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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Monitoring ML solutions | - Model retraining strategies - Performance monitoring and drift detection - Logging and alerting (Cloud Monitoring) |
| Scaling prototypes into ML models | - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Training at scale (Distributed training, TPUs) - Hyperparameter tuning |
| Architecting low-code ML solutions | - Implementing BigQuery ML for basic models - AutoML capabilities and implementation - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) |
| Collaborating within and across teams to manage data and models | - Data management and governance - Version control and reproducibility (e.g., DVC, MLOps) - Collaboration between Data Scientists, Data Engineers, and ML Engineers |
| Serving and scaling models | - Batch prediction - Model optimization (Quantization, Distillation) - Online prediction (Vertex AI Prediction) - Hardware accelerators (GPU/TPU) in serving |
| Automating and orchestrating ML pipelines | - Triggering and scheduling pipelines - Vertex AI Pipelines (Kubeflow Pipelines) - CI/CD for ML systems |
Your Professional-Machine-Learning-Engineer Questions, Answered by ExamTorrent
- Automating and orchestrating ML pipelines ()
- Serving and scaling models ()
- Architecting low-code ML solutions ()
Google Professional Machine Learning Engineer Sample Questions:
You work for a hospital that wants to optimize how it schedules operations. You need to create a model that uses the relationship between the number of surgeries scheduled and beds used. You want to predict how many beds will be needed for patients each day in advance based on the scheduled surgeries. You have one year of data for the hospital organized in 365 rows.
The data includes the following variables for each day:
- Number of scheduled surgeries
- Number of beds occupied
- Date
You want to maximize the speed of model development and testing. What should you do?
- A. Create a Vertex AI tabular dataset. Train an AutoML regression model, with number of beds as the target variable, and number of scheduled minor surgeries and date features (such as day of the week) as the predictors.
- B. Create a Vertex AI tabular dataset. Train a Vertex AI AutoML Forecasting model, with number of beds as the target variable, number of scheduled surgeries as a covariate and date as the time variable.
- C. Create a BigQuery table. Use BigQuery ML to build a regression model, with number of beds as the target variable, and number of scheduled surgeries and date features (such as day of week) as the predictors.
- D. Create a BigQuery table. Use BigQuery ML to build an ARIMA model, with number of beds as the target variable, and date as the time variable.
Correct Answer: B 🗳️
You built a binary classifier that detects fraudulent insurance claims. Only 0.4% of claims in the dataset are fraudulent. Your model reports 99.6% accuracy, but the fraud investigation team reports that almost no fraudulent claims are being flagged. Investigators have limited capacity, so every case they review must be worth their time. You need to select an evaluation approach that reflects business value. What should you do?
- A. Continue optimizing for accuracy, but increase the classification threshold to 0.9.
- B. Rebalance the test set by downsampling the majority class until classes are equal, and then report accuracy.
- C. Evaluate the model using log loss on the full imbalanced test set.
- D. Evaluate the model using the area under the precision-recall curve, and select an operating threshold based on the investigators' review capacity.
Correct Answer: D 🗳️
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You are developing a model to predict whether a failure will occur in a critical machine part. You have a dataset consisting of a multivariate time series and labels indicating whether the machine part failed. You recently started experimenting with a few different preprocessing and modeling approaches in a Vertex AI Workbench notebook. You want to log data and track artifacts from each run. How should you set up your experiments?
- A. 1. Use the Vertex AI SDK to create an experiment and set up Vertex ML Metadata.
2. Use the log_time_series_metrics function to track the preprocessed data, and use the log_merrics function to log loss values. - B. 1. Use the Vertex AI SDK to create an experiment and set up Vertex ML Metadata.
2. Use the log_time_series_metrics function to track the preprocessed data, and use the log_metrics function to log loss values. - C. 1. Create a Vertex AI TensorBoard instance, and use the Vertex AI SDK to create an experiment and associate the TensorBoard instance.
2. Use the log_time_series_metrics function to track the preprocessed data, and use the log_metrics function to log loss values. - D. 1. Create a Vertex AI TensorBoard instance and use the Vertex AI SDK to create an experiment and associate the TensorBoard instance.
2. Use the assign_input_artifact method to track the preprocessed data and use the log_time_series_metrics function to log loss values.
Correct Answer: D 🗳️
You are training an ML model on a large dataset. You are using a TPU to accelerate the training process. You notice that the training process is taking longer than expected. You discover that the TPU is not reaching its full capacity. What should you do?
- A. Increase the number of epochs
- B. Increase the learning rate
- C. Increase the batch size
- D. Decrease the learning rate
Correct Answer: C 🗳️
You work at an ecommerce startup. You need to create a customer churn prediction model. Your company's recent sales records are stored in a BigQuery table. You want to understand how your initial model is making predictions. You also want to iterate on the model as quickly as possible while minimizing cost. How should you build your first model?
- A. Export the data to a Cloud Storage bucket. Load the data into a pandas DataFrame on Vertex AI Workbench and train a logistic regression model with scikit-learn.
- B. Export the data to a Cloud Storage bucket. Create a tf.data.Dataset to read the data from Cloud Storage. Implement a deep neural network in TensorFlow.
- C. Prepare the data in BigQuery and associate the data with a Vertex AI dataset. Create an AutoMLTabularTrainingJob to tram a classification model.
- D. Create a tf.data.Dataset by using the TensorFlow BigQueryClient. Implement a deep neural network in TensorFlow.
Correct Answer: C 🗳️
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