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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 146–150 (page: 31)

Updated On: 25-Oct-2026
Page 31 of 82
PDF with 402 Questions

You are developing ML models with AI Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code.
What should you do?

  1. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job.
  2. Use the gcloud command-line tool to submit training jobs on AI Platform when you update your code.
  3. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository.
  4. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.

Answer(s): C

Explanation:


Reference:

https://cloud.google.com/ai-platform/training/docs/training-jobs



You work at a gaming startup that has several terabytes of structured data in Cloud Storage. This data includes gameplay time data, user metadata, and game metadata. You want to build a model that recommends new games to users that requires the least amount of coding.
What should you do?

  1. Load the data in BigQuery. Use BigQuery ML to train an Autoencoder model.
  2. Load the data in BigQuery. Use BigQuery ML to train a matrix factorization model.
  3. Read the data to a Vertex AI Workbench notebook. Use TensorFlow to train a two-tower model.
  4. Read the data to a Vertex AI Workbench notebook. Use TensorFlow to train a matrix factorization model.

Answer(s): B



You are building an ML workflow that preprocesses data, trains a model with hyperparameter tuning, and deploys the best model to an Agent Platform endpoint. Your preprocessing step takes two hours and outputs a 100 GB dataset. You run experiments daily with different model architectures using the same preprocessing step. You need to automate this process and optimize the pipeline runs to minimize cost and time.
What should you do?

  1. Configure an Agent Platform Pipelines workflow with a single component for the preprocessing and training steps. Use Agent Platform hyperparameter tuning jobs within the component to identify optimal parameters.
  2. Configure an Agent Platform Pipelines workflow with separate components for the preprocessing and training steps. Enable caching on the preprocessing component, and use Agent Platform Serverless Training custom jobs for the training steps.
  3. Configure an Agent Platform Pipelines workflow with separate components for the preprocessing and training steps. Use a Cloud Run function to trigger the pipeline, and configure Agent Platform Serverless Training custom jobs for the training steps.
  4. Configure an Agent Platform Pipelines workflow that starts with an Agent Platform Serverless Training custom job for the preprocessing step. Use a Cloud Run function to launch the hyperparameter tuning job that is triggered when the preprocessed data arrives in Cloud Storage.

Answer(s): B

Explanation:

Separating preprocessing and training allows the pipeline to cache the expensive preprocessing output. Because the input data and preprocessing configuration remain unchanged across daily architecture experiments, subsequent runs can reuse the 100 GB artifact instead of repeating the two-hour step. Serverless custom training jobs then provide managed, scalable execution for each training experiment.



You are developing a classification model to support predictions for your company’s various products. The dataset you were given for model development has class imbalance. You need to minimize false positives and false negatives.
What evaluation metric should you use to properly train the model?

  1. F1 score
  2. Recall
  3. Accuracy
  4. Precision

Answer(s): A



You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company’s manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work.
What should you do?

  1. Develop a custom TensorFlow regression model, and optimize it using Vertex AI Training.
  2. Develop a regression model using BigQuery ML.
  3. Develop a custom scikit-learn regression model, and optimize it using Vertex AI Training.
  4. Develop a custom PyTorch regression model, and optimize it using Vertex AI Training.

Answer(s): B



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