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Microsoft AI-300 Exam Syllabus Topics:

SectionObjectives
Implement secure and scalable AI systems- Scalability and performance optimization
  • 1. Autoscaling AI workloads
    • 2. Cost optimization strategies
      - Security and governance
      • 1. Identity and access management for AI services
        • 2. Data privacy and compliance considerations
          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
                  Design and implement generative AI solutions- RAG (Retrieval Augmented Generation) solutions
                  • 1. Vector search integration
                    • 2. Knowledge grounding and retrieval design
                      - Large language model integration
                      • 1. Use Azure OpenAI Service capabilities
                        • 2. Prompt engineering and prompt flow design
                          Plan and design AI solutions using Azure AI services- Requirements gathering and solution architecture
                          • 1. Select appropriate Azure AI services
                            • 2. Identify business requirements for AI solutions
                              - Responsible AI design
                              • 1. Responsible AI mitigation strategies
                                • 2. Fairness, transparency, and accountability considerations

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                                  Microsoft Operationalizing Machine Learning and Generative AI Solutions Sample Questions (Q51-Q56):

                                  NEW QUESTION # 51
                                  You create a binary classification model. You use the Fairlearn package to assess model fairness.
                                  You must eliminate the need to retrain the model.
                                  You need to implement the Fairlearn package.
                                  Which algorithm should you use?

                                  Answer: B

                                  Explanation:
                                  The best algorithm to implement within the Fairlearn package for a binary classification model without retraining is the ThresholdOptimizer.
                                  Why ThresholdOptimizer?
                                  This algorithm is a post-processing technique specifically designed to mitigate unfairness after a model has already been trained. By setting the prefit parameter to True, it treats your existing model as a "black box," adjusting its decision thresholds for different groups to satisfy fairness constraints (like demographic parity or equalized odds) without changing the underlying model weights.
                                  Reference:
                                  https://learn.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml


                                  NEW QUESTION # 52
                                  Hotspot Question
                                  A regression model is deployed to a production online endpoint and is actively serving predictions.
                                  Business stakeholders report that the model's prediction quality has changed.
                                  The endpoint remains available and responsive.
                                  You need to determine which metric to examine to validate each quality concern.
                                  Which metric should you review for each concern? To answer, select the appropriate options in the answer area.
                                  NOTE: Each correct selection is worth one point.

                                  Answer:

                                  Explanation:


                                  NEW QUESTION # 53
                                  During training, pipelines occasionally fail due to schema mismatch caused by upstream data changes. You need a robust and automated solution that prevents invalid data from reaching training steps. What is the BEST approach?

                                  Answer: D


                                  NEW QUESTION # 54
                                  You manage an Azure Machine Learning workspace. You have a folder that contains a CSV file.
                                  The folder is registered as a folder data asset.
                                  You plan to use the folder data asset for data wrangling during interactive development.
                                  You need to access and load the folder data asset into a Pandas data frame.
                                  Which method should you use to achieve this goal?

                                  Answer: A

                                  Explanation:
                                  Azure Machine Learning supports a Table type (mltable). This allows for the creation of a blueprint that defines how to load data files into memory as a Pandas or Spark data frame.
                                  Authoring MLTable Files
                                  To directly create the MLTable file, we recommend that you use the mltable Python SDK to author your MLTable files instead of a text editor.
                                  Supported file types
                                  You can create an MLTable using a range of different file types:
                                  * Delimited Text
                                  (for example, CSV files)
                                  from_delimited_files(paths=[path])
                                  Incorrect:
                                  * Parquet
                                  from_parquet_files(paths=[path])
                                  Reference:
                                  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-mltable


                                  NEW QUESTION # 55
                                  You create an Azure Machine Learning workspace.
                                  You must use the Python SDK v2 to implement an experiment from a Jupiter notebook in the workspace. The experiment must log string metrics.
                                  You need to implement the method to log the string metrics.
                                  Which method should you use?

                                  Answer: A


                                  NEW QUESTION # 56
                                  ......

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