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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 56–60 (page: 13)

Updated On: 25-Oct-2026
Page 13 of 82
PDF with 402 Questions

You are building a MLOps platform to automate your company’s ML experiments and model retraining. You need to organize the artifacts for dozens of pipelines. How should you store the pipelines’ artifacts?

  1. Store parameters in Cloud SQL, and store the models’ source code and binaries in GitHub.
  2. Store parameters in Cloud SQL, store the models’ source code in GitHub, and store the models’ binaries in Cloud Storage.
  3. Store parameters in Vertex ML Metadata, store the models’ source code in GitHub, and store the models’ binaries in Cloud Storage.
  4. Store parameters in Vertex ML Metadata and store the models’ source code and binaries in GitHub.

Answer(s): C



You developed a custom model by using Vertex AI to predict your application's user churn rate. You are using Vertex AI Model Monitoring for skew detection. The training data stored in BigQuery contains two sets of features - demographic and behavioral. You later discover that two separate models trained on each set perform better than the original model. You need to configure a new model monitoring pipeline that splits traffic among the two models. You want to use the same prediction-sampling-rate and monitoring-frequency for each model. You also want to minimize management effort.
What should you do?

  1. Keep the training dataset as is. Deploy the models to two separate endpoints, and submit two Vertex AI Model Monitoring jobs with appropriately selected feature-thresholds parameters.
  2. Keep the training dataset as is. Deploy both models to the same endpoint and submit a Vertex AI Model Monitoring job with a monitoring-config-from-file parameter that accounts for the model IDs and feature selections.
  3. Separate the training dataset into two tables based on demographic and behavioral features. Deploy the models to two separate endpoints, and submit two Vertex AI Model Monitoring jobs.
  4. Separate the training dataset into two tables based on demographic and behavioral features. Deploy both models to the same endpoint, and submit a Vertex AI Model Monitoring job with a monitoring-config-from-file parameter that accounts for the model IDs and training datasets.

Answer(s): B



Your company manages an ecommerce platform and has a large dataset of customer reviews. Each review has a positive, negative, or neutral label. You need to quickly prototype a sentiment analysis model that accurately predicts the sentiment labels of new customer reviews while minimizing time and cost.
What should you do?

  1. Use the Agent Platform Text Embeddings API to vectorize the text, and train a regression model by using Agent Platform AutoML to predict sentiment scores.
  2. Use Agent Platform AutoML to train a multi-class classification model that predicts sentiment labels based on the training data.
  3. Use the Natural Language API for real-time sentiment analysis.
  4. Train a sentiment analysis model by using a BERT-based model, and fine-tune the model by using domain-specific customer reviews.

Answer(s): B

Explanation:

Agent Platform AutoML can train a multi-class text classification model directly from the labeled customer reviews. It minimizes custom coding, infrastructure management, and tuning effort while producing predictions for the positive, negative, and neutral sentiment classes.



You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to explore model performance using multiple model architectures, store training data, and be able to compare the evaluation metrics in the same dashboard.
What should you do?

  1. Create multiple models using AutoML Tables.
  2. Automate multiple training runs using Cloud Composer.
  3. Run multiple training jobs on AI Platform with similar job names.
  4. Create an experiment in Kubeflow Pipelines to organize multiple runs.

Answer(s): D



Your team manually performs a monthly model retraining process that is slow and error-prone. You need to streamline this workflow by building a solution that automatically checks the schema of the input data and only deploys the new model if its evaluation metrics perform better than the current production model. You want to minimize the complexity of the solution. How should you build this solution?

  1. Create a pipeline in Managed Service tor Apache Airflow. Define tasks using Apache Airflow operators that execute the training, validation, and deployment scripts. Use a BranchPythonOperator command to implement the conditional logic for comparison and deploy the better-performing model.
  2. Build a workflow in Agent Platform Pipelines. Use a Managed Service for Apache Spark serverless component to run a PySpark job that validates the input data schema. Create a custom component that compares the new model's evaluation metrics against the production model and deploys the better-performing model.
  3. Schedule a recurring job in Agent Platform Serverless Training to automatically retrain the model each month. Compare the new model's performance to the current production model in an Agent Platform Workbench notebook, and deploy the new model if the performance is better than the current production model.
  4. Build a workflow in Agent Platform Pipelines. Use a data validation component to check the input data scheme. Add a subsequent component that conditionally deploys the new model to an Agent Platform endpoint if its evaluation metrics are better than the current production model.

Answer(s): D

Explanation:

Agent Platform Pipelines provides a managed orchestration framework for automating machine learning workflows, including data validation, model training, evaluation, and deployment. A data validation component checks the input data schema to ensure consistency and quality before training. After training, a model evaluation component compares the new model's performance metrics with those of the current production model. Conditional execution ensures that the new model is deployed to the Agent Platform endpoint only when its evaluation metrics demonstrate improved performance. This approach minimizes operational complexity, reduces manual errors, and enables reliable, automated monthly retraining and deployment.



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