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Google PROFESSIONAL MACHINE LEARNING ENGINEER Exam Prep
Professional Machine Learning Engineer (Page 3 )

Updated On: 24-Aug-2026
Page 3 of 44
PDF with 375 Questions

You are developing ML models with AI Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?

  1. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job.
  2. Use the gcloud command-line tool to submit training jobs on AI Platform when you update your code.
  3. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository.
  4. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud
    Storage using a sensor.

Answer(s): C

Explanation:

Option C is correct because Cloud Build with Cloud Source Repositories enables automated retraining triggers on code pushes, providing version-controlled, repeatable benchmarks with minimal manual intervention and cost control.
A) Incorrect — Cloud Functions reacting to Cloud Storage changes is event-driven but does not inherently provide developer-friendly version control or reliable, repository-backed trigger for ML benchmarks.
B) Incorrect — Using gcloud to submit jobs is manual per update and lacks continuous integration/automated triggering tied to code changes.
D) Incorrect — Cloud Composer (Airflow) daily checks introduces unnecessary scheduling overhead and may miss immediate retraining opportunities after code changes.


Reference:

https://cloud.google.com/ai-platform/training/docs/training-jobs



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: [`drivers_license', `passport', `credit_card']. You cannot change the format of the labels. Which loss function should you use?

  1. Categorical hinge
  2. Binary cross-entropy
  3. Categorical cross-entropy
  4. Sparse categorical cross-entropy

Answer(s): D

Explanation:

se sparse_categorical_crossentropy. Examples for above 3-class classification problem: [1] , [2], [3]


Reference:

https://stats.stackexchange.com/questions/326065/cross-entropy-vs-sparse-cross-entropy-when-to- use-one-over-the-other



You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations AI to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?

  1. Use the "Other Products You May Like" recommendation type to increase the click-through rate.
  2. Use the "Frequently Bought Together" recommendation type to increase the shopping cart size for each order.
  3. Import your user events and then your product catalog to make sure you have the highest quality event stream.
  4. Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.

Answer(s): B

Explanation:

Frequently bought together' recommendations aim to up-sell and cross-sell customers by providing product.


Reference:

https://rejoiner.com/resources/amazon-recommendations-secret-selling-online/



You are designing an architecture with a serverless ML system to enrich customer support tickets with informative metadata before they are routed to a support agent. You need a set of models to predict ticket priority, predict ticket resolution time, and perform sentiment analysis to help agents make strategic decisions when they process support requests. Tickets are not expected to have any domain-specific terms or jargon.

The proposed architecture has the following flow:



Which endpoints should the Enrichment Cloud Functions call?

  1. 1 = AI Platform, 2 = AI Platform, 3 = AutoML Vision
  2. 1 = AI Platform, 2 = AI Platform, 3 = AutoML Natural Language
  3. 1 = AI Platform, 2 = AI Platform, 3 = Cloud Natural Language API
  4. 1 = Cloud Natural Language API, 2 = AI Platform, 3 = Cloud Vision API

Answer(s): C

Explanation:

Option C is correct because: 1) priority prediction and 2) resolution time prediction can be served by models deployed in AI Platform (ML service for custom models). 3) sentiment analysis can be served by Cloud Natural Language API, which provides sentiment analysis suitable for text without domain-specific terms. Incorrect — A) AutoML Vision is for image data, not text. Incorrect — B) AutoML Natural Language would be suitable for sentiment but the combination used here requires a scalable custom-model approach in AI Platform for the two numeric predictions. Incorrect — D) Cloud Natural Language API cannot handle the two custom-priority/resolution-time predictions as hosted AI Platform models.



You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation data. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?

  1. Apply a dropout parameter of 0.2, and decrease the learning rate by a factor of 10.
  2. Apply a L2 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
  3. Run a hyperparameter tuning job on Vertex AI Vizier to optimize for the L2 regularization and dropout parameters.
  4. Run a hyperparameter tuning job on Vertex AI Vizier to optimize for the learning rate, and increase the number of neurons by a factor of 2.

Answer(s): C

Explanation:

Option C is correct because Vertex AI Vizier can search hyperparameters (e.g., regularization and dropout) to reduce overfitting by finding balanced capacity and regularization settings, improving validation performance.
A) Incorrect — while dropout helps regularization, decreasing learning rate with fixed dropout is not a comprehensive hyperparameter search strategy and may not yield optimal regularization balance.
B) Incorrect — L2=0.4 is a large regularization value that may underfit; paired with a fixed learning-rate adjustment does not explore the broader hyperparameter space.
D) Incorrect — increasing neurons increases capacity and may worsen overfitting; Vizier should optimize relevant regularization/architecture jointly rather than indiscriminately increasing size.



You built and manage a production system that is responsible for predicting sales numbers. Model accuracy is crucial, because the production model is required to keep up with market changes. Since being deployed to production, the model hasn't changed; however the accuracy of the model has steadily deteriorated. What issue is most likely causing the steady decline in model accuracy?

  1. Poor data quality
  2. Lack of model retraining
  3. Too few layers in the model for capturing information
  4. Incorrect data split ratio during model training, evaluation, validation, and test

Answer(s): B

Explanation:

Option B is correct because in production without model changes, deterioration in accuracy often indicates concept drift and the need for model retraining with fresh data to adapt to changing patterns. A) Poor data quality could cause issues, but if data quality were the root cause, it would likely be apparent from data validation and would not necessitate continuous retraining when data quality remains degraded over time. C) Too few layers is a model capacity issue, not a time-varying production accuracy problem. D) Incorrect data split ratio affects evaluation metrics during training, not ongoing production performance.



You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?

  1. Create a tf.data.Dataset.prefetch transformation.
  2. Convert the images to tf.Tensor objects, and then run Dataset.from_tensor_slices().
  3. Convert the images to tf.Tensor objects, and then run tf.data.Dataset.from_tensors().
  4. Convert the images into TFRecords, store the images in Cloud Storage, and then use the tf.data API to read the images for training.

Answer(s): D

Explanation:

Option D is correct because TFRecords with Cloud Storage enable scalable, streaming ingestion for large datasets that don’t fit in memory, fitting Google-recommended best practices for efficient I/O and parallel data loading with tf.data. A) prefetch helps latency but assumes data already available via a dataset; it doesn’t address large-scale storage and streaming directly. B) and C) from_tensor_slices/from_tensors require loading entire data into memory, causing OOM errors. They are impractical for large image datasets. Therefore D properly decouples storage from processing and leverages tf.data reading of TFRecords.


Reference:

https://www.tensorflow.org/api_docs/python/tf/data/Dataset



You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your model's features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis. Which algorithms should you use to build the model?

  1. Classification
  2. Reinforcement Learning
  3. Recurrent Neural Networks (RNN)
  4. Convolutional Neural Networks (CNN)

Answer(s): C

Explanation:

Option C is correct because Recurrent Neural Networks (RNNs) are suited for sequence data and time-series forecasting, enabling daily updates as new inventory data arrives and capturing temporal dependencies (history, seasonality). Incorrect — A) Classification is not ideal for predicting continuous inventory quantities or time-series values. Incorrect — B) Reinforcement Learning focuses on agent-environment interactions and long-term rewards, not standard supervised demand forecasting with daily updates. Incorrect — D) Convolutional Neural Networks are best for spatial/visual data, not typical tabular time-series inventory prediction.



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