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Microsoft DP-100 exam, also known as Designing and Implementing a Data Science Solution on Azure, is a certification exam for professionals who wish to demonstrate their skills in designing and implementing data science solutions on Azure. DP-100 exam is part of Microsoft's Azure certification program and is targeted towards data scientists, machine learning engineers, and other professionals involved in data science.

The DP-100 Certification Exam is intended for data scientists, data architects, and data engineers who want to validate their expertise in designing and implementing data science solutions on Azure. It is also suitable for professionals who are responsible for implementing Azure-based machine learning and AI solutions, including data analysts, developers, and IT administrators.

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Microsoft DP-100 Certification Exam is designed for data scientists and data engineers who want to demonstrate their ability to design and implement data science solutions on Azure. DP-100 exam is intended for individuals who have experience using Azure to build and deploy machine learning models, as well as those who have experience with data storage and processing on Azure.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q71-Q76):

NEW QUESTION # 71
You create an Azure Machine learning workspace.
You are use the Azure Machine -learning Python SDK v2 to define the search space for concrete hyperparafneters. The hyper parameters must consist of a list of predetermined, comma-separated.
You need to import the class from the azure ai ml. sweep package used to create the list of values.
Which class should you import?

A. Uniform
B. Randint
C. Choice
D. Normal

Answer: C

NEW QUESTION # 72
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:

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 # 73
You create an Azure Machine Learning dataset. You use the Azure Machine Learning designer to transform the dataset by using an Execute Python Script component and custom code.
You must upload the script and associated libraries as a script bundle.
You need to configure the Execute Python Script component.
Which configurations 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 # 74
You are using the Azure Machine Learning Service to automate hyperparameter exploration of your neural network classification model.
You must define the hyperparameter space to automatically tune hyperparameters using random sampling according to following requirements:
The learning rate must be selected from a normal distribution with a mean value of 10 and a standard deviation of 3.
Batch size must be 16, 32 and 64.
Keep probability must be a value selected from a uniform distribution between the range of 0.05 and
0.1.
You need to use the param_sampling method of the Python API for the Azure Machine Learning Service.
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

In random sampling, hyperparameter values are randomly selected from the defined search space. Random sampling allows the search space to include both discrete and continuous hyperparameters.
Example:
from azureml.train.hyperdrive import RandomParameterSampling
param_sampling = RandomParameterSampling( {
"learning_rate": normal(10, 3),
"keep_probability": uniform(0.05, 0.1),
"batch_size": choice(16, 32, 64)
}
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters

NEW QUESTION # 75
You are analyzing the asymmetry in a statistical distribution.
The following image contains two density curves that show the probability distribution of two datasets.

Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-elementary-statistics

NEW QUESTION # 76
......

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