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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 181–185 (page: 38)

Updated On: 25-Oct-2026
Page 38 of 82
PDF with 402 Questions

You work at a marketing research firm. Your team’s current project involves analyzing a large volume of customer surveys. Your team decides to use LLMs in Model Garden and track model training artifacts. You need to determine how to effectively share notebooks within your team and how to orchestrate the ML workflow. You want to use Google-managed services as much as possible.
What should you do?

  1. Develop your code in Agent Platform Colab Enterprise, and orchestrate your ML workflow by using Agent Platform Pipelines.
  2. Develop your code in Agent Platform Colab Enterprise, and orchestrate your ML workflow by using Cloud Workflows.
  3. Develop your code in Agent Platform Workbench, and orchestrate your ML workflow by using Cloud Build.
  4. Develop your code in Agent Platform Workbench, and orchestrate your ML workflow by using Agent Platform Pipelines.

Answer(s): A

Explanation:

Agent Platform Colab Enterprise provides a Google-managed, collaborative notebook environment that simplifies sharing work across the team. Agent Platform Pipelines is the managed orchestration service for repeatable ML workflows and integrates with training jobs, Model Garden resources, metadata, and model artifacts.



You need to use TensorFlow to train an image classification model. Your dataset is located in a Cloud Storage directory and contains millions of labeled images. Before training the model, you need to prepare the data. You want the data preprocessing and model training workflow to be as efficient, scalable, and low maintenance as possible.
What should you do?

  1. 1. Create a Dataflow job that creates sharded TFRecord files in a Cloud Storage directory.
    2. Reference tf.data.TFRecordDataset in the training script.
    3. Train the model by using Vertex AI Training with a V100 GPU.
  2. 1. Create a Dataflow job that moves the images into multiple Cloud Storage directories, where each directory is named according to the corresponding label
    2. Reference tfds.folder_dataset:ImageFolder in the training script.
    3. Train the model by using Vertex AI Training with a V100 GPU.
  3. 1. Create a Jupyter notebook that uses an nt-standard-64 V100 GPU Vertex AI Workbench instance.
    2. Write a Python script that creates sharded TFRecord files in a directory inside the instance.
    3. Reference tf.data.TFRecordDataset in the training script.
    4. Train the model by using the Workbench instance.
  4. 1. Create a Jupyter notebook that uses an n1-standard-64, V100 GPU Vertex AI Workbench instance.
    2. Write a Python script that copies the images into multiple Cloud Storage directories, where each directory is named according to the corresponding label.
    3. Reference tfds.foladr_dataset.ImageFolder in the training script.
    4. Train the model by using the Workbench instance.

Answer(s): A



You developed a BigQuery ML linear regressor model by using a training dataset stored in a BigQuery table. New data is added to the table every minute. You are using Cloud Scheduler and Vertex AI Pipelines to automate hourly model training, and use the model for direct inference. The feature preprocessing logic includes quantile bucketization and MinMax scaling on data received in the last hour. You want to minimize storage and computational overhead.
What should you do?

  1. Preprocess and stage the data in BigQuery prior to feeding it to the model during training and inference.
  2. Use the TRANSFORM clause in the CREATE MODEL statement in the SQL query to calculate the required statistics.
  3. Create a component in the Vertex AI Pipelines directed acyclic graph (DAG) to calculate the required statistics, and pass the statistics on to subsequent components.
  4. Create SQL queries to calculate and store the required statistics in separate BigQuery tables that are referenced in the CREATE MODEL statement.

Answer(s): B

Explanation:

Using the TRANSFORM clause allows you to apply preprocessing steps directly within the model training process. This means that the required statistics for quantile bucketization and MinMax scaling can be calculated on-the-fly during model training and inference, minimizing storage overhead by not requiring separate tables to store these statistics. This approach also streamlines the workflow by integrating the preprocessing into the model training SQL query, thus reducing computational overhead and complexity in your pipeline.



You are developing a customer service chatbot for an ecommerce company using the Gemini Pro model. You estimate that the application will handle thousands of unique user queries daily. During testing, you observe that the model provides accurate responses, but the latency is high and the projected costs exceed the project's budget. You need to optimize the application to reduce latency and minimize costs.
What should you do?

  1. Modify the prompt to include detailed few-shot examples within the existing Gemini Pro context window to optimize the model's behavior.
  2. Transition to the Gemini Flash model. Use Agent Platform Experiments to iteratively evaluate and refine prompt engineering techniques to improve the quality of the answers.
  3. Implement a cache to store and serve responses for common user queries, and use Gemini Pro for unique queries.
  4. Fine-tune the Gemini Pro model using a dataset of successful customer service interactions to optimize the model's output length and style.

Answer(s): B

Explanation:

Gemini Flash is optimized for low-latency, cost-efficient inference while maintaining strong response quality, making it suitable for high-volume customer service applications. Transitioning from Gemini Pro to Gemini Flash reduces inference costs and response times. Agent Platform Experiments enables systematic evaluation and refinement of prompt engineering techniques to improve response accuracy, relevance, and consistency. This approach balances performance, cost efficiency, and answer quality while supporting thousands of unique user queries daily.



You are an ML engineer in the contact center of a large enterprise. You need to build a sentiment analysis tool that predicts customer sentiment from recorded phone conversations. You need to identify the best approach to building a model while ensuring that the gender, age, and cultural differences of the customers who called the contact center do not impact any stage of the model development pipeline and results.
What should you do?

  1. Convert the speech to text and extract sentiments based on the sentences.
  2. Convert the speech to text and build a model based on the words.
  3. Extract sentiment directly from the voice recordings.
  4. Convert the speech to text and extract sentiment using syntactical analysis.

Answer(s): A



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