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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 111–115 (page: 24)

Updated On: 25-Oct-2026
Page 24 of 82
PDF with 402 Questions

You recently developed a wide and deep model in TensorFlow. You generated training datasets using a SQL script that preprocessed raw data in BigQuery by performing instance-level transformations of the data. You need to create a training pipeline to retrain the model on a weekly basis. The trained model will be used to generate daily recommendations. You want to minimize model development and training time. How should you develop the training pipeline?

  1. Use the Kubeflow Pipelines SDK to implement the pipeline. Use the BigQueryJobOp component to run the preprocessing script and the CustomTrainingJobOp component to launch a Vertex AI training job.
  2. Use the Kubeflow Pipelines SDK to implement the pipeline. Use the DataflowPythonJobOp component to preprocess the data and the CustomTrainingJobOp component to launch a Vertex AI training job.
  3. Use the TensorFlow Extended SDK to implement the pipeline Use the ExampleGen component with the BigQuery executor to ingest the data the Transform component to preprocess the data, and the Trainer component to launch a Vertex AI training job.
  4. Use the TensorFlow Extended SDK to implement the pipeline Implement the preprocessing steps as part of the input_fn of the model. Use the ExampleGen component with the BigQuery executor to ingest the data and the Trainer component to launch a Vertex AI training job.

Answer(s): C



Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model’s code, serving, and deployment. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?

  1. Use the Natural Language API to classify support requests.
  2. Use AutoML Natural Language to build the support requests classifier.
  3. Use an established text classification model on Vertex AI to perform transfer learning.
  4. Use an established text classification model on Vertex AI as-is to classify support requests.

Answer(s): C



You are an ML engineer at a bank. You have developed a binary classification model using AutoML Tables to predict whether a customer will make loan payments on time. The output is used to approve or reject loan requests. One customer’s loan request has been rejected by your model, and the bank’s risks department is asking you to provide the reasons that contributed to the model’s decision.
What should you do?

  1. Use local feature importance from the predictions.
  2. Use the correlation with target values in the data summary page.
  3. Use the feature importance percentages in the model evaluation page.
  4. Vary features independently to identify the threshold per feature that changes the classification.

Answer(s): A



You work for an online publisher that delivers news articles to over 50 million readers. You have built an AI model that recommends content for the company’s weekly newsletter. A recommendation is considered successful if the article is opened within two days of the newsletter’s published date and the user remains on the page for at least one minute.
All the information needed to compute the success metric is available in BigQuery and is updated hourly. The model is trained on eight weeks of data, on average its performance degrades below the acceptable baseline after five weeks, and training time is 12 hours. You want to ensure that the model’s performance is above the acceptable baseline while minimizing cost. How should you monitor the model to determine when retraining is necessary?

  1. Use Vertex AI Model Monitoring to detect skew of the input features with a sample rate of 100% and a monitoring frequency of two days.
  2. Schedule a cron job in Cloud Tasks to retrain the model every week before the newsletter is created.
  3. Schedule a weekly query in BigQuery to compute the success metric.
  4. Schedule a daily Dataflow job in Cloud Composer to compute the success metric.

Answer(s): C



You are an ML engineer at a large retail company. You have a BigQuery table containing five years of daily sales data for 50,000 distinct product SKUs. You need to generate a daily demand forecast for the next 30 days for each individual SKU. The solution must automatically account for holidays and weekly seasonality. You want to minimize data egress costs and operational complexity by keeping the data and model within the data warehouse.
What should you do?

  1. Use the CREATE MODEL... OPTIONS(model_type='ARIMA_PLUS') statement in BigQuery to generate a forecast based on historical SKU sales.
  2. Use the gcloud ai custom-jobs create command to run a PyTorch LSTM container on Agent Platform that pulls data from a CSV into Cloud Storage.
  3. Use the CREATE MODEL... OPTIONS(model_type='LINEAR_REG') statement in BigQuery to predict sales using the transaction date as a feature.
  4. Use the google-cloud-aiplatform SDK to create a TimeSeriesDataset and trigger an AutoMLForecastingTrainingJob.

Answer(s): A

Explanation:

BigQuery ML ARIMA_PLUS trains and forecasts directly within BigQuery, avoiding data movement and separate serving infrastructure. By specifying the SKU as the time-series identifier, one model can generate independent 30-day forecasts for all 50,000 products while automatically detecting seasonal patterns and modeling holiday effects.


Reference:

https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-time-series https://docs.cloud.google.com/bigquery/docs/arima-multiple-time-series-forecasting-tutorial https://docs.cloud.google.com/bigquery/docs/time-series-forecasting-holidays-tutorial



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