さらに、It-Passports DP-100ダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=10OeOi5jD2asQv1DFruGY3SKhnSu6D6Xw
どうしてIt-PassportsのMicrosoftのDP-100試験トレーニング資料はほかのトレーニング資料よりはるかに人気があるのでしょうか。それはいくつかの理由があります。第一、It-Passportsは受験生の要求をよく知っています。しかも、他のどのサイトよりも良いサービスを提供します。第二、専心すること。我々が決まったことを完璧に作るためにすべての不要な機会を諦めなければなりません。第三、我々は確かに最高の製品を持っていますが、粗悪品の方式で表示されたら、もちろん粗悪品と見られますから、我々は自分の製品を創造的かつプロの方法で見せます。It-PassportsのMicrosoftのDP-100試験トレーニング資料はこんなに成功するトレーニングですから、It-Passportsを選ばない理由はないです。
Microsoft DP-100認定試験は、Azureでデータサイエンスソリューションを設計および実装する能力を証明したいデータサイエンティストやデータエンジニア向けに設計されています。この試験は、Azureを使用して機械学習モデルを構築および展開した経験がある個人、およびAzureでのデータストレージと処理に精通している個人を対象としています。
Microsoft DP-100認定試験は、データサイエンスの概念とAzureデータサービスを深く理解する必要がある挑戦的で包括的なテストです。この試験に合格することにより、候補者はAzureに関するデータサイエンスソリューションの設計と実装に関する専門知識を実証し、この分野でのキャリアの見通しを強化できます。 DP-100認定は、データサイエンス業界の最新のトレンドとテクノロジーを最新の状態に保ちたいと考えているデータの専門家、データ科学者、開発者にとって貴重な資産です。
あらゆる人にとって、時間は非常に大切です。DP-100試験に対して、いろいろな資料があります。そのような資料を勉強するには、長い時間がかかります。でも、DP-100問題集を利用すれば、短い時間でDP-100試験に合格できます。そして、DP-100問題集は安くて、便利です。誰でも、DP-100問題集を選択すれば、試験に合格する可能性が大きいです。もし、DP-100問題集を勉強すれば、もし、将来にITエリートになります。
DP-100試験では、データの摂取と処理ソリューションの設計と実装、機械学習モデルの設計と実装、データストレージソリューションの設計と実装、データ視覚化ソリューションの設計と実装など、データサイエンスに関連するさまざまなトピックをカバーしています。この試験では、Azure Machine Learning、Azure Databricks、Azure Stream Analytics、Azure Data FactoryなどのAzureサービスに関する候補者の知識も評価します。
質問 # 508
You create a binary classification model by using Azure Machine Learning Studio.
You must tune hyperparameters by performing a parameter sweep of the model. The parameter sweep must meet the following requirements:
* iterate all possible combinations of hyperparameters
* minimize computing resources required to perform the sweep
You need to perform a parameter sweep of the model.
Which parameter sweep mode should you use?
正解：A
解説：
Maximum number of runs on random grid: This option also controls the number of iterations over a random sampling of parameter values, but the values are not generated randomly from the specified range; instead, a matrix is created of all possible combinations of parameter values and a random sampling is taken over the matrix. This method is more efficient and less prone to regional oversampling or undersampling.
If you are training a model that supports an integrated parameter sweep, you can also set a range of seed values to use and iterate over the random seeds as well. This is optional, but can be useful for avoiding bias introduced by seed selection.
Incorrect Answers:
B: If you are building a clustering model, use Sweep Clustering to automatically determine the optimum number of clusters and other parameters.
C: Entire grid: When you select this option, the module loops over a grid predefined by the system, to try different combinations and identify the best learner. This option is useful for cases where you don't know what the best parameter settings might be and want to try all possible combination of values.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/tune-model- hyperparameters
質問 # 509
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.
正解：
解説：
Explanation:
1. Confusion Matrix
2. Prediction vs True
The calibration curve plots a model ' s confidence in its predictions against the proportion of positive samples at each confidence level (https://learn.microsoft.com/en-us/azure/machine-learning/how-to-understand- automated-ml?view=azureml-api-2#calibration-curve) The predicted vs. true chart plots the relationship between the target feature (true/actual values) and the model
' s predictions (https://learn.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml?
view=azureml-api-2#predicted-vs-true)
質問 # 510
You arc I mating a deep learning model to identify cats and dogs. You have 25,000 color images.
You must meet the following requirements:
* Reduce the number of training epochs.
* Reduce the size of the neural network.
* Reduce over-fitting of the neural network.
You need to select the image modification values.
Which value should you use? To answer, select the appropriate Options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
質問 # 511
You have a dataset that contains over 150 features. You use the dataset to train a Support Vector Machine (SVM) binary classifier.
You need to use the Permutation Feature Importance module in Azure Machine Learning Studio to compute a set of feature importance scores for the dataset.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
正解：
解説：
1 - Add a Two-Class Support Vector Machine module to initialize the SVM classifier.
2 - Add a dataset to the experiment
3 - Add a Split Data module to create training and test dataset.
4 - Add a Permutation Feature Importance module and connect to the trained model and test dataset.
5 - Set the Metric for measuring performance property to Classification - Accuracy and then run the experiment.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-support-vector-machine
https:HYPERLINK "https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-support-vector-machine"//docs.microsoft.com/en-us/aHYPERLINK "https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-support-vector-machine"zure/machine-learning/studio-module-reference/two-class-support-vector-machine https://docs.microsoHYPERLINK "https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importance"ft.com/en-us/azure/machine-learnHYPERLINK "https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importance"ing/studio-module-reference/permutation-feature-importance
https:HYPERLINK "https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-support-vector-machine"//docs.microsoft.com/en-us/aHYPERLINK "https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-support-vector-machine"zure/machine-learning/studio-module-reference/two-class-support-vector-machine https://docs.microsoHYPERLINK "https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importance"ft.com/en-us/azure/machine-learnHYPERLINK "https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importance"ing/studio-module-reference/permutation-feature-importance
https://docs.microsoHYPERLINK "https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importance"ft.com/en-us/azure/machine-learnHYPERLINK "https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importance"ing/studio-module-reference/permutation-feature-importance
質問 # 512
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 create a model to forecast weather conditions based on historical data.
You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.
Solution: Run the following code:
Does the solution meet the goal?
正解：B
解説：
The two steps are present: process_step and train_step
Note:
Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.
PipelineData objects are also used when constructing Pipelines to describe step dependencies. To specify that a step requires the output of another step as input, use a PipelineData object in the constructor of both steps.
For example, the pipeline train step depends on the process_step_output output of the pipeline process step:
from azureml.pipeline.core import Pipeline, PipelineData
from azureml.pipeline.steps import PythonScriptStep
datastore = ws.get_default_datastore()
process_step_output = PipelineData("processed_data", datastore=datastore) process_step = PythonScriptStep(script_name="process.py", arguments=["--data_for_train", process_step_output], outputs=[process_step_output], compute_target=aml_compute, source_directory=process_directory) train_step = PythonScriptStep(script_name="train.py", arguments=["--data_for_train", process_step_output], inputs=[process_step_output], compute_target=aml_compute, source_directory=train_directory) pipeline = Pipeline(workspace=ws, steps=[process_step, train_step]) Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?
view=azure-ml-py
質問 # 513
......
DP-100無料試験: https://www.it-passports.com/DP-100.html
P.S. It-PassportsがGoogle Driveで共有している無料かつ新しいDP-100ダンプ：https://drive.google.com/open?id=10OeOi5jD2asQv1DFruGY3SKhnSu6D6Xw