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In order to set realistic expectations, please note: These questions are NOT official questions that you will find on the official exam. These questions DO cover all the material outlined in the knowledge sections below. Many of the questions are based on fictitious scenarios which have questions posed within them.The official knowledge requirements for the exam are reviewed routinely to ensure that the content has the latest requirements incorporated in the practice questions. Updates to content are often made without prior notification and are subject to change at any time.Each question has a detailed explanation and links to reference materials to support the answers which ensures accuracy of the problem solutions.The questions will be shuffled each time you repeat the tests so you will need to know why an answer is correct, not just that the correct answer was item "B" last time you went through the test.NOTE: This course should not be your only study material to prepare for the official exam. These practice tests are meant to supplement topic study material.Should you encounter content which needs attention, please send a message with a screenshot of the content that needs attention and I will be reviewed promptly. Providing the test and question number do not identify questions as the questions rotate each time they are run. The question numbers are different for everyone.Audience profileAs a candidate for this exam, you should have subject matter expertise in applying data science and machine learning to implement and run machine learning workloads on Azure. Additionally, you should have knowledge of optimizing language models for AI applications using Azure AI.Your responsibilities for this role include:Designing and creating a suitable working environment for data science workloads.Exploring data.Training machine learning models.Implementing pipelines.Running jobs to prepare for production.Managing, deploying, and monitoring scalable machine learning solutions.Using language models for building AI applications.As a candidate for this exam, you should have knowledge and experience in data science by using:Azure Machine LearningMLflowAzure AI services, including Azure AI SearchAzure AI FoundrySkills at a glanceDesign and prepare a machine learning solution (20-25%)Explore data, and run experiments (20-25%)Train and deploy models (25-30%)Optimize language models for AI applications (25-30%)Design and prepare a machine learning solution (20-25%)Design a machine learning solutionIdentify the structure and format for datasetsDetermine the compute specifications for machine learning workloadSelect the development approach to train a modelCreate and manage resources in an Azure Machine Learning workspaceCreate and manage a workspaceCreate and manage datastoresCreate and manage compute targetsSet up Git integration for source controlCreate and manage assets in an Azure Machine Learning workspaceCreate and manage data assetsCreate and manage environmentsShare assets across workspaces by using registriesExplore data, and run experiments (20-25%)Use automated machine learning to explore optimal modelsUse automated machine learning for tabular dataUse automated machine learning for computer visionUse automated machine learning for natural language processingSelect and understand training options, including preprocessing and algorithmsEvaluate an automated machine learning run, including responsible AI guidelinesUse notebooks for custom model trainingUse the terminal to configure a compute instanceAccess and wrangle data in notebooksWrangle data interactively with attached Synapse Spark pools and serverless Spark computeRetrieve features from a feature store to train a modelTrack model training by using MLflowEvaluate a model, including responsible AI guidelinesAutomate hyperparameter tuningSelect a sampling methodDefine the search spaceDefine the primary metricDefine early termination optionsTrain and deploy models (25-30%)Run model training scriptsConsume data in a jobConfigure compute for a job runConfigure an environment for a job runTrack model training with MLflow in a job runDefine parameters for a jobRun a script as a jobUse logs to troubleshoot job run errorsImplement training pipelinesCreate custom componentsCreate a pipelinePass data between steps in a pipelineRun and schedule a pipelineMonitor and troubleshoot pipeline runsManage modelsDefine the signature in the MLmodel filePackage a feature retrieval specification with the model artifactRegister an MLflow modelAssess a model by using responsible AI principlesDeploy a modelConfigure settings for online deploymentDeploy a model to an online endpointTest an online deployed serviceConfigure compute for a batch deploymentDeploy a model to a batch endpointInvoke the batch endpoint to start a batch scoring jobOptimize language models for AI applications (25-30%)Prepare for model optimizationSelect and deploy a language model from the model catalogCompare language models using benchmarksTest a deployed language model in the playgroundSelect an optimization approachOptimize through prompt engineering and prompt flowTest prompts with manual evaluationDefine and track prompt variantsCreate prompt templatesDefine chaining logic with the prompt flow SDKUse tracing to evaluate your flowOptimize through Retrieval Augmented Generation (RAG)Prepare data for RAG, including cleaning, chunking, and embeddingConfigure a vector storeConfigure an Azure AI Search-based index storeEvaluate your RAG solutionOptimize through fine-tuningPrepare data for fine-tuningSelect an appropriate base modelRun a fine-tuning jobEvaluate your fine-tuned model

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