DP-100練習Microsoft高合格率回答あなたを試験は高確率で合格させます！[2025]
最高の方法からパスMicrosoft AzureのDP-100試験合格させます
Microsoft DP-100認定試験は、データサイエンスと機械学習に関連する様々なトピックをカバーする包括的なテストです。データ探索、データ前処理、フィーチャーエンジニアリング、モデルトレーニング、展開に関する知識をテストする様々なモジュールから構成されています。また、機械学習アルゴリズム、統計モデリング、データ可視化技術に対する理解も評価されます。DP-100試験に合格することで、これらの全ての分野における熟練度を証明し、Azure上で効果的なデータサイエンスソリューションを設計・実装する能力を証明することができます。
質問 # 80
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. Resample the data set using under sampling or oversampling
- B. Generate synthetic samples in the minority class.
- C. Use accuracy as the evaluation metric of the model.
- D. Penalize the classification
- E. Normalize the training feature set.
正解：A、C、D
解説：
References:
https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/
質問 # 81
You need to define a modeling strategy for ad response.
Which three 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：
1 - Implement a K-Means Clustering model.
2 - Use the cluster as a feature in a Decision Jungle model.
3 - Use the raw score as a feature in a Score Matchbox Recommender model.
質問 # 82
You create a binary classification model. The model is registered in an Azure Machine Learning workspace. You use the Azure Machine Learning Fairness SDK to assess the model fairness.
You develop a training script for the model on a local machine.
You need to load the model fairness metrics into Azure Machine Learning studio.
What should you do?
- A. Implement the download_dashboard_by_upload_id function
- B. Upload the training script
- C. Implement the upload_dashboard_dictionary function
- D. Implement the creace_group_metric_sec function
正解：C
解説：
import azureml.contrib.fairness package to perform the upload:
from azureml.contrib.fairness import upload_dashboard_dictionary, download_dashboard_by_upload_id Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-fairness-aml
質問 # 83
You plan to implement an Azure Machine Learning solution. You have the following requirements:
* Run a Jupyter notebook to interactively tram a machine learning model.
* Deploy assets and workflows for machine learning proof of concept by using scripting rather than custom programming.
You need to select a development technique for each requirement
Which development technique 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
質問 # 84
You need to define a modeling strategy for ad response.
Which three 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：
1 - Implement a K-Means Clustering model
2 - Use the cluster as a feature in a Decision jungle model.
3 - Use the raw score as a feature in a Score Matchbox Recommender model Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/multiclass-decision-jungle
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/score-matchbox-recommender
質問 # 85
You are preparing to use the Azure ML SDK to run an experiment and need to create compute. You run the following code:
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://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.computetarget
質問 # 86
You have an Azure Machine Learning workspace.
You run the following code in a Python environment in which the configuration file for your workspace has been downloaded.
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：
質問 # 87
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 are using Azure Machine Learning to run an experiment that trains a classification model.
You want to use Hyperdrive to find parameters that optimize the AUC metric for the model. You configure a HyperDriveConfig for the experiment by running the following code:
You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data. The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a variable named y_predicted.
You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric. Solution: Run the following code:
Does the solution meet the goal?
- A. No
- B. Yes
正解：A
解説：
Use a solution with logging.info(message) instead.
Note: Python printing/logging example:
logging.info(message)
Destination: Driver logs, Azure Machine Learning designer
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipelines
質問 # 88
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 train and register a machine learning model.
You plan to deploy the model as a real-time web service. Applications must use key-based authentication to use the model.
You need to deploy the web service.
Solution:
Create an AciWebservice instance.
Set the value of the ssl_enabled property to True.
Deploy the model to the service.
Does the solution meet the goal?
- A. No
- B. Yes
正解：A
解説：
Explanation
Instead use only auth_enabled = TRUE
Note: Key-based authentication.
Web services deployed on AKS have key-based auth enabled by default. ACI-deployed services have key-based auth disabled by default, but you can enable it by setting auth_enabled = TRUE when creating the ACI web service. The following is an example of creating an ACI deployment configuration with key-based auth enabled.
deployment_config <- aci_webservice_deployment_config(cpu_cores = 1,
memory_gb = 1,
auth_enabled = TRUE)
Reference:
https://azure.github.io/azureml-sdk-for-r/articles/deploying-models.html
質問 # 89
You create an Azure Machine Learning workspace and install the MLflow library.
You need to tog different types of data by using the MLflow library.
Which method 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 # 90
You create a multi-class image classification deep learning model that uses a set of labeled images. You create a script file named train.py that uses the PyTorch 1.3 framework to train the model.
You must run the script by using an estimator. The code must not require any additional Python libraries to be installed in the environment for the estimator. The time required for model training must be minimized.
You need to define the estimator that will be used to run the script.
Which estimator type should you use?
- A. SKLearn
- B. TensorFlow
- C. Estimator
- D. PyTorch
正解：D
解説：
Explanation
For PyTorch, TensorFlow and Chainer tasks, Azure Machine Learning provides respective PyTorch, TensorFlow, and Chainer estimators to simplify using these frameworks.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-ml-models
質問 # 91
You have an Azure Machine Learning workspace that contains a training cluster and an inference cluster.
You plan to create a classification model by using the Azure Machine Learning designer.
You need to ensure that client applications can submit data as HTTP requests and receive predictions as responses.
Which three 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：
1 - Create a pipeline that trains a classification model and run the pipeline on the compute cluster.
2 - Create a batch inference pipeline and run the pipeline on the compute cluster.
3 - Deploy a service to the inference cluster.
質問 # 92
You are using an Azure Machine Learning workspace. You set up an environment for model testing and an environment for production.
The compute target for testing must minimize cost and deployment efforts. The compute target for production must provide fast response time, autoscaling of the deployed service, and support real-time inferencing.
You need to configure compute targets for model testing and production.
Which compute targets 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：
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target
質問 # 93
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 are creating a new experiment in Azure Learning learning Studio.
One class has a much smaller number of observations than the other classes in the training You need to select an appropriate data sampling strategy to compensate for the class imbalance.
Solution: You use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.
Does the solution meet the goal?
- A. Yes
- B. No
正解：A
質問 # 94
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 are analyzing a numerical dataset which contains missing values in several columns.
You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.
You need to analyze a full dataset to include all values.
Solution: Replace each missing value using the Multiple Imputation by Chained Equations (MICE) method.
Does the solution meet the goal?
- A. Yes
- B. No
正解：A
解説：
Explanation/Reference:
Explanation:
Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or
"Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values.
Note: Multivariate imputation by chained equations (MICE), sometimes called "fully conditional specification" or "sequential regression multiple imputation" has emerged in the statistical literature as one principled method of addressing missing data. Creating multiple imputations, as opposed to single imputations, accounts for the statistical uncertainty in the imputations. In addition, the chained equations approach is very flexible and can handle variables of varying types (e.g., continuous or binary) as well as complexities such as bounds or survey skip patterns.
References:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data
質問 # 95
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 are creating a model to predict the price of a student's artwork depending on the following variables: the student's length of education, degree type, and art form.
You start by creating a linear regression model.
You need to evaluate the linear regression model.
Solution: Use the following metrics: Accuracy, Precision, Recall, F1 score, and AUC.
Does the solution meet the goal?
- A. No
- B. Yes
正解：A
解説：
Those are metrics for evaluating classification models, instead use: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
質問 # 96
You run Azure Machine Learning training experiments. The training scripts directory contains 100 files that includes a file named. amlignore. The directory also contains subdirectories named. /outputs and./logs.
There are 20 files in the training scripts directory that must be excluded from the snapshot to the compute targets. You create a file named. gift ignore in the root of the directory. You add the names of the 20 files to the. gift ignore file. These 20 files continue to be copied to the compute targets.
You need to exclude the 20 files. What should you do?
- A. Move the file named. gift ignore to the. /outputs directory.
- B. Move the file named. gift ignore to the. /logs directory.
- C. Copy the contents of the file named. gift ignore to the file named. amlignore.
- D. Add the contents of the file named. amlignore to the file named. gift ignore.
正解：C
質問 # 97
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.
正解：
解説：
Explanation
Box 1: 500
For Random seed, type a value to use as seed for randomization. If you specify 0 (the default), a number is generated based on the system clock.
A seed value is optional, but you should provide a value if you want reproducibility across runs of the same experiment.
Here we must replicate the findings.
Box 2: Mean Absolute Error
Scenario: Given a trained model and a test dataset, you must compute the Permutation Feature Importance scores of feature variables. You need to set up the Permutation Feature Importance module to select the correct metric to investigate the model's accuracy and replicate the findings.
Regression. Choose one of the following: Precision, Recall, Mean Absolute Error , Root Mean Squared Error, Relative Absolute Error, Relative Squared Error, Coefficient of Determination References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importan
質問 # 98
You have an Azure Machine learning workspace. The workspace contains a dataset with data in a tabular form.
You plan to use the Azure Machine Learning SDK for Python vl to create a control script that will load the dataset into a pandas dataframe in preparation for model training The script will accept a parameter designating the dataset You need to complete the script.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
質問 # 99
You are analyzing a dataset by using Azure Machine Learning Studio.
YOU need to generate a statistical summary that contains the p value and the unique value count for each feature column.
Which two modules can you users? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. Execute Python Script
- B. Summarize Data
- C. Export Count Table
- D. Convert to Indicator Values
- E. Compute linear Correlation
正解：C、E
解説：
The Export Count Table module is provided for backward compatibility with experiments that use the Build Count Table (deprecated) and Count Featurizer (deprecated) modules.
E: Summarize Data statistics are useful when you want to understand the characteristics of the complete dataset. For example, you might need to know:
How many missing values are there in each column?
How many unique values are there in a feature column?
What is the mean and standard deviation for each column?
The module calculates the important scores for each column, and returns a row of summary statistics for each variable (data column) provided as input.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/export-count-table
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/summarize-data
質問 # 100
......
Microsoft DP-100（Azure上でデータサイエンスソリューションを設計・実装する）認定試験は、データサイエンスの専門家にとって最も求められている認定試験の1つです。この試験は、Azureプラットフォーム上でデータサイエンスソリューションを設計・実装するスキルや知識をテストするように設計されています。この認定試験は、データサイエンスの専門知識を検証し、Azureテクノロジーを活用して複雑なビジネス問題を解決する能力を証明する素晴らしい方法です。
Microsoft DP-100事前に試験練習テストGoShiken: ：https://www.goshiken.com/Microsoft/DP-100-mondaishu.html
DP-100練習テスト問題回答解釈:：https://drive.google.com/open?id=1YYRr1z-Asrx6MOuXTbneTKCfTsw6Jps4