Online Access Free Professional-Machine-Learning-Engineer Practice Test

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 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.
• B.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. 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.
• D.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.

You have built a custom model that performs several memory-intensive preprocessing tasks before it makes a prediction. You deployed the model to a Vertex AI endpoint, and validated that results were received in a reasonable amount of time. After routing user traffic to the endpoint, you discover that the endpoint does not autoscale as expected when receiving multiple requests.
What should you do?

• A.Increase the CPU utilization target in the autoscaling configurations.
• B.Decrease the CPU utilization target in the autoscaling configurations
• C.Decrease the number of workers per machine
• D.Use a machine type with more memory

You developed a demand forecasting model using BigQuery ML. New sales data is ingested into a BigQuery table daily. You want to implement an automated retraining strategy where the model is evaluated against the previous week's data every Sunday, and the model is retrained if the mean absolute error (MAE) exceeds 500 units. You want to use native BigQuery capabilities to automate this workflow and minimize external dependencies. What should you do?

• A.Create a Cloud Run function triggered by a Cloud Scheduler job. Configure the function to use the BigQuery client library to run the evaluation query. If the MAE threshold is reached, issue a second API call to retrain the model.
• B.Write a CREATE OR REPLACE MODEL statement with the OPTIONS clause set to warm_start = TRUE. Schedule the query to run every Sunday using a BigQuery scheduled query.
• C.Use BigQuery procedural language to write a query that runs the ML.EVALUATE statement, stores the MAE in a variable, and uses an IF statement to conditionally execute the CREATE OR REPLACE MODEL statement. Schedule the script using a BigQuery scheduled query.
• D.Create an Agent Platform Pipelines workflow containing a BigQueryJobOp component that runs the ML.EVALUATE statement. Add a conditional step to check the MAE and conditionally execute a training step. Schedule the pipeline using Agent Platform Pipelines.

You are developing a batch process that will train a custom model and perform predictions. You need to be able to show lineage for both your model and the batch predictions. What should you do?

• A.1. Create a Vertex AI managed dataset.
  2. Use a Vertex AI training pipeline to train your model.
  3. Generate batch predictions in Vertex AI.
• B.1. Use Vertex AI Experiments to evaluate model performance during training.
  2. Register your model in Vertex AI Model Registry.
  3. Generate batch predictions in Vertex AI.
• C.1. Upload your dataset to BigQuery.
  2. Use a Vertex AI custom training job to train your model.
  3. Generate predictions by using Vertex AI SDK custom prediction routines.
• D.1. Use a Vertex AI Pipelines custom training job component to train your model.
  2. Generate predictions by using a Vertex AI Pipelines model batch predict component.

You are an ML engineer at a bank. You need to build a solution that provides transparent and understandable explanations for AI-driven decisions for loan approvals, credit limits, and interest rates. You want to build this system to require minimal operational overhead. What should you do?

• A.Deploy the Learning Interpretability Tool (LIT) on App Engine to provide explainability and visualization of the output.
• B.Use AutoML Tables with built-in explainability features, and use Shapley values for explainability.
• C.Use Vertex Explainable AI to generate feature attributions, and use feature-based explanations for your models.
• D.Deploy pre-trained models from TensorFlow Hub to provide explainability using visualization tools.