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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 21–25 (page: 6)

Updated On: 25-Oct-2026
Page 6 of 82
PDF with 402 Questions

You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories.
What should you do?

  1. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run.
  2. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
  3. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.
  4. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic.

Answer(s): B



You need to execute a batch prediction on 100 million records in a BigQuery table with a custom TensorFlow DNN regressor model, and then store the predicted results in a BigQuery table. You want to minimize the effort required to build this inference pipeline.
What should you do?

  1. Import the TensorFlow model with BigQuery ML, and run the ml.predict function.
  2. Use the TensorFlow BigQuery reader to load the data, and use the BigQuery API to write the results to BigQuery.
  3. Create a Dataflow pipeline to convert the data in BigQuery to TFRecords. Run a batch inference on Vertex AI Prediction, and write the results to BigQuery.
  4. Load the TensorFlow SavedModel in a Dataflow pipeline. Use the BigQuery I/O connector with a custom function to perform the inference within the pipeline, and write the results to BigQuery.

Answer(s): A



You work for an advertising company and want to understand the effectiveness of your company’s latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table, and then manipulate the results of that query with a pandas dataframe in an AI Platform notebook.
What should you do?

  1. Use AI Platform Notebooks’ BigQuery cell magic to query the data, and ingest the results as a pandas dataframe.
  2. Export your table as a CSV file from BigQuery to Google Drive, and use the Google Drive API to ingest the file into your notebook instance.
  3. Download your table from BigQuery as a local CSV file, and upload it to your AI Platform notebook instance. Use pandas.read_csv to ingest he file as a pandas dataframe.
  4. From a bash cell in your AI Platform notebook, use the bq extract command to export the table as a CSV file to Cloud Storage, and then use gsutil cp to copy the data into the notebook. Use pandas.read_csv to ingest the file as a pandas dataframe.

Answer(s): A



You work for a toy manufacturer that has been experiencing a large increase in demand. You need to build an ML model to reduce the amount of time spent by quality control inspectors checking for product defects. Faster defect detection is a priority. The factory does not have reliable Wi-Fi. Your company wants to implement the new ML model as soon as possible.
Which model should you use?

  1. AutoML Vision Edge mobile-high-accuracy-1 model
  2. AutoML Vision Edge mobile-low-latency-1 model
  3. AutoML Vision model
  4. AutoML Vision Edge mobile-versatile-1 model

Answer(s): B



You are building a predictive maintenance model to preemptively detect part defects in bridges. You plan to use high definition images of the bridges as model inputs. You need to explain the output of the model to the relevant stakeholders so they can take appropriate action. How should you build the model?

  1. Use scikit-learn to build a tree-based model, and use SHAP values to explain the model output.
  2. Use scikit-learn to build a tree-based model, and use partial dependence plots (PDP) to explain the model output.
  3. Use TensorFlow to create a deep learning-based model, and use Integrated Gradients to explain the model output.
  4. Use TensorFlow to create a deep learning-based model, and use the sampled Shapley method to explain the model output.

Answer(s): C



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