EXAMINATIONS MICROSOFT DP-100 ACTUAL QUESTIONS, NEW DP-100 EXAM DISCOUNT

Examinations Microsoft DP-100 Actual Questions, New DP-100 Exam Discount

Examinations Microsoft DP-100 Actual Questions, New DP-100 Exam Discount

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Tags: Examinations DP-100 Actual Questions, New DP-100 Exam Discount, Updated DP-100 Demo, New DP-100 Exam Preparation, DP-100 Reliable Exam Review

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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.

The Microsoft DP-100 Exam is designed to test the candidate's knowledge of a wide range of topics, including data ingestion and processing, machine learning models, real-time and batch data processing, monitoring and optimization solutions, as well as data storage and management solutions. Designing and Implementing a Data Science Solution on Azure certification is a rigorous test of a candidate's skills and knowledge, and passing the exam requires a deep understanding of these topics.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q181-Q186):

NEW QUESTION # 181
You run an automated machine learning experiment in an Azure Machine Learning workspace. Information about the run is listed in the table below:

You need to write a script that uses the Azure Machine Learning SDK to retrieve the best iteration of the experiment run. Which Python code segment should you use?

  • A.
  • B.
  • C.
  • D.

Answer: D

Explanation:
The get_output method on automl_classifier returns the best run and the fitted model for the last invocation.
Overloads on get_output allow you to retrieve the best run and fitted model for any logged metric or for a particular iteration.
In [ ]:
best_run, fitted_model = local_run.get_output()
Reference:
https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/automated- machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb


NEW QUESTION # 182
You have a binary classifier that predicts positive cases of diabetes within two separate age groups.
The classifier exhibits a high degree of disparity between the age groups.
You need to modify the output of the classifier to maximize its degree of fairness across the age groups and meet the following requirements:
* Eliminate the need to retrain the model on which the classifier is based.
* Minimize the disparity between true positive rates and false positive rates across age groups.
Which algorithm and panty constraint should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 183
You train classification and regression models by using automated machine learning.
You must evaluate automated machine learning experiment results. The results include how a classification model is making systematic errors in its predictions and the relationship between the target feature and the regression model's predictions. You must use charts generated by automated machine learning.
You need to choose a chart type for each model type.
Which chart types should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 184
You manage an Azure Machine Learning workspace. You plan to import data from Azure Data Lake Storage Gen2. You need to build a URI that represents the storage location. Which protocol should you use?

  • A. wasbs
  • B. adl
  • C. abfss
  • D. https

Answer: C


NEW QUESTION # 185
You need to identify the methods for dividing the data according to the testing requirements.
Which properties should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Scenario: Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.
Box 1: Assign to folds
Use Assign to folds option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.
Not Head: Use Head mode to get only the first n rows. This option is useful if you want to test a pipeline on a small number of rows, and don't need the data to be balanced or sampled in any way.
Not Sampling: The Sampling option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.
Box 2: Partition evenly
Specify the partitioner method: Indicate how you want data to be apportioned to each partition, using these options:
Partition evenly: Use this option to place an equal number of rows in each partition. To specify the number of output partitions, type a whole number in the Specify number of folds to split evenly into text box.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/partition-and-sample


NEW QUESTION # 186
......

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