence from prospects and intervene as appropriate. Replace the previous proprietary software with Dynamics 365 Sales to track sales cycles and customer interactions.
Have the sales executives use Dynamics 365 Sales to track interactions for open opportunities and send follow-up communications to prospects.
Have the sales executives use handsfree headsets to interact with an AI agent when they have questions about internal policies or customer data.
Requirements
Infrastructure Migration
Fabrikam has identified the following infrastructure migration requirements:
Azure must be used for all future infrastructure workloads. The company must follow Microsoft-recommended methodologies for infrastructure migration to the cloud.
Any created AI agents must have their return on investment (ROI) calculated to ensure that the solution will save the company money.
Sales Cycle Enablement
Fabrikam has identified the following requirements for sales cycle enablement:
The final AI agent must follow Microsoft recommendations for a conversational user experience.
A designated checklist must be reviewed to ensure that the AI agent follows Microsoft deployment recommendations for a compliant solution.
Detailed telemetry must be logged for the first created AI agent to help troubleshoot and optimize the agent during the initial AI agent adoption process.
Unexpected AI agent actions must end in an escalation to a live representative. For example, a sales executive must be rerouted to a representative if the agent cannot answer a question after two failed attempts.
The return on investment (ROI) of switching from the current process to the future process is required for stakeholder sign off. The sales team must use Dynamics 365 Sales to correspond with prospects more quickly and efficiently than currently.
Sales managers must report on the adoption of the AI agent to key Fabrikam stakeholders on a monthly basis. Any sensitive information, such as user IDs and names, shared via the AI agent must be tracked for future auditing.
QUESTION 24 [HOT_AREA]
Scenario: Fabrikam Inc.
Which components should you use to meet the sales cycle enablement requirements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Click to View Verified Solution & Explanation
Reasoning: The case study requires a low-code platform for AI agent creation with Dataverse as its core and a conversational user experience. Microsoft Copilot Studio (formerly Power Virtual Agents) perfectly meets these low-code conversational AI requirements.
✅ a Fallback topic
Reasoning: The requirement states that "unexpected AI agent actions must end in an escalation to a live representative." In conversational AI platforms like Copilot Studio, a Fallback topic is specifically designed to handle ununderstood user input and facilitate escalation to human agents.
QUESTION 25 [HOT_AREA]
Scenario: Fabrikam Inc.
Which existing tool and data should you use to gather the required metrics for stakeholder signoff for the AI agents? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Click to View Verified Solution & Explanation
Reasoning: Microsoft Copilot Studio (formerly Power Virtual Agents) is the primary tool for building, deploying, and managing conversational AI agents. It provides built-in analytics, reporting, and telemetry crucial for monitoring AI agent adoption, usage, and performance, which is essential for stakeholder sign-off and optimization.
✅ **the current time to complete the task today per instance **
Reasoning: To calculate the Return on Investment (ROI) and demonstrate efficiency gains for stakeholder sign-off, a baseline metric is needed. The case study highlights sales executives spending "a lot of time" searching for data and aims for them to correspond "more quickly and efficiently." Measuring the current time per task provides this crucial "before" metric to compare against post-AI agent implementation.
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