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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 196–200 (page: 41)

Updated On: 25-Oct-2026
Page 41 of 82
PDF with 402 Questions

You are training a deep learning model for semantic image segmentation with reduced training time.
While using a Deep Learning VM Image, you receive the following error: The resource 'projects/deeplearning-platform/zones/europe-west4-c/acceleratorTypes/nvidia-tesla-k80' was not found.
What should you do?

  1. Ensure that you have GPU quota in the selected region.
  2. Ensure that the required GPU is available in the selected region.
  3. Ensure that you have preemptible GPU quota in the selected region.
  4. Ensure that the selected GPU has enough GPU memory for the workload.

Answer(s): B



You are a SQL analyst. You need to utilize a TensorFlow customer segmentation model stored In Cloud Storage. You want to use the simplest and most efficient approach.
What should you do?

  1. Import the model to Model Registry. Deploy the model to an Agent Platform endpoint, and use SQL for inference in BigQuery.
  2. Deploy the model by using TensorFlow Serving, and call for inference from BigQuery.
  3. Convert the model into a BigQuery ML model, and use SQL for inference.
  4. Import the model into BigQuery, and use SQL for inference.

Answer(s): A

Explanation:

Importing the TensorFlow model into the Agent Platform Model Registry and deploying it to an Agent Platform endpoint allows you to perform scalable, managed online predictions. BigQuery supports making calls to Agent Platform endpoints via SQL functions, enabling simple and efficient inference directly from SQL queries.



You work as an ML researcher at an investment bank, and you are experimenting with the Gemma large language model (LLM). You plan to deploy the model for an internal use case. You need to have full control of the mode's underlying infrastructure and minimize the model's inference time.
Which serving configuration should you use for this task?

  1. Deploy the model on a Vertex AI endpoint manually by creating a custom inference container.
  2. Deploy the model on a Google Kubernetes Engine (GKE) cluster by using the deployment options in Model Garden.
  3. Deploy the model on a Vertex AI endpoint by using one-click deployment in Model Garden.
  4. Deploy the model on a Google Kubernetes Engine (GKE) cluster manually by cresting a custom yaml manifest.

Answer(s): B

Explanation:

Deploying the Gemma LLM on a GKE cluster using Model Garden deployment options gives you full control over the underlying infrastructure while benefiting from optimized configurations. This setup minimizes inference time through fine-tuned resource allocation and allows customization of the serving environment, which is ideal for internal, performance-sensitive use cases.



You need to analyze user activity data from your company’s mobile applications. Your team will use BigQuery for data analysis, transformation, and experimentation with ML algorithms. You need to ensure real-time ingestion of the user activity data into BigQuery.
What should you do?

  1. Configure Pub/Sub to stream the data into BigQuery.
  2. Run an Apache Spark streaming job on Dataproc to ingest the data into BigQuery.
  3. Run a Dataflow streaming job to ingest the data into BigQuery.
  4. Configure Pub/Sub and a Dataflow streaming job to ingest the data into BigQuery.

Answer(s): A



You are working on a classification problem with time series data. After conducting just a few experiments using random cross-validation, you achieved an Area Under the Receiver Operating Characteristic Curve (AUC ROC) value of 99% on the training data. You haven’t explored using any sophisticated algorithms or spent any time on hyperparameter tuning.
What should your next step be to identify and fix the problem?

  1. Address the model overfitting by using a less complex algorithm and use k-fold cross-validation.
  2. Address data leakage by applying nested cross-validation during model training.
  3. Address data leakage by removing features highly correlated with the target value.
  4. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.

Answer(s): B



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