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The DP-100 Exam is intended for data scientists who have experience working with data and developing machine learning models. It focuses on the practical application of data science techniques using Azure tools and services. Candidates will need to demonstrate their ability to use Azure Machine Learning to prepare data, train models, and deploy solutions. They will also need to know how to work with other Azure services such as Azure Databricks, Azure Stream Analytics, and Azure Synapse Analytics.
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Microsoft DP-100 Certification Exam is designed to test the candidate's knowledge and skills in designing and implementing a data science solution on Azure. DP-100 exam is aimed at data scientists, data analysts, and other professionals who work with data and want to demonstrate their proficiency in using Azure tools and services to build scalable and efficient data solutions.
NEW QUESTION # 413
You have a dataset that contains records of patients tested for diabetes. The dataset includes the patient's age.
You plan to create an analysis that will report the mean age value from the differentially private data derived from the dataset.
You need to identify the epsilon value to use in the analysis that minimizes the risk of exposing the actual data.
Which epsilon value should you use?
Answer: C
NEW QUESTION # 414
You are developing a linear regression model in Azure Machine Learning Studio. You run an experiment to compare different algorithms.
The following image displays the results dataset output:
Use the drop-down menus to select the answer choice that answers each question based on the information presented in the image.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Boosted Decision Tree Regression
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
Box 2:
Online Gradient Descent: If you want the algorithm to find the best parameters for you, set Create trainer mode option to Parameter Range. You can then specify multiple values for the algorithm to try.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression
NEW QUESTION # 415
You create an Azure Machine Learning workspace.
You need to detect data drift between a baseline dataset and a subsequent target dataset by using the DataDriftDetector class.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-datadrift/azureml.datadrift.datadriftdetector(class)
NEW QUESTION # 416 
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 417
You have a multi-class image classification deep learning model that uses a set of labeled photographs. You create the following code to select hyperparameter values when training the model.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Yes
Hyperparameters are adjustable parameters you choose to train a model that govern the training process itself. Azure Machine Learning allows you to automate hyperparameter exploration in an efficient manner, saving you significant time and resources. You specify the range of hyperparameter values and a maximum number of training runs. The system then automatically launches multiple simultaneous runs with different parameter configurations and finds the configuration that results in the best performance, measured by the metric you choose. Poorly performing training runs are automatically early terminated, reducing wastage of compute resources. These resources are instead used to explore other hyperparameter configurations.
Box 2: Yes
uniform(low, high) - Returns a value uniformly distributed between low and high Box 3: No Bayesian sampling does not currently support any early termination policy.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
NEW QUESTION # 418
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