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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 Mar. 07, 2026 (see below)
You have an Azure Machine Learning workspace named workspaces.
You must add a datastore that connects an Azure Blob storage container to workspaces. You must be able to configure a privilege level.
You need to configure authentication.
Which authentication method should you use?
You have an Azure Machine Learning workspace. You plan to tune model hyperparameters by using a sweep job.
You need to find a sampling method that supports early termination of low-performance jobs and continuous hyperpara meters.
Solution: Use the Bayesian sampling method over the hyperparameter space.
Does the solution meet the goal?
You are analyzing a dataset containing historical data from a local taxi company. You arc developing a regression a regression model.
You must predict the fare of a taxi trip.
You need to select performance metrics to correctly evaluate the- regression model.
Which two metrics can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
You are building recurrent neural network to perform a binary classification.
The training loss, validation loss, training accuracy, and validation accuracy of each training epoch has been provided. You need to identify whether the classification model is over fitted.
Which of the following is correct?
An overfit model is one where performance on the train set is good and continues to improve, whereas performance on the validation set improves to a point and then begins to degrade.
https://machinelearningmastery.com/diagnose-overfitting-underfitting-lstm-models/
You plan to run a Python script as an Azure Machine Learning experiment.
The script contains the following code:
import os, argparse, glob
from azureml.core import Run
parser = argparse.ArgumentParser()
parser.add_argument('--input-data',
type=str, dest='data_folder')
args = parser.parse_args()
data_path = args.data_folder
file_paths = glob.glob(data_path + "/*.jpg")
You must specify a file dataset as an input to the script. The dataset consists of multiple large image files and must be streamed directly from its source.
You need to write code to define a ScriptRunConfig object for the experiment and pass the ds dataset as an argument.
Which code segment should you use?
If you have structured data not yet registered as a dataset, create a TabularDataset and use it directly in your training script for your local or remote experiment.
To load the TabularDataset to pandas DataFrame
df = dataset.to_pandas_dataframe()
Note: TabularDataset represents data in a tabular format created by parsing the provided file or list of files.
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-with-datasets
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