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Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 66–70 (page: 15)

Updated On: 25-Oct-2026
Page 15 of 82
PDF with 402 Questions

You work as an ML engineer at a social media company, and you are developing a visual filter for users’ profile photos. This requires you to train an ML model to detect bounding boxes around human faces. You want to use this filter in your company’s iOS-based mobile phone application. You want to minimize code development and want the model to be optimized for inference on mobile phones.
What should you do?

  1. Train a model using AutoML Vision and use the “export for Core ML” option.
  2. Train a model using AutoML Vision and use the “export for Coral” option.
  3. Train a model using AutoML Vision and use the “export for TensorFlow.js” option.
  4. Train a custom TensorFlow model and convert it to TensorFlow Lite (TFLite).

Answer(s): A



You work for a bank. You have created a custom model to predict whether a loan application should be flagged for human review. The input features are stored in a BigQuery table. The model is performing well, and you plan to deploy it to production. Due to compliance requirements the model must provide explanations for each prediction. You want to add this functionality to your model code with minimal effort and provide explanations that are as accurate as possible.
What should you do?

  1. Create an AutoML tabular model by using the BigQuery data with integrated Vertex Explainable AI.
  2. Create a BigQuery ML deep neural network model and use the ML.EXPLAIN_PREDICT method with the num_integral_steps parameter.
  3. Upload the custom model to Vertex AI Model Registry and configure feature-based attribution by using sampled Shapley with input baselines.
  4. Update the custom serving container to include sampled Shapley-based explanations in the prediction outputs.

Answer(s): C



You are the Director of Data Science at a large company, and your Data Science team has recently begun using the Kubeflow Pipelines SDK to orchestrate their training pipelines. Your team is struggling to integrate their custom Python code into the Kubeflow Pipelines SDK. How should you instruct them to proceed in order to quickly integrate their code with the Kubeflow Pipelines SDK?

  1. Use the func_to_container_op function to create custom components from the Python code.
  2. Use the predefined components available in the Kubeflow Pipelines SDK to access Dataproc, and run the custom code there.
  3. Package the custom Python code into Docker containers, and use the load_component_from_file function to import the containers into the pipeline.
  4. Deploy the custom Python code to Cloud Functions, and use Kubeflow Pipelines to trigger the Cloud Function.

Answer(s): A



You are developing a custom TensorFlow classification model based on tabular data. Your raw data is stored in BigQuery. contains hundreds of millions of rows, and includes both categorical and numerical features. You need to use a MaxMin scaler on some numerical features, and apply a one-hot encoding to some categorical features such as SKU names. Your model will be trained over multiple epochs. You want to minimize the effort and cost of your solution.
What should you do?

  1. 1. Write a SQL query to create a separate lookup table to scale the numerical features.
    2. Deploy a TensorFlow-based model from Hugging Face to BigQuery to encode the text features.
    3. Feed the resulting BigQuery view into Vertex AI Training.
  2. 1. Use BigQuery to scale the numerical features.
    2. Feed the features into Vertex AI Training.
    3. Allow TensorFlow to perform the one-hot text encoding.
  3. 1. Use TFX components with Dataflow to encode the text features and scale the numerical features.
    2. Export results to Cloud Storage as TFRecords.
    3. Feed the data into Vertex AI Training.
  4. 1. Write a SQL query to create a separate lookup table to scale the numerical features.
    2. Perform the one-hot text encoding in BigQuery.
    3. Feed the resulting BigQuery view into Vertex AI Training.

Answer(s): C



You are building an application that extracts information from invoices and receipts. You want to implement this application with minimal custom code and training.
What should you do?

  1. Use the Cloud Vision API with TEXT_DETECTION type to extract text from the invoices and receipts, and use a pre-built natural language processing (NLP) model to parse the extracted text.
  2. Use the Cloud Document AI API to extract information from the invoices and receipts.
  3. Use Vertex AI Agent Builder with the pre-built Layout Parser model to extract information from the invoices and receipts.
  4. Train an AutoML Natural Language model to classify and extract information from the invoices and receipts.

Answer(s): B

Explanation:

The Cloud Document AI API is specifically designed for processing and extracting structured information from documents such as invoices and receipts. It provides pre-trained models that can recognize and extract relevant fields (e.g., invoice number, date, total amount) with minimal custom code or training. This solution requires minimal setup and is highly efficient for structured documents, making it the ideal choice for this use case.



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