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DP-100 (Written)

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About DP-100 Certification Exam Practice Test DP-100 Certification Practice Test, to check your understanding of DP-100 exam concepts.This exam covers all the important topic questions which are expected to appear in DP-100 exam.

With this test, you can fully prepare for the exam and pass it on your first try.

This set of tests is timed, so you'll know when you've taken longer than the official test allows. At the end of the test, you can review your answers to see which practice sections you need to improve on to pass the actual exam.

The test is completed and the score is displayed.

Exam Content

Identify dataset structures: Understand how to work with different types of data sources, including tabular data, images, and text,Select compute specifications: Choose the appropriate compute resources for training and deployment, such as Azure Machine Learning Compute Instances or Compute Clusters,Create and manage Azure Machine Learning workspace resources: Set up and configure workspaces, manage experiments, and organize resources efficiently,Data preparation and feature engineering: Clean and preprocess data, handle missing values, and create features that improve model performance,Use Automated Machine Learning (AutoML): Explore the best models for a given problem using AutoML, which automates the process of model selection and hyperparameter tuning,Custom model training with Notebooks: Implement custom machine learning models using Jupyter Notebooks in Azure Machine Learning,Automated hyperparameter tuning: Optimize model performance by automatically tuning hyperparameters using Azure Machine Learning's hyperparameter tuning capabilities,Experiment tracking and versioning: Track experiments, manage model versions, and maintain reproducibility,Run model training scripts: Implement and execute training scripts using Azure Machine Learning SDK or Azure Machine Learning Designer,Implement training pipelines: Create scalable and repeatable training pipelines using Azure Machine Learning Pipelines,Model management: Register, version, and manage models in the Azure Machine Learning workspace,Deploy models: Deploy trained models as web services on Azure Container Instances, Azure Kubernetes Service, or IoT Edge devices. Ensure proper monitoring and logging for deployed models,Optimize language models: Fine-tune and optimize pre-trained language models for specific use cases,Prompt engineering and prompt flow: Improve model performance by crafting effective prompts and using prompt flow techniques,Retrieval-Augmented Generation (RAG): Enhance model capabilities by integrating retrieval mechanisms to provide context-aware responses,Model optimization techniques: Apply techniques such as quantization, pruning, and knowledge distillation to improve model efficiency and performance,Monitor model performance: Track key metrics to evaluate model accuracy, precision, recall, and other performance indicators,Set up alerts and logging: Implement monitoring solutions to detect issues and receive alerts based on predefined thresholds,Model retraining and updating: Develop strategies for retraining models with new data and updating deployed models to maintain accuracy and relevance,Data security: Implement security best practices to protect data, including encryption at rest and in transit,Compliance: Ensure that solutions meet regulatory requirements and adhere to compliance standards such as GDPR, HIPAA, etc,Role-based access control: Manage access to Azure Machine Learning resources using role-based access control (RBAC),Estimate and optimize costs: Understand the cost structure of Azure Machine Learning services and optimize resource usage to manage costs effectively,Resource scaling: Implement strategies for scaling compute resources based on demand to balance performance and cost.

Knowledge Content