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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 201–205 (page: 42)

Updated On: 25-Oct-2026
Page 42 of 82
PDF with 402 Questions

You work on the data science team for a multinational beverage company. You need to develop an ML model to predict the company’s profitability for a new line of naturally flavored bottled waters in different locations. You are provided with historical data that includes product types, product sales volumes, expenses, and profits for all regions.
What should you use as the input and output for your model?

  1. Use latitude, longitude, and product type as features. Use profit as model output.
  2. Use latitude, longitude, and product type as features. Use revenue and expenses as model outputs.
  3. Use product type and the feature cross of latitude with longitude, followed by binning, as features. Use profit as model output.
  4. Use product type and the feature cross of latitude with longitude, followed by binning, as features. Use revenue and expenses as model outputs.

Answer(s): C



You work for a magazine distributor and need to build a model that predicts which customers will renew their subscriptions for the upcoming year. Using your company’s historical data as your training set, you created a TensorFlow model and deployed it to Vertex AI. You need to determine which customer attribute has the most predictive power for each prediction served by the model.
What should you do?

  1. Stream prediction results to BigQuery. Use BigQuery’s CORR(X1, X2) function to calculate the Pearson correlation coefficient between each feature and the target variable.
  2. Use Vertex Explainable AI. Submit each prediction request with the explain' keyword to retrieve feature attributions using the sampled Shapley method.
  3. Use Vertex AI Workbench user-managed notebooks to perform a Lasso regression analysis on your model, which will eliminate features that do not provide a strong signal.
  4. Use the What-If tool in Google Cloud to determine how your model will perform when individual features are excluded. Rank the feature importance in order of those that caused the most significant performance drop when removed from the model.

Answer(s): B



You work on a team that builds state-of-the-art deep learning models by using the TensorFlow framework. Your team runs multiple ML experiments each week, which makes it difficult to track the experiment runs. You want a simple approach to effectively track, visualize, and debug ML experiment runs on Google Cloud while minimizing any overhead code. How should you proceed?

  1. Set up Vertex AI Experiments to track metrics and parameters. Configure Vertex AI TensorBoard for visualization.
  2. Set up a Cloud Run function to write and save metrics files to a Cloud Storage bucket. Configure a Google Cloud VM to host TensorBoard locally for visualization.
  3. Set up a Vertex AI Workbench notebook instance. Use the instance to save metrics data in a Cloud Storage bucket and to host TensorBoard locally for visualization.
  4. Set up a Cloud Run function to write and save metrics files to a BigQuery table. Configure a Google Cloud VM to host TensorBoard locally for visualization.

Answer(s): A



Your team is prototyping a novel image captioning application. The team plans to test a variety of models, including a Google-supported foundation model and a popular open-weight model, and wants to quickly determine the approach with the highest quality and least latency. All prototyping work must be conducted in a secure, collaborative environment. You need to recommend the most efficient approach for the team to access and compare these models.
What should you do?

  1. Create an Agent Platform Pipelines workflow that includes two separate training components: one for the foundation model and one for the open-weight model. Use the pipeline to run a comparison test.
  2. Create an Agent Platform Colab Enterprise notebook. Access both the foundation model and the open-weight model by using the Model Garden SDK.
  3. Create a Compute Engine VM instance with a Deep Learning VM image. Install the necessary SDKs and open-source libraries (e.g., Hugging Face Transformers). Download both models for in-memory comparison.
  4. Create an Agent Platform Workbench instance. Install the necessary open-source libraries (e.g., Hugging Face Transformers). Download the open-weight model from an external repository to run the model on the notebook VM.

Answer(s): B

Explanation:

Agent Platform Colab Enterprise provides a managed, collaborative notebook environment with Google Cloud security and governance capabilities. Model Garden provides centralized access to Google-supported foundation models as well as open models, enabling the team to prototype and compare model quality and latency efficiently without manually downloading and configuring separate model environments.


Reference:

https://cloud.google.com/model-garden https://docs.cloud.google.com/colab/docs?hl=en



You developed a Vertex AI pipeline that trains a classification model on data stored in a large BigQuery table. The pipeline has four steps, where each step is created by a Python function that uses the KubeFlow v2 API. The components have the following names:

You launch your Vertex AI pipeline as the following:

You perform many model iterations by adjusting the code and parameters of the training step. You observe high costs associated with the development, particularly the data export and preprocessing steps. You need to reduce model development costs.
What should you do?

  1. Change the components’ YAML filenames to export.yaml, preprocess,yaml, f "train-
    {dt}.yaml", f"calibrate-{dt).vaml".
  2. Add the {"kubeflow.v1.caching": True} parameter to the set of params provided to your PipelineJob.
  3. Move the first step of your pipeline to a separate step, and provide a cached path to Cloud Storage as an input to the main pipeline.
  4. Change the name of the pipeline to f"my-awesome-pipeline-{dt}".

Answer(s): A



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