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Microsoft DP-100 exam is a challenging certification exam that requires a thorough understanding of data science and machine learning concepts. It is an excellent way for professionals to demonstrate their skills and knowledge in the field of data science and machine learning and gain a competitive edge in the job market. Passing DP-100 exam will provide individuals with a valuable certification that is recognized by employers worldwide and open up new opportunities for career growth and advancement.
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Microsoft DP-100 exam is a certification test that validates a candidate's knowledge and skills in designing and implementing data science solutions on Azure. DP-100 exam is ideal for data scientists, data engineers, and AI developers who want to showcase their expertise in building and deploying intelligent solutions using Azure services. The DP-100 Exam focuses on the core concepts of data science, including data exploration and preparation, modeling, feature engineering, and machine learning.
NEW QUESTION # 71
Your Azure Machine Learning workspace has a dataset named real_estate_dat a. A sample of the data in the dataset follows.
You want to use automated machine learning to find the best regression model for predicting the price column.
You need to configure an automated machine learning experiment using the Azure Machine Learning SDK.
How should you complete the code? 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-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py
NEW QUESTION # 72
You need to configure the Permutation Feature Importance module for the model training requirements.
What should you do? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: 500
For Random seed, type a value to use as seed for randomization. If you specify 0 (the default), a number is generated based on the system clock.
A seed value is optional, but you should provide a value if you want reproducibility across runs of the same experiment.
Here we must replicate the findings.
Box 2: Mean Absolute Error
Scenario: Given a trained model and a test dataset, you must compute the Permutation Feature Importance scores of feature variables. You need to set up the Permutation Feature Importance module to select the correct metric to investigate the model's accuracy and replicate the findings.
Regression. Choose one of the following: Precision, Recall, Mean Absolute Error , Root Mean Squared Error, Relative Absolute Error, Relative Squared Error, Coefficient of Determination References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importan
NEW QUESTION # 73
Drag and Drop Question
You need to correct the model fit issue.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Step 1: Augment the data
Scenario: Columns in each dataset contain missing and null values. The datasets also contain many outliers.
Step 2: Add the Bayesian Linear Regression module.
Scenario: You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.
Step 3: Configure the regularization weight.
Regularization typically is used to avoid overfitting. For example, in L2 regularization weight, type the value to use as the weight for L2 regularization. We recommend that you use a non-zero value to avoid overfitting.
Scenario:
Model fit: The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.
Incorrect Answers:
Multiclass Decision Jungle module:
Decision jungles are a recent extension to decision forests. A decision jungle consists of an ensemble of decision directed acyclic graphs (DAGs).
L-BFGS:
L-BFGS stands for "limited memory Broyden-Fletcher-Goldfarb-Shanno". It can be found in the wwo-Class Logistic Regression module, which is used to create a logistic regression model that can be used to predict two (and only two) outcomes.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regr ession
NEW QUESTION # 74
You create a Python script named train.py and save it in a folder named scripts. The script uses the scikit-learn framework to train a machine learning model.
You must run the script as an Azure Machine Learning experiment on your local workstation.
You need to write Python code to initiate an experiment that runs the train.py script.
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:
Explanation
Graphical user interface, text, application, table, Word Description automatically generated
Box 1: source_directory
source_directory: A local directory containing code files needed for a run.
Box 2: script
Script: The file path relative to the source_directory of the script to be run.
Box 3: environment
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.scriptrunconfig
NEW QUESTION # 75
You use Azure Machine Learning to implement hyperparameter tuning for an Azure ML Python SDK v2- based model training.
Training runs must terminate when the primary metric is lowered by 25 percent or more compared to the best performing run.
You need to configure an early termination policy to terminate training jobs.
Which values 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 # 76
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