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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 131–135 (page: 28)

Updated On: 25-Oct-2026
Page 28 of 82
PDF with 402 Questions

You trained a model, packaged it with a custom Docker container for serving, and deployed it to Vertex AI Model Registry.
When you submit a batch prediction job, it fails with this error: "Error: model server never became ready. Please validate that your model file or container configuration are valid. " There are no additional errors in the logs.
What should you do?

  1. Add a logging configuration to your application to emit logs to Cloud Logging.
  2. Change the HTTP port in your model’s configuration to the default value of 8080.
  3. Change the healthRoute value in your model’s configuration to /healthcheck.
  4. Pull the Docker image locally, and use the docker run command to launch it locally. Use the docker logs command to explore the error logs.

Answer(s): D



You work for a media company that offers a news service. News articles are currently manually assigned multiple tags based on their content. You need to create a scalable solution that accurately and automatically tags articles. You have a historical database of 2 million articles with 120 distinct tags in a Cloud Storage bucket. Your team has experience with LLMs but limited programming skills.
What should you do?

  1. Import data to BigQuery, and apply the Gemini LLM API to the database. Insert multiple article-tagging pairs in the prompt context.
  2. Import data to BigQuery, and fine-tune the Gemini model from Model Garden using the CREATE MODEL command. Use the fine-tuned model for tagging.
  3. Use the Natural Language API to perform entity recognition directly on the Cloud Storage database. Use the extracted entities as tags.
  4. Fine-tune the Gemini model from Model Garden using the google.genai.create_tuned_model method in an Agent Platform Workbench notebook. Use the fine-tuned model for tagging directly on the Cloud Storage database.

Answer(s): B

Explanation:

BigQuery ML provides a scalable, SQL-based approach to fine-tuning Gemini models for automated article tagging with minimal programming effort. By importing the 2 million historical articles and their associated tags into BigQuery, the team can use the CREATE MODEL statement to fine-tune a supported Gemini model through supervised learning. The fine-tuned model learns the relationships between article content and the 120 predefined tags, enabling accurate multi-label classification. BigQuery's integration with Gemini also allows the team to generate tags for new articles using SQL-based inference, reducing manual tagging effort while supporting large-scale processing


Reference:

https://docs.cloud.google.com/bigquery/docs/tune-evaluate https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-remote-model-t uned



You work for an ecommerce company that wants to automatically classify products in images to improve user experience. You have a substantial dataset of labeled images depicting various unique products. You need to implement a solution for identifying custom products that is scalable, effective, and can be rapidly deployed.
What should you do?

  1. Develop a rule-based system to categorize the images.
  2. Use a TensorFlow deep learning model that is trained on the image dataset.
  3. Use a pre-trained object detection model from Model Garden.
  4. Use AutoML Vision to train a model using the image dataset.

Answer(s): D

Explanation:

AutoML Vision is an ideal choice for this use case because it provides a scalable and easy-to-use platform for training custom machine learning models on your labeled image dataset. It eliminates the need for deep technical expertise in building and fine-tuning models while leveraging advanced techniques to achieve high accuracy. With AutoML Vision, you can train a custom model specifically tailored to classify your unique products, ensuring better performance compared to a generic pre-trained model.



You are responsible for managing and monitoring a Vertex AI model that is deployed in production. You want to automatically retrain the model when its performance deteriorates.
What should you do?

  1. Create a Vertex AI Model Monitoring job to track the model's performance with production data, and trigger retraining when specific metrics drop below predefined thresholds.
  2. Collect feedback from end users, and retrain the model based on their assessment of its performance.
  3. Configure a scheduled job to evaluate the model's performance on a static dataset, and retrain the model if the performance drops below predefined thresholds.
  4. Use Vertex Explainable AI to analyze feature attributions and identify potential biases in the model. Retrain when significant shifts in feature importance or biases are detected.

Answer(s): A

Explanation:

Vertex AI Model Monitoring automatically tracks model performance on production data and can be configured to trigger retraining workflows when key metrics fall below defined thresholds, enabling proactive, automated model maintenance.



You are building an ML pipeline to process and analyze both streaming and batch datasets. You need the pipeline to handle data validation, preprocessing, model training, and model deployment in a consistent and automated way. You want to design an efficient and scalable solution that captures model training metadata and is easily reproducible. You want to be able to reuse custom components for different parts of your pipeline.
What should you do?

  1. Use Cloud Composer for distributed processing of batch and streaming data in the pipeline.
  2. Use Dataflow for distributed processing of batch and streaming data in the pipeline.
  3. Use Cloud Build to build and push Docker images for each pipeline component.
  4. Implement an orchestration framework such as Kubeflow Pipelines or Vertex AI Pipelines.

Answer(s): D

Explanation:

Vertex AI Pipelines (or Kubeflow Pipelines) are designed for orchestrating end-to-end ML workflows. They support consistent execution of validation, preprocessing, training, and deployment steps, capture metadata automatically, allow for easy reproducibility, and promote reuse of custom components. This makes them the most efficient and scalable solution for your use case.



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