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Microsoft DP-100 Exam Questions
- Topic 1: 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 2: 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 3: 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 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.
Free Microsoft DP-100 Exam Actual Questions
Note: Premium Questions for DP-100 were last updated On Jul. 03, 2025 (see below)
You are building a binary classification model by using a supplied training set.
The training set is imbalanced between two classes.
You need to resolve the data imbalance.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution NOTE: Each correct selection is worth one point.
You have an Azure Machine Learning (ML) model deployed to an online endpoint.
You need to review container logs from the endpoint by using Azure Ml Python SDK v2. The logs must include the console log from the inference server with print/log statements from the models scoring script.
What should you do first?
You train and publish a machine teaming model.
You need to run a pipeline that retrains the model based on a trigger from an external system.
What should you configure?
You use the following Python code in a notebook to deploy a model as a web service:
The deployment fails.
You need to use the Python SDK in the notebook to determine the events that occurred during service deployment an initialization.
Which code segment should you use?
You use Azure Machine Learning Designer to load the following datasets into an experiment:
Dataset1
Dataset2
You use Azure Machine Learning Designer to load the following datasets into an experiment:
You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.
Solution: Use the Join Data component.
Does the solution meet the goal?
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