[2024年03月最新リリース] 合格できるDP-100試験にはリアル問題とアンサー
合格できるDP-100レビューガイド、頼もしいDP-100テストエンジン
Microsoft DP-100認定試験は、データサイエンスでのキャリアを促進しようとする専門家にとって貴重な資格です。それは、候補者がAzureに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ために必要なスキルと知識を持っていることを雇用主に示しています。この認定はMicrosoftによって認識され、業界内で非常に尊敬されています。
DP-100試験では、データの調査と準備、モデリング、視覚化、展開など、幅広いトピックをカバーしています。候補者は、Azure Machine Learning、Azure Databricks、Azure Cognitiveサービスなど、さまざまなAzureツールやサービスを使用して、エンドツーエンドのデータサイエンスソリューションを構築する能力についてテストされます。また、機械学習、統計分析、データの視覚化の原則、および利害関係者に洞察と調査結果を伝える能力の理解を示す必要があります。この試験に合格することは、Azureベースのデータサイエンスソリューションの専門知識を紹介したいデータサイエンスの専門家にとって重要な成果です。
質問 # 104
You are a data scientist creating a linear regression model.
You need to determine how closely the data fits the regression line.
Which metric should you review?
- A. Precision
- B. Recall
- C. Root Mean Square Error
- D. Coefficient of determination
- E. Mean absolute error
正解：D
解説：
Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.
Incorrect Answers:
A: Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.
C: Recall is the fraction of all correct results returned by the model.
D: Precision is the proportion of true results over all positive results.
E: Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
質問 # 105
You train a classification model by using a decision tree algorithm.
You create an estimator by running the following Python code. The variable feature_names is a list of all feature names, and class_names is a list of all class names.
from interpret.ext.blackbox import TabularExplainer
You need to explain the predictions made by the model for all classes by determining the importance of all features.
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/machine-learning/how-to-machine-learning-interpretability-aml
質問 # 106
You create an Azure Machine Learning compute resource to train models. The compute resource is configured as follows:
* Minimum nodes: 2
* Maximum nodes: 4
You must decrease the minimum number of nodes and increase the maximum number of nodes to the following values:
* Minimum nodes: 0
* Maximum nodes: 8
You need to reconfigure the compute resource.
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. Use the Azure Machine Learning designer.
- B. Use the Azure portal.
- C. Use the Azure Machine Learning studio.
- D. Run the refresh_state() method of the BatchCompute class in the Python SDK.
- E. Run the update method of the AmlCompute class in the Python SDK.
正解：B、C、E
解説：
A: You can manage assets and resources in the Azure Machine Learning studio.
B: The update(min_nodes=None, max_nodes=None, idle_seconds_before_scaledown=None) of the AmlCompute class updates the ScaleSettings for this AmlCompute target.
C: To change the nodes in the cluster, use the UI for your cluster in the Azure portal.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute(class)
質問 # 107
You are running a training experiment on remote compute in Azure Machine Learning.
The experiment is configured to use a conda environment that includes the mlflow and azureml-contrib-run packages.
You must use MLflow as the logging package for tracking metrics generated in the experiment You need to complete the script for the experiment How should you complete the code? To answer, select the appropriate options in the answer area.
正解：
解説：
質問 # 108
You are the owner of an Azure Machine Learning workspace.
You must prevent the creation or deletion of compute resources by using a custom role. You must allow all other operations inside the workspace.
You need to configure the custom role.
How should you complete the configuration? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation
Graphical user interface, application Description automatically generated
Graphical user interface, application Description automatically generated
Box 1: Microsoft.MachineLearningServices/workspaces/*/read
Reader role: Read-only actions in the workspace. Readers can list and view assets, including datastore credentials, in a workspace. Readers can't create or update these assets.
Box 2: Microsoft.MachineLearningServices/workspaces/*/write
If the roles include Actions that have a wildcard (*), the effective permissions are computed by subtracting the NotActions from the allowed Actions.
Box 3: Box 2: Microsoft.MachineLearningServices/workspaces/computes/*/delete Box 4: Microsoft.MachineLearningServices/workspaces/computes/*/write Reference:
https://docs.microsoft.com/en-us/azure/role-based-access-control/overview#how-azure-rbac-determines-if-a-user
質問 # 109
You use the following code to run a script as an experiment in Azure Machine Learning:
You must identify the output files that are generated by the experiment run.
You need to add code to retrieve the output file names.
Which code segment should you add to the script?
- A. files= run.get_file_names()
- B. files = run.get_properties()
- C. files = run.get_details_with_logs()
- D. files = run.get_details()
- E. files = run.get_metrics()
正解：A
解説：
Explanation
You can list all of the files that are associated with this run record by called run.get_file_names() Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-track-experiments
質問 # 110
You are creating a machine learning model.
You need to identify outliers in the data.
Which two visualizations can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
NOTE: Each correct selection is worth one point.
- A. box plot
- B. Venn diagram
- C. random forest diagram
- D. scatter
- E. ROC curve
正解：A、D
解説：
The box-plot algorithm can be used to display outliers.
One other way to quickly identify Outliers visually is to create scatter plots.
Reference:
https://blogs.msdn.microsoft.com/azuredev/2017/05/27/data-cleansing-tools-in-azure-machine-learning/
質問 # 111
You use Azure Machine Learning studio to analyze an mltable data asset containing a decimal column named column1. You need to verify that the column1 values are normally distributed.
Which statistic should you use?
- A. Type
- B. Max
- C. Mean
- D. Profile
正解：D
質問 # 112
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.
正解：
解説：
質問 # 113
You create an Azure Machine Learning compute target named ComputeOne by using the STANDARD_D1 virtual machine image.
You define a Python variable named was that references the Azure Machine Learning workspace. You run the following Python 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://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.computetarget
質問 # 114
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 Studio to perform feature engineering on a dataset.
You need to normalize values to produce a feature column grouped into bins.
Solution: Apply an Entropy Minimum Description Length (MDL) binning mode.
Does the solution meet the goal?
- A. No
- B. Yes
正解：B
解説：
Entropy MDL binning mode: This method requires that you select the column you want to predict and the column or columns that you want to group into bins. It then makes a pass over the data and attempts to determine the number of bins that minimizes the entropy. In other words, it chooses a number of bins that allows the data column to best predict the target column. It then returns the bin number associated with each row of your data in a column named <colname>quantized.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins
質問 # 115
You are evaluating a completed binary classification machine.
You need to use the precision as the evaluation metric.
Which visualization should you use?
- A. coefficient of determination
- B. Receiver Operating Characteristic CROC) curve
- C. Gradient descent
- D. scatter plot
正解：B
質問 # 116
You are planning to register a trained model in an Azure Machine Learning workspace.
You must store additional metadata about the model in a key-value format. You must be able to add new metadata and modify or delete metadata after creation.
You need to register the model.
Which parameter should you use?
- A. description
- B. cags
- C. properties
- D. model_framework
正解：C
解説：
azureml.core.Model.properties:
Dictionary of key value properties for the Model. These properties cannot be changed after registration, however new key value pairs can be added.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model
質問 # 117
You are using C-Support Vector classification to do a multi-class classification with an unbalanced training dataset. The C-Support Vector classification using Python code shown below:
You need to evaluate the C-Support Vector classification code.
Which evaluation statement 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
Box 1: Automatically adjust weights inversely proportional to class frequencies in the input data The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)).
Box 2: Penalty parameter
Parameter: C : float, optional (default=1.0)
Penalty parameter C of the error term.
References:
https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html
質問 # 118
You create a multi-class image classification deep learning model.
The model must be retrained monthly with the new image data fetched from a public web portal. You create an Azure Machine Learning pipeline to fetch new data, standardize the size of images and retrain the model.
You need to use the Azure Machine Learning Python SEX v2 to configure the schedule for the pipeline. The schedule should be defined by using the frequency and interval properties with frequency set to month' and interval set to "1:
Which three classes should you instantiate 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 - PipelineJob
2 - Recurrence Trigger
3 - JobSchedule
質問 # 119
You need to define a process for penalty event detection.
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 - Vary the length of frequency bands between modeling epochs.
2 - Standardize to mono audio clips.
3 - Use an Inverse Fourier transform on frequency changes over time.
質問 # 120
You use an Azure Machine Learning workspace.
You create the following Python 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：
Explanation
Graphical user interface, text, application Description automatically generated
Box 1: No
Environment is a required parameter. The environment to use for the run. If no environment is specified, azureml.core.runconfig.DEFAULT_CPU_IMAGE will be used as the Docker image for the run.
The following example shows how to instantiate a new environment.
from azureml.core import Environment
myenv = Environment(name="myenv")
Box 2: Yes
Parameter compute_target: The compute target where training will happen. This can either be a ComputeTarget object, the name of an existing ComputeTarget, or the string "local". If no compute target is specified, your local machine will be used.
Box 3: Yes
Parameter source_directory. A local directory containing code files needed for a run.
Parameter script. The file path relative to the source_directory of the script to be run.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.scriptrunconfig
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.environment.environment
質問 # 121
You need to configure the Feature Based Feature Selection module based on the experiment requirements and datasets.
How should you configure the module properties? 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: Mutual Information.
The mutual information score is particularly useful in feature selection because it maximizes the mutual information between the joint distribution and target variables in datasets with many dimensions.
Box 2: MedianValue
MedianValue is the feature column, , it is the predictor of the dataset.
Scenario: The MedianValue and AvgRoomsinHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/filter-based-feature-selection
質問 # 122
You register a file dataset named csvjolder that references a folder. The folder includes multiple com ma-separated values (CSV) files in an Azure storage blob container. You plan to use the following code to run a script that loads data from the file dataset. You create and instantiate the following variables:
You have the following code:
You need to pass the dataset to ensure that the script can read the files it references. Which code segment should you insert to replace the code comment?
- A.
- B.
- C.
- D.
正解：C
解説：
Explanation
Example:
from azureml.train.estimator import Estimator
script_params = {
# to mount files referenced by mnist dataset
'--data-folder': mnist_file_dataset.as_named_input('mnist_opendataset').as_mount(),
'--regularization': 0.5
}
est = Estimator(source_directory=script_folder,
script_params=script_params,
compute_target=compute_target,
environment_definition=env,
entry_script='train.py')
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-train-models-with-aml
質問 # 123
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