Professional Machine Learning Engineer Practice Test

Professional Machine Learning Engineer Practice Test practice questions, page 12 of 15, with answer review and explanations.

Question 56

You work for a credit card company and have been asked to create a custom fraud detection model based on historical data using AutoML Tables. You need to prioritize detection of fraudulent transactions while minimizing false positives. Which optimization objective should you use when training the model?

  1. An optimization objective that minimizes Log loss
  2. An optimization objective that maximizes the Precision at a Recall value of 0.50
  3. An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value
  4. An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) value

Question 57

Your company manages a video sharing website where users can watch and upload videos. You need to create an ML model to predict which newly uploaded videos will be the most popular so that those videos can be prioritized on your company's website. Which result should you use to determine whether the model is successful?

  1. The model predicts videos as popular if the user who uploads them has over 10,000 likes.
  2. The model predicts 97.5% of the most popular clickbait videos measured by number of clicks.
  3. The model predicts 95% of the most popular videos measured by watch time within 30 days of being uploaded.
  4. The Pearson correlation coefficient between the log-transformed number of views after 7 days and 30 days after publication is equal to 0.

Question 58

You are working on a Neural Network-based project. The dataset provided to you has columns with different ranges. While preparing the data for model training, you discover that gradient optimization is having difficulty moving weights to a good solution. What should you do?

  1. Use feature construction to combine the strongest features.
  2. Use the representation transformation (normalization) technique.
  3. Improve the data cleaning step by removing features with missing values.
  4. Change the partitioning step to reduce the dimension of the test set and have a larger training set.

Question 59

Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?

  1. Use Kubeflow Pipelines to execute the experiments. Export the metrics file, and query the results using the Kubeflow Pipelines API.
  2. Use AI Platform Training to execute the experiments. Write the accuracy metrics to BigQuery, and query the results using the BigQuery API.
  3. Use AI Platform Training to execute the experiments. Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
  4. Use AI Platform Notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API.

Question 60

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?

  1. Write your data in TFRecords.
  2. Z-normalize all the numeric features.
  3. Oversample the fraudulent transaction 10 times.
  4. Use one-hot encoding on all categorical features.