Google PROFESSIONAL-MACHINE-LEARNING-ENGINEER Online Practice Questions and Exam Preparation

Google PROFESSIONAL-MACHINE-LEARNING-ENGINEER Online Questions & Answers

• Question 1:

  You are experimenting with a built-in distributed XGBoost model in Vertex AI Workbench user-managed notebooks. You use BigQuery to split your data into training and validation sets using the following queries: CREATE OR REPLACE TABLE `myproject.mydataset.training` AS (SELECT * FROM `myproject.mydataset.mytable` WHERE RAND() <= 0.8);

  CREATE OR REPLACE TABLE `myproject.mydataset.validation` AS (SELECT * FROM `myproject.mydataset.mytable` WHERE RAND() <= 0.2);

  After training the model, you achieve an area under the receiver operating characteristic curve (AUC ROC) value of 0.8, but after deploying the model to production, you notice that your model performance has dropped to an AUC ROC value of 0.65. What problem is most likely occurring?

  A. There is training-serving skew in your production environment.
  B. There is not a sufficient amount of training data.
  C. The tables that you created to hold your training and validation records share some records, and you may not be using all the data in your initial table.
  D. The RAND() function generated a number that is less than 0.2 in both instances, so every record in the validation table will also be in the training table.
  C. The tables that you created to hold your training and validation records share some records, and you may not be using all the data in your initial table.

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  Explanation/Reference:

  https://developers.google.com/machine-learning/guides/rules-of-ml#training-serving_skew

• Question 2:

  You created an ML pipeline with multiple input parameters. You want to investigate the tradeoffs between different parameter combinations. The parameter options are:

  1.

  Input dataset

  2.

  Max tree depth of the boosted tree regressor

  3.

  Optimizer learning rate

  You need to compare the pipeline performance of the different parameter combinations measured in F1 score, time to train, and model complexity. You want your approach to be reproducible, and track all pipeline runs on the same platform. What should you do?

  A. 1. Use BigQueryML to create a boosted tree regressor, and use the hyperparameter tuning capability. 2. Configure the hyperparameter syntax to select different input datasets: max tree depths, and optimizer learning rates. Choose the grid search option.
  B. 1. Create a Vertex AI pipeline with a custom model training job as part of the pipeline. Configure the pipeline's parameters to include those you are investigating. 2. In the custom training step, use the Bayesian optimization method with F1 score as the target to maximize.
  C. 1. Create a Vertex AI Workbench notebook for each of the different input datasets. 2. In each notebook, run different local training jobs with different combinations of the max tree depth and optimizer learning rate parameters. 3. After each notebook finishes, append the results to a BigQuery table.
  D. 1. Create an experiment in Vertex AI Experiments. 2. Create a Vertex AI pipeline with a custom model training job as part of the pipeline. Configure the pipeline's parameters to include those you are investigating. 3. Submit multiple runs to the same experiment, using different values for the parameters.
  D. 1. Create an experiment in Vertex AI Experiments. 2. Create a Vertex AI pipeline with a custom model training job as part of the pipeline. Configure the pipeline's parameters to include those you are investigating. 3. Submit multiple runs to the same experiment, using different values for the parameters.

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  Explanation/Reference:

• Question 3:

  You have trained an XGBoost model that you plan to deploy on Vertex AI for online prediction. You are now uploading your model to Vertex AI Model Registry, and you need to configure the explanation method that will serve online prediction requests to be returned with minimal latency. You also want to be alerted when feature attributions of the model meaningfully change over time. What should you do?

  A. 1. Specify sampled Shapley as the explanation method with a path count of 5. 2. Deploy the model to Vertex AI Endpoints. 3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.
  B. 1. Specify Integrated Gradients as the explanation method with a path count of 5. 2. Deploy the model to Vertex AI Endpoints. 3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.
  C. 1. Specify sampled Shapley as the explanation method with a path count of 50. 2. Deploy the model to Vertex AI Endpoints. 3. Create a Model Monitoring job that uses training-serving skew as the monitoring objective.
  D. 1. Specify Integrated Gradients as the explanation method with a path count of 50. 2. Deploy the model to Vertex AI Endpoints. 3. Create a Model Monitoring job that uses training-serving skew as the monitoring objective.
  A. 1. Specify sampled Shapley as the explanation method with a path count of 5. 2. Deploy the model to Vertex AI Endpoints. 3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.

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  Explanation/Reference:

• Question 4:

  You want to train an AutoML model to predict house prices by using a small public dataset stored in BigQuery. You need to prepare the data and want to use the simplest, most efficient approach. What should you do?

  A. Write a query that preprocesses the data by using BigQuery and creates a new table. Create a Vertex AI managed dataset with the new table as the data source.
  B. Use Dataflow to preprocess the data. Write the output in TFRecord format to a Cloud Storage bucket.
  C. Write a query that preprocesses the data by using BigQuery. Export the query results as CSV files, and use those files to create a Vertex AI managed dataset.
  D. Use a Vertex AI Workbench notebook instance to preprocess the data by using the pandas library. Export the data as CSV files, and use those files to create a Vertex AI managed dataset.
  A. Write a query that preprocesses the data by using BigQuery and creates a new table. Create a Vertex AI managed dataset with the new table as the data source.

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  Explanation/Reference:

• Question 5:

  You are an ML engineer at a mobile gaming company. A data scientist on your team recently trained a TensorFlow model, and you are responsible for deploying this model into a mobile application. You discover that the inference latency of the current model doesn't meet production requirements. You need to reduce the inference time by 50%, and you are willing to accept a small decrease in model accuracy in order to reach the latency requirement. Without training a new model, which model optimization technique for reducing latency should you try first?

  A. Weight pruning
  B. Dynamic range quantization
  C. Model distillation
  D. Dimensionality reduction
  B. Dynamic range quantization

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  Explanation/Reference:

  https://www.tensorflow.org/lite/performance/post_training_quantization#dynamic_range_quantization

• Question 6:

  You work on the data science team at a manufacturing company. You are reviewing the company's historical sales data, which has hundreds of millions of records. For your exploratory data analysis, you need to calculate descriptive statistics such as mean, median, and mode; conduct complex statistical tests for hypothesis testing; and plot variations of the features over time. You want to use as much of the sales data as possible in your analyses while minimizing computational resources. What should you do?

  A. Visualize the time plots in Google Data Studio. Import the dataset into Vertex Al Workbench user-managed notebooks. Use this data to calculate the descriptive statistics and run the statistical analyses.
  B. Spin up a Vertex Al Workbench user-managed notebooks instance and import the dataset. Use this data to create statistical and visual analyses.
  C. Use BigQuery to calculate the descriptive statistics. Use Vertex Al Workbench user-managed notebooks to visualize the time plots and run the statistical analyses.
  D. Use BigQuery to calculate the descriptive statistics, and use Google Data Studio to visualize the time plots. Use Vertex Al Workbench user-managed notebooks to run the statistical analyses.
  C. Use BigQuery to calculate the descriptive statistics. Use Vertex Al Workbench user-managed notebooks to visualize the time plots and run the statistical analyses.

• Question 7:

  You developed a custom model by using Vertex AI to forecast the sales of your company's products based on historical transactional data. You anticipate changes in the feature distributions and the correlations between the features in the near future. You also expect to receive a large volume of prediction requests. You plan to use Vertex AI Model Monitoring for drift detection and you want to minimize the cost. What should you do?

  A. Use the features for monitoring. Set a monitoring-frequency value that is higher than the default.
  B. Use the features for monitoring. Set a prediction-sampling-rate value that is closer to 1 than 0.
  C. Use the features and the feature attributions for monitoring. Set a monitoring-frequency value that is lower than the default.
  D. Use the features and the feature attributions for monitoring. Set a prediction-sampling-rate value that is closer to 0 than 1.
  D. Use the features and the feature attributions for monitoring. Set a prediction-sampling-rate value that is closer to 0 than 1.

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  Explanation/Reference:

• Question 8:

  You work for a magazine publisher and have been tasked with predicting whether customers will cancel their annual subscription. In your exploratory data analysis, you find that 90% of individuals renew their subscription every year, and only 10% of individuals cancel their subscription. After training a NN Classifier, your model predicts those who cancel their subscription with 99% accuracy and predicts those who renew their subscription with 82% accuracy. How should you interpret these results?

  A. This is not a good result because the model should have a higher accuracy for those who renew their subscription than for those who cancel their subscription.
  B. This is not a good result because the model is performing worse than predicting that people will always renew their subscription.
  C. This is a good result because predicting those who cancel their subscription is more difficult, since there is less data for this group.
  D. This is a good result because the accuracy across both groups is greater than 80%.
  C. This is a good result because predicting those who cancel their subscription is more difficult, since there is less data for this group.

• Question 9:

  Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?

  A. 1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station. 2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction.
  B. 1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station. 2. Dispatch an available shuttle and provide the map with the required stops based on the prediction.
  C. 1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints. 2. Dispatch an appropriately sized shuttle and indicate the required stops on the map.
  D. 1. Build a reinforcement learning model with tree-based classification models that predict the presence of passengers at shuttle stops as agents and a reward function around a distance-based metric. 2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.
  C. 1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints. 2. Dispatch an appropriately sized shuttle and indicate the required stops on the map.

• Question 10:

  You recently deployed an ML model. Three months after deployment, you notice that your model is underperforming on certain subgroups, thus potentially leading to biased results. You suspect that the inequitable performance is due to class imbalances in the training data, but you cannot collect more data. What should you do? (Choose two.)

  A. Remove training examples of high-performing subgroups, and retrain the model.
  B. Add an additional objective to penalize the model more for errors made on the minority class, and retrain the model
  C. Remove the features that have the highest correlations with the majority class.
  D. Upsample or reweight your existing training data, and retrain the model
  E. Redeploy the model, and provide a label explaining the model's behavior to users.
  B. Add an additional objective to penalize the model more for errors made on the minority class, and retrain the model
  D. Upsample or reweight your existing training data, and retrain the model

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  Explanation/Reference:

  https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/