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Microsoft DP-100 (Designing and Implementing a Data Science Solution on Azure) Certification Exam is designed for professionals who want to validate their expertise in designing and implementing data science solutions using Microsoft Azure technologies. Designing and Implementing a Data Science Solution on Azure certification exam is aimed at data scientists, data engineers, and other professionals who work with data and want to demonstrate their skills in this field. DP-100 Exam Tests the candidate's knowledge of Azure data services, machine learning, and other related technologies.
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To prepare for the DP-100 exam, candidates must have a solid understanding of data science and machine learning concepts, as well as experience working with Azure data services. They should be familiar with Azure Machine Learning Studio, Azure Databricks, Azure HDInsight, and other relevant Azure technologies. DP-100 exam consists of multiple-choice questions and performance-based tasks that require candidates to demonstrate their ability to design and implement data science solutions in real-world scenarios. The DP-100 Certification is a valuable credential for professionals seeking to advance their careers in data science and machine learning on the Azure platform.
NEW QUESTION # 147
You use Azure Machine Learning designer to create a real-time service endpoint.
You have a single Azure Machine Learning service compute resource.
You train the model and prepare the real-time pipeline for deployment.
You need to publish the inference pipeline as a web service.
Which compute type should you use?
Answer: E
Explanation:
Azure Kubernetes Service (AKS) can be used real-time inference.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target
NEW QUESTION # 148
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.
You must run the script as an Azure ML experiment on a compute cluster named aml-compute.
You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.
Solution: Run the following code:
Does the solution meet the goal?
Answer: A
Explanation:
The scikit-learn estimator provides a simple way of launching a scikit-learn training job on a compute target.
It is implemented through the SKLearn class, which can be used to support single-node CPU training.
Example:
from azureml.train.sklearn import SKLearn
}
estimator = SKLearn(source_directory=project_folder,
compute_target=compute_target,
entry_script='train_iris.py'
)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-scikit-learn
NEW QUESTION # 149
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:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importance
NEW QUESTION # 150
You manage an Azure Machine Learning workspace named workspace 1 with a compute instance named computet.
You must remove a kernel named kernel 1 from computet1. You connect to compute 1 by using noa terminal window from workspace 1.
You need to enter a command in the terminal window to remove kernel 1.
Which command should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection it worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 151
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:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters
NEW QUESTION # 152
......
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P.S. Free 2026 Microsoft DP-100 dumps are available on Google Drive shared by TorrentExam: https://drive.google.com/open?id=144vWo-PKiK119ZkpT7Kw-U9Y62IhI1n2