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| Section | Weight | Objectives |
|---|---|---|
| Prepare a model for deployment | 20-25% | - Manage deployment assets
|
| Deploy and retrain models | 10-15% | - Monitor deployed models
|
| Explore data and train models | 35-40% | - Run experiments and train models
|
| Design and prepare a machine learning solution | 20-25% | - Prepare development environments
|
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NEW QUESTION # 371
You create an Azure Machine Learning workspace.
You plan to write an Azure Machine Learning SDK for Python v2 script that logs an image for an experiment.
The logged image must be available from the images tab in Azure Machine Learning Studio.
You need to complete the script.
Which code segments should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 372
You create an MLflow model
You must deploy the model to Azure Machine Learning for batch inference.
You need to create the batch deployment.
Which two components should you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point
Answer: B,D
NEW QUESTION # 373
You plan to preprocess text from CSV files. You load the Azure Machine Learning Studio default stop words list.
You need to configure the Preprocess Text module to meet the following requirements:
* Ensure that multiple related words from a single canonical form.
* Remove pipe characters from text.
* Remove words to optimize information retrieval.
Which three options should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Remove stop words
Remove words to optimize information retrieval.
Remove stop words: Select this option if you want to apply a predefined stopword list to the text column. Stop word removal is performed before any other processes.
Box 2: Lemmatization
Ensure that multiple related words from a single canonical form.
Lemmatization converts multiple related words to a single canonical form Box 3: Remove special characters Remove special characters: Use this option to replace any non-alphanumeric special characters with the pipe | character.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/preprocess-text
NEW QUESTION # 374
Your Azure Machine Learning workspace has a dataset named real_estate_data. A sample of the data in the dataset follows.
You want to use automated machine learning to find the best regression model for predicting the price column.
You need to configure an automated machine learning experiment using the Azure Machine Learning SDK.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: training_data
The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column). If training_data is specified, then the label_column_name parameter must also be specified.
Box 2: validation_data
Provide validation data: In this case, you can either start with a single data file and split it into training and validation sets or you can provide a separate data file for the validation set. Either way, the validation_data parameter in your AutoMLConfig object assigns which data to use as your validation set.
Example, the following code example explicitly defines which portion of the provided data in dataset to use for training and validation.
dataset = Dataset.Tabular.from_delimited_files(data)
training_data, validation_data = dataset.random_split(percentage=0.8, seed=1) automl_config = AutoMLConfig(compute_target = aml_remote_compute, task = 'classification', primary_metric = 'AUC_weighted', training_data = training_data, validation_data = validation_data, label_column_name = 'Class' ) Box 3: label_column_name label_column_name:
The name of the label column. If the input data is from a pandas.DataFrame which doesn't have column names, column indices can be used instead, expressed as integers.
This parameter is applicable to training_data and validation_data parameters.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.auto
NEW QUESTION # 375
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.
Answer:
Explanation:
Explanation:
NEW QUESTION # 376
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