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Online Professional-Machine-Learning-Engineer Questions Video

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Question 28

You are building an ML model to predict trends in the stock market based on a wide range of factors. While exploring the data, you notice that some features have a large range. You want to ensure that the features with the largest magnitude don’t overfit the model. What should you do?

Options:

A.

Standardize the data by transforming it with a logarithmic function.

B.

Apply a principal component analysis (PCA) to minimize the effect of any particular feature.

C.

Use a binning strategy to replace the magnitude of each feature with the appropriate bin number.

D.

Normalize the data by scaling it to have values between 0 and 1.

Question 29

You are an ML engineer at a bank. You have developed a binary classification model using AutoML Tables to predict whether a customer will make loan payments on time. The output is used to approve or reject loan requests. One customer’s loan request has been rejected by your model, and the bank’s risks department is asking you to provide the reasons that contributed to the model’s decision. What should you do?

Options:

A.

Use local feature importance from the predictions.

B.

Use the correlation with target values in the data summary page.

C.

Use the feature importance percentages in the model evaluation page.

D.

Vary features independently to identify the threshold per feature that changes the classification.

Question 30

You work for a magazine distributor and need to build a model that predicts which customers will renew their subscriptions for the upcoming year. Using your company’s historical data as your training set, you created a TensorFlow model and deployed it to AI Platform. You need to determine which customer attribute has the most predictive power for each prediction served by the model. What should you do?

Options:

A.

Use AI Platform notebooks to perform a Lasso regression analysis on your model, which will eliminate features that do not provide a strong signal.

B.

Stream prediction results to BigQuery. Use BigQuery’s CORR(X1, X2) function to calculate the Pearson correlation coefficient between each feature and the target variable.

C.

Use the AI Explanations feature on AI Platform. Submit each prediction request with the ‘explain’ keyword to retrieve feature attributions using the sampled Shapley method.

D.

Use the What-If tool in Google Cloud to determine how your model will perform when individual features are excluded. Rank the feature importance in order of those that caused the most significant performance drop when removed from the model.

Question 31

You work for a retail company. You have been tasked with building a model to determine the probability of churn for each customer. You need the predictions to be interpretable so the results can be used to develop marketing campaigns that target at-risk customers. What should you do?

Options:

A.

Build a random forest regression model in a Vertex Al Workbench notebook instance Configure the model to generate feature importance’s after the model is trained.

B.

Build an AutoML tabular regression model Configure the model to generate explanations when it makes predictions.

C.

Build a custom TensorFlow neural network by using Vertex Al custom training Configure the model to generate explanations when it makes predictions.

D.

Build a random forest classification model in a Vertex Al Workbench notebook instance Configure the model to generate feature importance’s after the model is trained.

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Exam Name: Google Professional Machine Learning Engineer
Last Update: Nov 1, 2024
Questions: 270
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