QUESTION: 69

You need to build an ML model for a social media application to predict whether a user’s submitted profile photo meets the requirements. The application will inform the user if the picture meets the requirements. How should you build a model to ensure that the application does not falsely accept a non-compliant picture?

Use AutoML to optimize the model’s recall in order to minimize false negatives.
Use AutoML to optimize the model’s F1 score in order to balance the accuracy of false positives and false negatives.
Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that meet the profile photo requirements.
Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that do not meet the profile photo requirements.

Answer(s): C

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QUESTION: 70

You lead a data science team at a large international corporation. Most of the models your team trains are large-scale models using high-level TensorFlow APIs on AI Platform with GPUs. Your team usually takes a few weeks or months to iterate on a new version of a model. You were recently asked to review your team’s spending. How should you reduce your Google Cloud compute costs without impacting the model’s performance?

Use AI Platform to run distributed training jobs with checkpoints.
Use AI Platform to run distributed training jobs without checkpoints.
Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs with checkpoints.
Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs without checkpoints.

Answer(s): D

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QUESTION: 71

You need to train a regression model based on a dataset containing 50,000 records that is stored in BigQuery. The data includes a total of 20 categorical and numerical features with a target variable that can include negative values. You need to minimize effort and training time while maximizing model performance. What approach should you take to train this regression model?

Create a custom TensorFlow DNN model
Use BQML XGBoost regression to train the model.
Use AutoML Tables to train the model without early stopping.
Use AutoML Tables to train the model with RMSLE as the optimization objective.

Answer(s): A

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QUESTION: 72

You are building a linear model with over 100 input features, all with values between –1 and 1. You suspect that many features are non-informative. You want to remove the non-informative features from your model while keeping the informative ones in their original form. Which technique should you use?

Use principal component analysis (PCA) to eliminate the least informative features.
Use L1 regularization to reduce the coefficients of uninformative features to 0.
After building your model, use Shapley values to determine which features are the most informative.
Use an iterative dropout technique to identify which features do not degrade the model when removed.

Answer(s): B

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Free Professional Machine Learning Engineer Exam Braindumps (page: 18)

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QUESTION: 72