QUESTION: 29 Exam Topic: First Question sets

You want to train a classification model using data located in a comma-separated values (CSV) file.
The classification model will be trained via the Automated Machine Learning interface using the Classification task type.
You have been informed that only linear models need to be assessed by the Automated Machine Learning.
Which of the following actions should you take?

You should disable deep learning.
You should enable automatic featurization.
You should disable automatic featurization.
You should set the task type to Forecasting.

Answer(s): A

Explanation:

Reference:

https://econml.azurewebsites.net/spec/estimation/dml.html
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-automated-ml-for-ml-models

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QUESTION: 30 Exam Topic: First Question sets

You are preparing to train a regression model via automated machine learning. The data available to you has features with missing values, as well as categorical features with little discrete values.
You want to make sure that automated machine learning is configured as follows:
-missing values must be automatically imputed.
-categorical features must be encoded as part of the training task.
Which of the following actions should you take?

You should make use of the featurization parameter with the 'auto' value pair.
You should make use of the featurization parameter with the 'off' value pair.
You should make use of the featurization parameter with the 'on' value pair.
You should make use of the featurization parameter with the 'FeaturizationConfig' value pair.

Answer(s): A

Explanation:

Featurization str or FeaturizationConfig
Values: 'auto' / 'off' / FeaturizationConfig
Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used.
Column type is automatically detected. Based on the detected column type preprocessing/featurization is done as follows:
Categorical: Target encoding, one hot encoding, drop high cardinality categories, impute missing values.
Numeric: Impute missing values, cluster distance, weight of evidence.
DateTime: Several features such as day, seconds, minutes, hours etc.
Text: Bag of words, pre-trained Word embedding, text target encoding.

Reference:

https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig

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QUESTION: 31 Exam Topic: First Question sets

You make use of Azure Machine Learning Studio to develop a linear regression model. You perform an experiment to assess various algorithms.
Which of the following is an algorithm that reduces the variances between actual and predicted values?

Fast Forest Quantile Regression
Poisson Regression
Boosted Decision Tree Regression
Linear Regression

Answer(s): D

Explanation:

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/algorithm-module-reference/boosted-decision-tree-regression https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression

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QUESTION: 32 Exam Topic: First Question sets

This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.
You have been tasked with constructing a machine learning model that translates language text into a different language text.
The machine learning model must be constructed and trained to learn the sequence of the.
Recommendation: You make use of Convolutional Neural Networks (CNNs).
Will the requirements be satisfied?

Answer(s): B

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QUESTION: 29 Exam Topic: First Question sets

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QUESTION: 32 Exam Topic: First Question sets