最新の無料DP-100効率的問題集をダウンロード2024年11月26日更新された431問がある
Microsoft DP-100試験練習テスト解答
Microsoft DP-100認定試験は、業界で非常に尊敬されており、世界中のトップ組織によって認められています。これは、データサイエンティストとしてのキャリアを昇進させ、成長と開発のための新しい機会を開くのに役立つ貴重な資格情報です。新しい仕事を探しているか、現在の役割でプロモーションを求めているかにかかわらず、DP-100認定は、競争から目立つために必要な優位性を与え、専門的な目標を達成することができます。したがって、認定データサイエンティストになることに興味があり、キャリアを次のレベルに引き上げたい場合、Microsoft DP-100試験は間違いなく検討する価値があります。
質問 # 180
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
You must register datastores in workspace1 for Azure Blob and Azure Data Lake Gen2 storage to meet the following requirements:
* Data scientists accessing the datastore must have the same level of access.
* Access must be restricted to specified containers or folders.
You need to configure a security access method used to register the Azure Blob and Azure Data lake Gen?
storage in workspace1. Which security access method should you configure? To answer, select the appropriate options in the answers area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
質問 # 181
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.
正解：
解説：
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters
質問 # 182
You use Azure Machine Learning to train and register a model.
You must deploy the model into production as a real-time web service to an inference cluster named service-compute that the IT department has created in the Azure Machine Learning workspace.
Client applications consuming the deployed web service must be authenticated based on their Azure Active Directory service principal.
You need to write a script that uses the Azure Machine Learning SDK to deploy the model. The necessary modules have been imported.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-kubernetes-service
https://docs.microsoft.com/en-us/azure/databricks/dev-tools/api/latest/aad/service-prin-aad-token
質問 # 183
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?
- A. No
- B. Yes
正解：A
解説：
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.
Compare with this 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=azu
質問 # 184
You need to configure the Edit Metadata module so that the structure of the datasets match.
Which configuration options should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
質問 # 185
You have the following Azure subscriptions and Azure Machine Learning service workspaces:
You need to obtain a reference to the ml-project workspace.
Solution: Run the following Python code:
Does the solution meet the goal?
- A. No
- B. Yes
正解：A
質問 # 186
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.
An IT department creates the following Azure resource groups and resources:
The IT department creates an Azure Kubernetes Service (AKS)-based inference compute target named aks- cluster in the Azure Machine Learning workspace.
You have a Microsoft Surface Book computer with a GPU. Python 3.6 and Visual Studio Code are installed.
You need to run a script that trains a deep neural network (DNN) model and logs the loss and accuracy metrics.
Solution: Install the Azure ML SDK on the Surface Book. Run Python code to connect to the workspace and then run the training script as an experiment on local compute.
Does the solution meet the goal?
- A. No
- B. Yes
正解：A
解説：
Need to attach the mlvm virtual machine as a compute target in the Azure Machine Learning workspace.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target
質問 # 187
You need to identify the methods for dividing the data according, to the testing requirements.
Which properties should you select? To answer, select the appropriate option-, m the answer area. NOTE:
Each correct selection is worth one point.
正解：
解説：
質問 # 188
You have machine learning models produce unfair predictions across sensitive features.
You must use a post-processing technique to apply a constraint to the models to mitigate their unfairness.
You need to select a post-processing technique and model type.
What 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 # 189
You are building a binary classification model by using a supplied training set.
The training set is imbalanced between two classes.
You need to resolve the data imbalance.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution NOTE: Each correct selection is worth one point.
- A. Normalize the training feature set.
- B. Penalize the classification
- C. Use accuracy as the evaluation metric of the model.
- D. Resample the data set using under sampling or oversampling
- E. Generate synthetic samples in the minority class.
正解：B、C、D
解説：
Reference:
https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/
質問 # 190
You are solving a classification task.
You must evaluate your model on a limited data sample by using k-fold cross validation. You start by configuring a k parameter as the number of splits.
You need to configure the k parameter for the cross-validation.
Which value should you use?
- A. k=0.5
- B. k=5
- C. k=1
- D. k=0
正解：B
解説：
Explanation
Leave One Out (LOO) cross-validation
Setting K = n (the number of observations) yields n-fold and is called leave-one out cross-validation (LOO), a special case of the K-fold approach.
LOO CV is sometimes useful but typically doesn't shake up the data enough. The estimates from each fold are highly correlated and hence their average can have high variance.
This is why the usual choice is K=5 or 10. It provides a good compromise for the bias-variance tradeoff.
質問 # 191
You have an Azure Machine Learning workspace. You are running an experiment on your local computer.
You need to use MLflow Tracking to store metrics and artifacts from your local experiment runs in the workspace.
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 - Import MLflow and Workapsce classes.
2 - Load the workspace.
3 - Retrieve the tracking URI and set the experiment name.
4 - Start a training run and activate the MLflow logging API.
質問 # 192
You have a binary classifier that predicts positive cases of diabetes within two separate age groups.
The classifier exhibits a high degree of disparity between the age groups.
You need to modify the output of the classifier to maximize its degree of fairness across the age groups and meet the following requirements:
* Eliminate the need to retrain the model on which the classifier is based.
* Minimize the disparity between true positive rates and false positive rates across age groups.
Which algorithm and panty constraint should you use? To answer, select the appropriate options in the answer are a. NOTE: Each correct selection is worth one point.
正解：
解説：
質問 # 193
You are building an intelligent solution using machine learning models.
The environment must support the following requirements:
* Data scientists must build notebooks in a cloud environment
* Data scientists must use automatic feature engineering and model building in machine learning pipelines.
* Notebooks must be deployed to retrain using Spark instances with dynamic worker allocation.
* Notebooks must be exportable to be version controlled locally.
You need to create the environment.
Which four 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.
正解：
解説：
Explanation
Step 1: Create an Azure HDInsight cluster to include the Apache Spark Mlib library Step 2: Install Microsot Machine Learning for Apache Spark You install AzureML on your Azure HDInsight cluster.
Microsoft Machine Learning for Apache Spark (MMLSpark) provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.
Step 3: Create and execute the Zeppelin notebooks on the cluster
Step 4: When the cluster is ready, export Zeppelin notebooks to a local environment.
Notebooks must be exportable to be version controlled locally.
References:
https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-zeppelin-notebook
https://azuremlbuild.blob.core.windows.net/pysparkapi/intro.html
質問 # 194 
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：
質問 # 195
You are creating a classification model for a banking company to identify possible instances of credit card fraud. You plan to create the model in Azure Machine Learning by using automated machine learning.
The training dataset that you are using is highly unbalanced.
You need to evaluate the classification model.
Which primary metric should you use?
- A. normalized_mean_absolute_error
- B. normalized_root_mean_squared_error
- C. [spearman_correlation
- D. AUC.weighted
- E. accuracy
正解：D
解説：
AUC_weighted is a Classification metric.
Note: AUC is the Area under the Receiver Operating Characteristic Curve. Weighted is the arithmetic mean of the score for each class, weighted by the number of true instances in each class.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml
質問 # 196
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 # 197
You train and register a model by using the Azure Machine Learning SDK on a local workstation. Python 3.6 and Visual Studio Code are installed on the workstation.
When you try to deploy the model into production as an Azure Kubernetes Service (AKS)-based web service, you experience an error in the scoring script that causes deployment to fail.
You need to debug the service on the local workstation before deploying the service to production.
Which four 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.
正解：
解説：
Explanation:
Step 1: Install Docker on the workstation
Prerequisites include having a working Docker installation on your local system.
Build or download the dockerfile to the compute node.
Step 2: Create an AksWebservice deployment configuration and deploy the model to it To deploy a model to Azure Kubernetes Service, create a deployment configuration that describes the compute resources needed.
# If deploying to a cluster configured for dev/test, ensure that it was created with enough
# cores and memory to handle this deployment configuration. Note that memory is also used by
# things such as dependencies and AML components.
deployment_config = AksWebservice.deploy_configuration(cpu_cores = 1, memory_gb = 1) service = Model.deploy(ws, "myservice", [model], inference_config, deployment_config, aks_target) service.wait_for_deployment(show_output = True) print(service.state) print(service.get_logs()) Step 3: Create a LocalWebservice deployment configuration for the service and deploy the model to it To deploy locally, modify your code to use LocalWebservice.deploy_configuration() to create a deployment configuration. Then use Model.deploy() to deploy the service.
Step 4: Debug and modify the scoring script as necessary. Use the reload() method of the service after each modification.
During local testing, you may need to update the score.py file to add logging or attempt to resolve any problems that you've discovered. To reload changes to the score.py file, use reload(). For example, the following code reloads the script for the service, and then sends data to it.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-kubernetes-service
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment-local
質問 # 198
You are creating a new experiment in Azure Machine Learning Studio. You have a small dataset that has missing values in many columns. The data does not require the application of predictors for each column. You plan to use the Clean Missing Data module to handle the missing data.
You need to select a data cleaning method.
Which method should you use?
- A. Synthetic Minority Oversampling Technique (SMOTE)
- B. Replace using; Probabilistic PCA
- C. Replace using MICE
- D. Normalization
正解：B
解説：
Replace using Probabilistic PCA: Compared to other options, such as Multiple Imputation using Chained Equations (MICE), this option has the advantage of not requiring the application of predictors for each column. Instead, it approximates the covariance for the full dataset. Therefore, it might offer better performance for datasets that have missing values in many columns.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data
質問 # 199
You are building a binary classification model by using a supplied training set.
The training set is imbalanced between two classes.
You need to resolve the data imbalance.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution NOTE: Each correct selection is worth one point.
- A. Penalize the classification
- B. Normalize the training feature set.
References:
https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/ - C. Use accuracy as the evaluation metric of the model.
- D. Resample the data set using under sampling or oversampling
- E. Generate synthetic samples in the minority class.
正解：A、C、D
質問 # 200
......
マイクロソフトDP-100（Azure上でデータサイエンスソリューションの設計と実装）認定試験は、マイクロソフトAzureテクノロジーを使用してデータサイエンスソリューションの設計と実装の専門知識を検証したい専門家を対象としています。この認定試験は、データサイエンティスト、データエンジニア、および他のデータに取り組むプロフェッショナルが対象であり、この分野でのスキルを証明したいと考えています。この試験では、Azureデータサービス、機械学習、およびその他の関連技術に関する候補者の知識がテストされます。
Microsoft DP-100認定試験は、Azureプラットフォーム上での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候補者の知識と専門知識を評価する包括的なテストです。この試験は、データプロフェッショナル、データサイエンティスト、および開発者がスキルセットを拡大し、Azureデータサービスに関する知識を認定することを目的としています。DP-100試験は、データの探索と準備、モデリング、展開、および監視を含む幅広いトピックをカバーしています。
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最新の認証試験DP-100問題集練習テスト解答はこちら：https://drive.google.com/open?id=1Yrh5PgLzFZ4o9GHNMFW5mLJBsb53wbVb