QUESTION: 89

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?

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

Answer(s): D

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QUESTION: 90

You are working on a binary classification ML algorithm that detects whether an image of a classified scanned document contains a company’s logo. In the dataset, 96% of examples don’t have the logo, so the dataset is very skewed. Which metrics would give you the most confidence in your model?

F-score where recall is weighed more than precision
RMSE
F1 score
F-score where precision is weighed more than recall

Answer(s): A

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QUESTION: 91

You work on the data science team for a multinational beverage company. You need to develop an ML model to predict the company’s profitability for a new line of naturally flavored bottled waters in different locations. You are provided with historical data that includes product types, product sales volumes, expenses, and profits for all regions. What should you use as the input and output for your model?

Use latitude, longitude, and product type as features. Use profit as model output.
Use latitude, longitude, and product type as features. Use revenue and expenses as model outputs.
Use product type and the feature cross of latitude with longitude, followed by binning, as features. Use profit as model output.
Use product type and the feature cross of latitude with longitude, followed by binning, as features. Use revenue and expenses as model outputs.

Answer(s): C

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QUESTION: 92

You work as an ML engineer at a social media company, and you are developing a visual filter for users’ profile photos. This requires you to train an ML model to detect bounding boxes around human faces. You want to use this filter in your company’s iOS-based mobile phone application. You want to minimize code development and want the model to be optimized for inference on mobile phones. What should you do?

Train a model using AutoML Vision and use the “export for Core ML” option.
Train a model using AutoML Vision and use the “export for Coral” option.
Train a model using AutoML Vision and use the “export for TensorFlow.js” option.
Train a custom TensorFlow model and convert it to TensorFlow Lite (TFLite).

Answer(s): A

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Free Professional Machine Learning Engineer Exam Braindumps (page: 23)

QUESTION: 89

QUESTION: 90

QUESTION: 91

QUESTION: 92