About Microsoft DP-100: Designing and Implementing a Data Science Solution on Azure

Skills Covered

To nail DP-100, you will need to scrutinize the below-mentioned areas:

Set up Azure ML Workspace
The first domain gives considerable attention to skills related to the Azure ML workspace. So, the test-takers have a chance to learn about workspace settings, the management of workspace using Azure ML, and registering in addition to maintaining the datastores.
Execute Experiments & Train Models
This objective imparts updated understanding about the concepts like creating models by using Azure ML Designer, custom code modules in Designer, defining a pipeline data flow, and an experiment running by using Azure Machine Learning SDK.
Deploy and Consume Models
The last segment is all about deployment and consumption models. Topics like evaluating compute options, creating production compute targets, batch inferencing pipeline creation, and running this pipeline efficiently are well covered within such a scope.
Manage and Optimize Models
Using automated ML for the optimal model creation, hyperdrive to tune hyperparameters, model management, and knowing the crucial model explainers to interpret models are some of the key topics explained in this portion.

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What is the duration of the DP-100 Exam

Length of Examination: 150 minutes
Number of Questions: 45-60
Passing Score: 700/1000
Format: Multiple choices, multiple answers

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Microsoft DP-100 Exam Syllabus Topics:

Topic	Details
Topic 1	Train and deploy models: In this topic, Microsoft Data Scientists develop the expertise to execute training scripts and implement efficient training pipelines. Participants learn to manage models effectively and deploy them using Azure services. The focus is on assessing proficiency in training processes, deployment strategies, and lifecycle management of machine learning models to ensure production-ready solutions that align with organizational goals.
Topic 2	Design and prepare a machine learning solution: This topic equips Microsoft Data Scientists with skills to design comprehensive machine learning solutions tailored to business needs. It delves into managing resources and assets in the Azure Machine Learning workspace, offering insights into organizing computational and storage resources effectively.
Topic 3	Explore data, and run experiments: This section focuses on enabling Microsoft Data Scientists to explore datasets and experiment with model configurations effectively. By leveraging automated machine learning, participants explore optimal models and streamline custom model training with notebooks. It evaluates skills in automating hyperparameter tuning to refine models for improved performance.
Topic 4	Optimize language models for AI applications: This advanced topic teaches Microsoft Data Scientists to enhance AI applications by optimizing language models. Skills are developed in preparing models for optimization, prompt engineering, and employing techniques like Prompt Flow and Retrieval Augmented Generation (RAG). Additionally, fine-tuning strategies are explored to maximize model performance.

Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx

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