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Microsoft Operationalizing Machine Learning and Generative AI Solutions - AI-300 dump torrent

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AI-300 Guide Torrent
  • Exam Code: AI-300
  • Exam Name: Operationalizing Machine Learning and Generative AI Solutions

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Microsoft AI-300 Exam Syllabus Topics:

SectionObjectives
Topic 1: Design and implement generative AI solutions- RAG (Retrieval Augmented Generation) solutions
  • 1. Knowledge grounding and retrieval design
    • 2. Vector search integration
      - Large language model integration
      • 1. Prompt engineering and prompt flow design
        • 2. Use Azure OpenAI Service capabilities
          Topic 2: Operationalizing machine learning solutions- Deployment and monitoring
          • 1. Monitor performance and drift
            • 2. Deploy models to endpoints
              - ML lifecycle management
              • 1. Model training and evaluation in Azure Machine Learning
                • 2. Model versioning and registry usage
                  Topic 3: Implement secure and scalable AI systems- Security and governance
                  • 1. Data privacy and compliance considerations
                    • 2. Identity and access management for AI services
                      - Scalability and performance optimization
                      • 1. Autoscaling AI workloads
                        • 2. Cost optimization strategies
                          Topic 4: Plan and design AI solutions using Azure AI services- Requirements gathering and solution architecture
                          • 1. Identify business requirements for AI solutions
                            • 2. Select appropriate Azure AI services
                              - Responsible AI design
                              • 1. Fairness, transparency, and accountability considerations
                                • 2. Responsible AI mitigation strategies

                                  Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions:

                                  1. A team manages an Azure Machine Learning workspace where they deploy models to online endpoints.
                                  The team needs to introduce a new version of a model to production without disrupting existing users.
                                  The team must validate the new version before full rollout.
                                  You need to reduce risk during deployment.
                                  What should you do?

                                  A) Route all traffic to the new deployment.
                                  B) Replace the existing endpoint.
                                  C) Split traffic between deployments.
                                  D) Deploy the model to a batch endpoint.


                                  2. A financial services company is deploying Microsoft Foundry to host generative AI workloads that process regulated customer data. The Microsoft Foundry environment must prevent any public network exposure while still allowing services managed by Microsoft Foundry to communicate with dependent Azure resources.
                                  Security auditors require that all traffic to and from the Microsoft Foundry resource remain on private networks, with no public endpoints available.
                                  You need to configure the Microsoft Foundry environment so that network access is restricted while maintaining full platform functionality.
                                  Which two actions should you perform? Each correct answer presents part of the solution.
                                  Choose two.
                                  NOTE: Each correct selection is worth one point.

                                  A) Use API key authentication for all model endpoints.
                                  B) Configure a managed virtual network for the Microsoft Foundry resource.
                                  C) Disable public network access to the Microsoft Foundry resource.
                                  D) Disable all inbound network access.
                                  E) Deploy the Microsoft Foundry resource in a separate Azure subscription.


                                  3. You want to ensure ML pipelines produce identical results when executed in different regions or workspaces. Which factor is MOST critical to control for reproducibility?

                                  A) Same dataset name
                                  B) Same compute SKU
                                  C) Versioned datasets and environments
                                  D) Same user identity


                                  4. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
                                  After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
                                  You manage a Retrieval-Augmented Generation (RAG) application built on Microsoft Foundry.
                                  The application retrieves documents from an indexed knowledge base and generates answers for internal users.
                                  Recent feedback indicates that answers are fluent but sometimes include information that is not supported by the documents that were retrieved.
                                  You need to evaluate whether a proposed change improves RAG answer quality by using supported and measurable techniques.
                                  Solution: Compare embedding vector dimensions used by the retrieval pipeline before and after the change.
                                  Does the solution meet the goal?

                                  A) Yes
                                  B) No


                                  5. Drag and Drop Question
                                  A team manages prompts that are used by a generative AI application built on Microsoft Foundry.
                                  Multiple developers contribute prompt updates, and changes must be reviewed and tracked over time.
                                  The team requires that:
                                  - Prompt changes are reviewed before being applied to the version in
                                  production.
                                  - Previous prompt versions can be restored if issues occur.
                                  - Prompt updates follow the same governance practices as the
                                  application code.
                                  You need to implement a controlled process for managing and updating prompts in production.
                                  How should you manage prompt updates to meet the requirements? To answer, move the appropriate actions to the correct requirements. You may use each action once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
                                  NOTE: Each correct selection is worth one point.


                                  Solutions:

                                  Question # 1
                                  Answer: C
                                  Question # 2
                                  Answer: B,D
                                  Question # 3
                                  Answer: C
                                  Question # 4
                                  Answer: B
                                  Question # 5
                                  Answer: Only visible for members

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