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While running a model training pipeline on Vertex Al, you discover that the evaluation step is failing because of an out-of-memory error. You are currently using TensorFlow Model Analysis (TFMA) with a standard Evaluator TensorFlow Extended (TFX) pipeline component for the evaluation step. You want to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead. What should you do?

A. Include the flag -runner=DataflowRunner in beam_pipeline_args to run the evaluation step on Dataflow. B. Move the evaluation step out of your pipeline and run it on custom Compute Engine VMs with sufficient memory. C. Migrate your pipeline to Kubeflow hosted on Google Kubernetes Engine, and specify the appropriate node parameters for the evaluation step. D. Add tfma.MetricsSpec () to limit the number of metrics in the evaluation step.

You are developing an ML model using a dataset with categorical input variables. You have randomly split half of the data into training and test sets. After applying one-hot encoding on the categorical variables in the training set, you discover that one categorical variable is missing from the test set. What should you do?

A. Use sparse representation in the test set. B. Randomly redistribute the data, with 70% for the training set and 30% for the test set C. Apply one-hot encoding on the categorical variables in the test data D. Collect more data representing all categories

You work for a bank and are building a random forest model for fraud detection. You have a dataset that includes transactions, of which 1% are identified as fraudulent. Which data transformation strategy would likely improve the performance of your classifier?

A. Modify the target variable using the Box-Cox transformation. B. Z-normalize all the numeric features. C. Oversample the fraudulent transaction 10 times. D. Log transform all numeric features.

You are developing a classification model to support predictions for your company’s various products. The dataset you were given for model development has class imbalance You need to minimize false positives and false negatives What evaluation metric should you use to properly train the model?

A. F1 score B. Recall C. Accuracy D. Precision

You are training an object detection machine learning model on a dataset that consists of three million X-ray images, each roughly 2 GB in size. You are using Vertex AI Training to run a custom training application on a Compute Engine instance with 32-cores, 128 GB of RAM, and 1 NVIDIA P100 GPU. You notice that model training is taking a very long time. You want to decrease training time without sacrificing model performance. What should you do?

A. Increase the instance memory to 512 GB, and increase the batch size. B. Replace the NVIDIA P100 GPU with a K80 GPU in the training job. C. Enable early stopping in your Vertex AI Training job. D. Use the tf.distribute.Strategy API and run a distributed training job.

You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?

A. Train a TensorFlow model on Vertex AI. B. Train a classification Vertex AutoML model. C. Run a logistic regression job on BigQuery ML. D. Use scikit-learn in Vertex AI Workbench user-managed notebooks with pandas library.

You recently developed a deep learning model. To test your new model, you trained it for a few epochs on a large dataset. You observe that the training and validation losses barely changed during the training run. You want to quickly debug your model. What should you do first?

A. Verify that your model can obtain a low loss on a small subset of the dataset B. Add handcrafted features to inject your domain knowledge into the model C. Use the Vertex AI hyperparameter tuning service to identify a better learning rate D. Use hardware accelerators and train your model for more epochs

You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company’s manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?

A. Develop a custom TensorFlow regression model, and optimize it using Vertex AI Training. B. Develop a regression model using BigQuery ML. C. Develop a custom scikit-learn regression model, and optimize it using Vertex AI Training. D. Develop a custom PyTorch regression model, and optimize it using Vertex AI Training.

Your organization manages an online message board. A few months ago, you discovered an increase in toxic language and bullying on the message board. You deployed an automated text classifier that flags certain comments as toxic or harmful. Now some users are reporting that benign comments referencing their religion are being misclassified as abusive. Upon further inspection, you find that your classifier's false positive rate is higher for comments that reference certain underrepresented religious groups. Your team has a limited budget and is already overextended. What should you do?

A. Add synthetic training data where those phrases are used in non-toxic ways. B. Remove the model and replace it with human moderation. C. Replace your model with a different text classifier. D. Raise the threshold for comments to be considered toxic or harmful.

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 Vertex AI. You need to determine which customer attribute has the most predictive power for each prediction served by the model. What should you do?

A. 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. B. Use Vertex Explainable AI. Submit each prediction request with the explain' keyword to retrieve feature attributions using the sampled Shapley method. C. Use Vertex AI Workbench user-managed notebooks to perform a Lasso regression analysis on your model, which will eliminate features that do not provide a strong signal. 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.