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| Section | Objectives |
|---|---|
| Train machine learning models | - Train models using Azure Machine Learning - Tune hyperparameters and evaluate models |
| Explore and analyze data | - Perform exploratory data analysis - Ingest and prepare data for modeling |
| Deploy and consume models | - Monitor deployed models and endpoints - Deploy models to endpoints |
| Optimize and manage models | - Track experiments and manage model lifecycle - Improve model performance |
| Design and prepare a machine learning solution | - Manage compute and data assets - Plan and configure Azure Machine Learning workspace - Select appropriate Azure services for machine learning workloads |
Während andere Leute noch überall die Prüfungsunterlagen für Microsoft DP-100 suchen, üben Sie schon verschiedene Prüfungsaufgaben. Sie können im Vorbereitungsphase schon ganz vorne liegen. Wir Fast2test bieten Ihnen Microsoft DP-100 Prüfungsunterlagen mit reichliche Ressourcen. Sie dürfen auch die ganz realistische Prüfungsumwelt der Microsoft DP-100 Prüfung damit erfahren.
185. Frage
You create a batch inference pipeline by using the Azure ML SDK.
You configure the pipeline parameters by executing the following code:
You need to obtain the output from the pipeline execution.
Where will you find the output?
Antwort: B
Begründung:
output_action (str): How the output is to be organized. Currently supported values are
'append_row' and 'summary_only'.
'append_row' ?All values output by run() method invocations will be aggregated into one unique file named parallel_run_step.txt that is created in the output location.
'summary_only'
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/ azureml.contrib.pipeline.steps.parallelrunconfig
186. Frage
Drag and Drop Question
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.
Antwort:
Begründung:
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.
Incorrect Answers:
Multiclass Decision Jungle module:
Decision jungles are a recent extension to decision forests. A decision jungle consists of an ensemble of decision directed acyclic graphs (DAGs).
L-BFGS:
L-BFGS stands for "limited memory Broyden-Fletcher-Goldfarb-Shanno". It can be found in the wwo-Class Logistic Regression module, which is used to create a logistic regression model that can be used to predict two (and only two) outcomes.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regr ession
187. Frage
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: Remove the entire column that contains the missing data point.
Does the solution meet the goal?
Antwort: A
Begründung:
Explanation
Use the Multiple Imputation by Chained Equations (MICE) method.
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
188. Frage
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?
Antwort: A
189. Frage
space and set up a development environment. You plan to train a deep neural network (DNN) by using the Tensorflow framework and by using estimators to submit training scripts.
You must optimize computation speed for training runs.
You need to choose the appropriate estimator to use as well as the appropriate training compute target configuration.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Box 1: Tensorflow
TensorFlow represents an estimator for training in TensorFlow experiments.
Box 2: 12 vCPU, 112 GB memory..,2 GPU,..
Use GPUs for the deep neural network.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn
190. Frage
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