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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 116–120 (page: 25)

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

You work for a retail company that is using a regression model built with BigQuery ML to predict product sales. This model is being used to serve online predictions. Recently you developed a new version of the model that uses a different architecture (custom model). Initial analysis revealed that both models are performing as expected. You want to deploy the new version of the model to production and monitor the performance over the next two months. You need to minimize the impact to the existing and future model users. How should you deploy the model?

  1. Import the new model to the same Vertex AI Model Registry as a different version of the existing model. Deploy the new model to the same Vertex AI endpoint as the existing model, and use traffic splitting to route 95% of production traffic to the BigQuery ML model and 5% of production traffic to the new model.
  2. Import the new model to the same Vertex AI Model Registry as the existing model. Deploy the models to one Vertex AI endpoint. Route 95% of production traffic to the BigQuery ML model and 5% of production traffic to the new model.
  3. Import the new model to the same Vertex AI Model Registry as the existing model. Deploy each model to a separate Vertex AI endpoint.
  4. Deploy the new model to a separate Vertex AI endpoint. Create a Cloud Run service that routes the prediction requests to the corresponding endpoints based on the input feature values.

Answer(s): A



During batch training of a neural network, you notice that there is an oscillation in the loss. How should you adjust your model to ensure that it converges?

  1. Decrease the size of the training batch.
  2. Decrease the learning rate hyperparameter.
  3. Increase the learning rate hyperparameter.
  4. Increase the size of the training batch.

Answer(s): B



You are creating an ML pipeline for data processing, model training, and model deployment that uses different Google Cloud services. You have developed code for each individual task, and you expect a high frequency of new files. You now need to create an orchestration layer on top of these tasks. You only want this orchestration pipeline to run if new files are present in your dataset in a Cloud Storage bucket. You also want to minimize the compute node costs.
What should you do?

  1. Create a pipeline in Vertex AI Pipelines. Configure the first step to compare the contents of the bucket to the last time the pipeline was run. Use the scheduler API to run the pipeline periodically.
  2. Create a Cloud Function that uses a Cloud Storage trigger and deploys a Cloud Composer directed acyclic graph (DAG).
  3. Create a pipeline in Vertex AI Pipelines. Create a Cloud Function that uses a Cloud Storage trigger and deploys the pipeline.
  4. Deploy a Cloud Composer directed acyclic graph (DAG) with a GCSObjectUpdateSensor class that detects when a new file is added to the Cloud Storage bucket.

Answer(s): C



You are an ML engineer at a global car manufacture. You need to build an ML model to predict car sales in different cities around the world.
Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

  1. Three individual features: binned latitude, binned longitude, and one-hot encoded car type.
  2. One feature obtained as an element-wise product between latitude, longitude, and car type.
  3. One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type.
  4. Two feature crosses as an element-wise product: the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type.

Answer(s): C



You work for a company that builds bridges for cities around the world. To track the progress of projects at construction sites, your company has set up cameras at each location. Each hour, the cameras take a picture that is sent to a Cloud Storage bucket. A team of specialists reviews the images, filters important ones, and then annotates unique specialized construction equipment in the images. You want to propose an ML solution that will help the company scale and reduce costs for this process. You need the solution to have minimal up-front cost.
What method should you propose?

  1. Train an AutoML object detection model to annotate the objects in the images to help specialists with the annotation task.
  2. Use the Cloud Vision API to automatically annotate objects in the images to help specialists with the annotation task.
  3. Create a BigQuery ML classification model to classify important images. Use the model to predict which new images are important to help specialists with the filtering task.
  4. Use Vertex AI to train an open source object detection to annotate the objects in the images to help specialists with the annotation task.

Answer(s): A



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