Google Google-Professional-Machine-Learning-Engineer -Exam Dumps 2026 Edition

Exam Title

Google Machine Learning Engineer

Total Questions

282

Exam Code:

Google-Professional-Machine-Learning-Engineer

Certification NameGoogle Professional Machine Learning Engineer Exam
Last Updated:
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Exam Demo

Viewing questions 1-5 out of 18 questions.

Topic 1 - Professional Machine Learning Engineer
Question #1 Topic 1
Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: ['driversjicense', 'passport', 'credit_card']. Which loss function should you use?
  • A:
    Categorical hinge
  • B:
    Binary cross-entropy
  • C:
    Categorical cross-entropy
  • D:
    Sparse categorical cross-entropy
Question #2 Topic 1
You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to explore model performance using multiple model architectures, store training data, and be able to compare the evaluation metrics in the same dashboard. What should you do?
  • A:
    Create multiple models using AutoML Tables
  • B:
    Automate multiple training runs using Cloud Composer
  • C:
    Run multiple training jobs on Al Platform with similar job names
  • D:
    Create an experiment in Kubeflow Pipelines to organize multiple runs
Question #3 Topic 1
You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents. You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?
  • A:
    Use the class distribution to generate 10% positive examples
  • B:
    Use a convolutional neural network with max pooling and softmax activation
  • C:
    Downsample the data with upweighting to create a sample with 10% positive examples
  • D:
    Remove negative examples until the numbers of positive and negative examples are equal
Question #4 Topic 1
You have a demand forecasting pipeline in production that uses Dataflow to preprocess raw data prior to model training and prediction. During preprocessing, you employ Z-score normalization on data stored in BigQuery and write it back to BigQuery. New training data is added every week. You want to make the process more efficient by minimizing computation time and manual intervention. What should you do?
  • A:
    Normalize the data using Google Kubernetes Engine
  • B:
    Translate the normalization algorithm into SQL for use with BigQuery
  • C:
    Use the normalizer_fn argument in TensorFlow's Feature Column API
  • D:
    Normalize the data with Apache Spark using the Dataproc connector for BigQuery
Question #5 Topic 1
You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%. How can you make your production model more accurate?
  • A:
    Normalize the data for the training, and test datasets as two separate steps.
  • B:
    Split the training and test data based on time rather than a random split to avoid leakage
  • C:
    Add more data to your test set to ensure that you have a fair distribution and sample for testing
  • D:
    Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.
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