최신 DP-100 무료덤프 - Microsoft Designing and Implementing a Data Science Solution on Azure
You manage an Azure Machine Learning workspace. The Pylhon scrip! named scriptpy reads an argument named training_data. The trainlng.data argument specifies the path to the training data in a file named datasetl.
csv.
You plan to run the scriptpy Python script as a command job that trains a machine learning model.
You need to provide the command to pass the path for the datasct as a parameter value when you submit the script as a training job.
Solution: python script.py -training_data ${{inputs,training_data}}
Does the solution meet the goal?
csv.
You plan to run the scriptpy Python script as a command job that trains a machine learning model.
You need to provide the command to pass the path for the datasct as a parameter value when you submit the script as a training job.
Solution: python script.py -training_data ${{inputs,training_data}}
Does the solution meet the goal?
정답: B
You create an Azure Machine Learning workspace. The workspace contains a dataset named sample.dataset, a compute instance, and a compute cluster. You must create a two-stage pipeline that will prepare data in the dataset and then train and register a model based on the prepared data. The first stage of the pipeline contains the following code:
You need to identify the location containing the output of the first stage of the script that you can use as input for the second stage. Which storage location should you use?
You need to identify the location containing the output of the first stage of the script that you can use as input for the second stage. Which storage location should you use?
정답: B
You are evaluating a completed binary classification machine.
You need to use the precision as the evaluation metric.
Which visualization should you use?
You need to use the precision as the evaluation metric.
Which visualization should you use?
정답: A
설명: (DumpTOP 회원만 볼 수 있음)
You manage an Azure Machine Learning workspace. You create an experiment named experiment1 by using the Azure Machine Learning Python SDK v2 and MLflow.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
정답:
Explanation:
You need to correct the model fit issue.
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.
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.
정답:
Explanation:
Step 1: Augment the data
Scenario: Columns in each dataset contain missing and null values. The datasets also contain many outliers.
Step 2: Add the Bayesian Linear Regression module.
Scenario: You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.
Step 3: Configure the regularization weight.
Regularization typically is used to avoid overfitting. For example, in L2 regularization weight, type the value to use as the weight for L2 regularization. We recommend that you use a non-zero value to avoid overfitting.
Scenario:
Model fit: The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.
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 an Azure Machine Learning model.
You plan to deploy the model to an online end point.
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution:
Create a Kubernetes online endpoint and set the value of its auth-mode parameter to amyl Token. Deploy the model to the online endpoint.
Does the solution meet the goal?
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 an Azure Machine Learning model.
You plan to deploy the model to an online end point.
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution:
Create a Kubernetes online endpoint and set the value of its auth-mode parameter to amyl Token. Deploy the model to the online endpoint.
Does the solution meet the goal?
정답: A
You train and register an Azure Machine Learning model
You plan to deploy the model to an online endpoint
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution:
Create a managed online endpoint and set the value of its auth.mode parameter to aml.token. Deploy the model to the online endpoint.
Does the solution meet the goal?
You plan to deploy the model to an online endpoint
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution:
Create a managed online endpoint and set the value of its auth.mode parameter to aml.token. Deploy the model to the online endpoint.
Does the solution meet the goal?
정답: A
You are using a decision tree algorithm. You have trained a model that generalizes well at a tree depth equal to 10.
You need to select the bias and variance properties of the model with varying tree depth values.
Which properties should you select for each tree depth? To answer, select the appropriate options in the answer area.
You need to select the bias and variance properties of the model with varying tree depth values.
Which properties should you select for each tree depth? To answer, select the appropriate options in the answer area.
정답:
Explanation:
In decision trees, the depth of the tree determines the variance. A complicated decision tree (e.g. deep) has low bias and high variance.
Note: In statistics and machine learning, the bias-variance tradeoff is the property of a set of predictive models whereby models with a lower bias in parameter estimation have a higher variance of the parameter estimates across samples, and vice versa. Increasing the bias will decrease the variance. Increasing the variance will decrease the bias.
References:
https://machinelearningmastery.com/gentle-introduction-to-the-bias-variance-trade-off-in-machine-learning/
You use Azure Machine Learning Designer to load the following datasets into an experiment:
Data set 1
Dataset 2
You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.
Solution: Use the Apply Transformation component.
Does the solution meet the goal?
Data set 1
Dataset 2
You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.
Solution: Use the Apply Transformation component.
Does the solution meet the goal?
정답: A
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?
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?
정답: A
설명: (DumpTOP 회원만 볼 수 있음)
You are designing an Azure Machine Leaning solution by using the Python SDK v2.
You must train and deploy the solution by using a compute target. The compute target must meet the following requirements:
* Enable the use of on-premises compute resources.
* Support autoscalling.
You need to configure a compute target for training and inference.
Which compute target t should you configure?
To answer select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
You must train and deploy the solution by using a compute target. The compute target must meet the following requirements:
* Enable the use of on-premises compute resources.
* Support autoscalling.
You need to configure a compute target for training and inference.
Which compute target t should you configure?
To answer select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답:
Explanation:
You use Azure Machine Learning to train a model based on a dataset named dataset1.
You define a dataset monitor and create a dataset named dataset2 that contains new data.
You need to compare dataset1 and dataset2 by using the Azure Machine Learning SDK for Python.
Which method of the DataDriftDetector class should you use?
You define a dataset monitor and create a dataset named dataset2 that contains new data.
You need to compare dataset1 and dataset2 by using the Azure Machine Learning SDK for Python.
Which method of the DataDriftDetector class should you use?
정답: C
설명: (DumpTOP 회원만 볼 수 있음)
You manage an Azure Machine learning workspace.
You build a custom model you must log with Mlftow. The custom model includes the following:
* The model is not natively supported by Mlflow.
* The model cannot be serialized in Pickle format.
* The model source code is complex.
* The Python library tor the model must be packaged with the model.
You need to create a custom model flavor to enable logging with ML. flow.
What should you use?
You build a custom model you must log with Mlftow. The custom model includes the following:
* The model is not natively supported by Mlflow.
* The model cannot be serialized in Pickle format.
* The model source code is complex.
* The Python library tor the model must be packaged with the model.
You need to create a custom model flavor to enable logging with ML. flow.
What should you use?
정답: A