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| Section | Objectives |
|---|---|
| Topic 1: Train machine learning models | - Tune hyperparameters and evaluate models - Train models using Azure Machine Learning |
| Topic 2: Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
| Topic 3: Optimize and manage models | - Track experiments and manage model lifecycle - Improve model performance |
| Topic 4: Design and prepare a machine learning solution | - Plan and configure Azure Machine Learning workspace - Manage compute and data assets - Select appropriate Azure services for machine learning workloads |
| Topic 5: Explore and analyze data | - Ingest and prepare data for modeling - Perform exploratory data analysis |
あなたはいまMicrosoftのDP-100認定試験にどうやって合格できるかということで首を傾けているのですか。MicrosoftのDP-100認定試験は現在のいろいろなIT認定試験における最も価値のある資格の一つです。ここ数十年間では、インターネット・テクノロジーは世界中の人々の注目を集めているのです。それがもう現代生活の不可欠な一部となりました。その中で、Microsoftの認証資格は広範な国際的な認可を得ました。ですから、IT業界で仕事している皆さんはMicrosoftの認定試験を受験して資格を取得することを通して、彼らの知識やスキルを向上させます。DP-100認定試験はMicrosoftの最も重要な試験の一つです。この資格は皆さんに大きな利益をもたらすことができます。
質問 # 243
You create an Azure Machine Learning workspace named workspace1. You assign a custom role to a user of workspace1.
The custom role has the following JSON definition:
Instructions: For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
正解：
解説：
Reference:
https://docs.microsoft.com/en-us/azure/role-based-access-control/overview#how-azure-rbac-determines-if-a-user-has-access-to-a-resource
質問 # 244
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?
正解：C
解説：
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
質問 # 245
You use Azure Machine Learning to implement hyperparameter tuning with a Bandit early termination policy.
The policy uses a slack_factor set to 01. an evaluation interval set to 1, and an evaluation delay set to b.
You need to evaluate the outcome of the early termination policy
What should you evaluate? To answer, select the appropriate options m the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation
質問 # 246
You are performing feature scaling by using the scikit-learn Python library for x.1 x2, and x3 features.
Original and scaled data is shown in the following image.
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.
正解：
解説：
Reference:
http://benalexkeen.com/feature-scaling-with-scikit-learn/
質問 # 247
You have an Azure Al Foundry project named Projects
You are developing a web classification Prompt flow named Flow1 in Project 1. The current input of Flow1 is defined as the following:
You plan to add a large language model (LLM) node named Nodel to Flowl. Nodel will use an input named url of type string to classify the url provided as Flowl input. In Nodel. you will add Jinja code to reference the value of its url input.
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