Professional Machine Learning Engineer Practice Test

Professional Machine Learning Engineer Practice Test practice questions, page 15 of 15, with answer review and explanations.

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?

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

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?

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

Question 73

You work for a global footwear retailer and need to predict when an item will be out of stock based on historical inventory data Customer behavior is highly dynamic since footwear demand is influenced by many different factors. You want to serve models that are trained on all available data, but track your performance on specific subsets of data before pushing to production. What is the most streamlined and reliable way to perform this validation?

  1. Use then TFX ModelValidator tools to specify performance metrics for production readiness.
  2. Use k-fold cross-validation as a validation strategy to ensure that your model is ready for production.
  3. Use the last relevant week of data as a validation set to ensure that your model is performing accurately on current data.
  4. Use the entire dataset and treat the area under the receiver operating characteristics curve (AUC ROC) as the main metric.

Question 74

You have deployed a model on Vertex AI for real-time inference. During an online prediction request, you get an “Out of Memory” error. What should you do?

  1. Use batch prediction mode instead of online mode.
  2. Send the request again with a smaller batch of instances.
  3. Use base64 to encode your data before using it for prediction.
  4. Apply for a quota increase for the number of prediction requests.

Question 75

You work at a subscription-based company. You have trained an ensemble of trees and neural networks to predict customer churn, which is the likelihood that customers will not renew their yearly subscription. The average prediction is a 15% churn rate, but for a particular customer the model predicts that they are 70% likely to churn. The customer has a product usage history of 30%, is located in New York City, and became a customer in 1997. You need to explain the difference between the actual prediction, a 70% churn rate, and the average prediction. You want to use Vertex Explainable AI. What should you do?

  1. Train local surrogate models to explain individual predictions.
  2. Configure sampled Shapley explanations on Vertex Explainable AI.
  3. Configure integrated gradients explanations on Vertex Explainable AI.
  4. Measure the effect of each feature as the weight of the feature multiplied by the feature value.