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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 121–125 (page: 26)

Updated On: 25-Oct-2026
Page 26 of 82
PDF with 402 Questions

You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your model’s features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis.
Which algorithms should you use to build the model?

  1. Classification
  2. Reinforcement Learning
  3. Recurrent Neural Networks (RNN)
  4. Convolutional Neural Networks (CNN)

Answer(s): C



You are developing a natural language processing model that analyzes customer feedback to identify positive, negative, and neutral experiences. During the testing phase, you notice that the model demonstrates a significant bias against certain demographic groups, leading to skewed analysis results. You want to address this issue following Google's responsible AI practices.
What should you do?

  1. Use Agent Platform’s model evaluation service to assess bias in the model’s predictions, and use post-processing to adjust outputs for identified demographic discrepancies.
  2. Implement a more complex model architecture that can capture nuanced patterns in language to reduce bias.
  3. Audit the training dataset to identify underrepresented groups and augment the dataset with additional samples before retraining the model.
  4. Use explainable AI tooling to generate explanations and systematically adjust the predictions to address identified biases.

Answer(s): C

Explanation:

Auditing and augmenting the training dataset to improve representation of underrepresented groups directly addresses the root cause of model bias. This aligns with Google's responsible AI practices, which emphasize fairness by improving data quality and representation before relying on post-processing or model adjustments.



You are using Vertex AI and TensorFlow to develop a custom image classification model. You need the model’s decisions and the rationale to be understandable to your company’s stakeholders. You also want to explore the results to identify any issues or potential biases.
What should you do?

  1. 1. Use TensorFlow to generate and visualize features and statistics.
    2. Analyze the results together with the standard model evaluation metrics.
  2. 1. Use TensorFlow Profiler to visualize the model execution.
    2. Analyze the relationship between incorrect predictions and execution bottlenecks.
  3. 1. Use Vertex Explainable AI to generate example-based explanations.
    2. Visualize the results of sample inputs from the entire dataset together with the standard model evaluation metrics.
  4. 1. Use Vertex Explainable AI to generate feature attributions. Aggregate feature attributions over the entire dataset.
    2. Analyze the aggregation result together with the standard model evaluation metrics.

Answer(s): D



You manage a freight-demand forecasting model at a logistics company. The training workflow is orchestrated using custom components in Agent Platform Pipelines. Currently, a Cloud Scheduler job triggers a Cloud Run function that submits a pipeline run using a compiled JSON definition stored in Cloud Storage.
A data scientist has updated the training component's Python logic, which requires a new dependency to be installed. The data scientist merged these updates, including the Kubeflow Pipelines (KFP) component definitions and the underlying training script, into the main branch of your repository. You need to configure a Cloud Build trigger to automate the CI/CD transition. How should you implement this process to ensure that the next scheduled run uses the updated logic?

  1. 1. Build and push new Docker images for the modified components to Artifact Registry.
    2. Use Cloud Build to create a new Model Registry entry for the updated code version.
    3. Update the automatic trigger target URI to point to the latest Model Registry resource ID.
  2. 1. Build and push a new Docker container image for training to Artifact Registry.
    2. Use the KFP SDK to compile the pipeline into a JSON file.
    3. Upload the compiled pipeline definition to the designated Cloud Storage bucket location used by the automated trigger.
  3. 1. Build and push a new Docker container image for training to Artifact Registry.
    2. Use Cloud Build to submit an Agent Platform Pipelines custom job for training.
    3. Create a new Model Registry entry for the updated code version.
  4. 1. Use the KFP SDK to compile the pipeline into a JSON file.
    2. Upload this file to the Cloud Storage URI monitored by the Cloud Run function.
    3. Update the Cloud Run function environment variables to reference the new version I

Answer(s): B

Explanation:

Cloud Build can automate the CI/CD process by building a new Docker container image containing the updated training logic and required dependencies, then pushing it to Artifact Registry. The Kubeflow Pipelines (KFP) SDK is used to compile the updated pipeline components into a JSON pipeline definition that references the new container image. Uploading this compiled JSON file to the designated Cloud Storage location ensures that the existing Cloud Scheduler and Cloud Run function workflow retrieves the updated pipeline definition during the next scheduled execution. This approach automates deployment of code and dependency changes without modifying the existing scheduling infrastructure.



You have developed a fraud detection model for a large financial institution using Vertex AI. The model achieves high accuracy, but the stakeholders are concerned about the model's potential for bias based on customer demographics. You have been asked to provide insights into the model's decision-making process and identify any fairness issues.
What should you do?

  1. Create feature groups using Vertex AI Feature Store to segregate customer demographic features and non-demographic features. Retrain the model using only non-demographic features.
  2. Use feature attribution in Vertex AI to analyze model predictions and the impact of each feature on the model's predictions.
  3. Enable Vertex AI Model Monitoring to detect training-serving skew. Configure an alert to send an email when the skew or drift for a modes feature exceeds a predefined threshold. Re-train the model by appending new data to existing raining data.
  4. Compile a dataset of unfair predictions. Use Vertex AI Vector Search to identify similar data points in the model's predictions. Report these data points to the stakeholders.

Answer(s): B

Explanation:

Feature attribution in Vertex AI (using tools like SHAP or integrated gradients) provides transparency into which features most influence the model’s predictions. This helps identify whether demographic features are contributing disproportionately, which is essential for detecting and addressing fairness concerns. It aligns with stakeholder needs for explainability and bias detection.



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