You have developed a simple feedforward network on a very wide dataset. You trained the model with mini-batch gradient descent and L1 regularization. During training, you noticed the loss steadily decreasing before moving back to the top at a very sharp angle and starting to oscillate. You want to fix this behavior with minimal changes to the model. What should you do?

B- B. Explore the feature set to remove NaNs and clip any noisy outliers. Shuffle the data before retraining.

As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?

A- A. Use the batch prediction functionality of VertexAI

You are training a TensorFlow model on a structured dataset with 100 billion records stored in several CSV files. You need to improve the input/output execution performance. What should you do?

A- C. Convert the CSV files into shards of TFRecords, and store the data in Cloud Storage.

You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time. How should you build the model?

B- B. Use AutoML Natural Language to extract custom entities for classification.

You are an ML engineer at a global car manufacture. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

C- C. One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type.

Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers' account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

D- D. 1. Build a notification system on Firebase. 2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.

You trained a neural network on a small normalized wide dataset. The model performs well without overfitting, but you want to improve how the model pipeline processes the features because they are not all expected to be relevant for the prediction. You want to implement changes that minimize model complexity while maintaining or improving the model’s offline performance. What should you do?

C- C. Perform correlation analysis. Remove features that are highly correlated to one another and features that are not correlated to the target.

One of your models is trained using data provided by a third-party data broker. The data broker does not reliably notify you of formatting changes in the data. You want to make your model training pipeline more robust to issues like this. What should you do?

A- A. Use TensorFlow Data Validation to detect and flag schema anomalies.

Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:You followed the standard 80%-10%-10% data distribution across the training, testing, and evaluation subsets. How should you distribute the training examples across the train-test-eval subsets while maintaining the 80-10-10 proportion?

B- Distribute authors randomly across the train-test-eval subsets: (*) Train set: [TextA1, TextA2, TextD1, TextD2, ...] Test set: [TextB1, TextB2, ...] Eval set: [TexC1,TextC2 ...]

Your team has been tasked with creating an ML solution in Google Cloud to classify support requests for one of your platforms. You analyzed the requirements and decided to use TensorFlow to build the classifier so that you have full control of the model's code, serving, and deployment. You will use Kubeflow pipelines for the ML platform. To save time, you want to build on existing resources and use managed services instead of building a completely new model. How should you build the classifier?

C- Use an established text classification model on Vertex AI to perform transfer learning.

Professional Machine Learning Engineer - Google Cloud Certified Exam Questions

456

Total Questions

AUG

2026

Last Updated

1st

1st Try Guaranteed

Experts Verified

Taking the practice exam for the Professional Machine Learning Engineer - Google Cloud Certified certification is a critical step in your preparation journey. With a comprehensive set of 529 real practice questions, this exam provides candidates with an opportunity to experience the format and content of the actual certification test. Engaging with this extensive question bank allows for focused self-assessment, helping you identify areas of strength and pinpoint topics needing further review.

Each question is designed to reflect the kind of scenarios and knowledge areas you will encounter in the official exam, offering a realistic simulation of the test environment. As you work through the practice questions, you can develop effective strategies for time management and pacing, both essential for success. Additionally, analyzing your performance on this practice exam can inform your study plan, guiding you toward resources and topics that require further attention. By leveraging this tool, you can enhance your confidence and readiness, ensuring that you approach the actual certification exam with clarity and assurance.

Per page:

Welcome to the questions-and-answers listing page for the Professional Machine Learning Engineer - Google Cloud Certified exam. Here, you will find a comprehensive collection of 529 real practice questions designed to help you prepare effectively for this certification. Each question is a valuable resource to test your knowledge, identify areas for improvement, and build confidence as you approach the exam.

To make the most of this page, we recommend going through the questions methodically. Focus on understanding the reasoning behind each answer, whether you get it right or wrong. Actively engage with the material by noting down concepts or topics that require further study. It’s beneficial to revisit challenging questions regularly to reinforce your understanding and retention.

When preparing for exams like this, here are some study approach tips that can enhance your chances of success:

1. **Create a Study Schedule**: Allocate specific times for studying and stick to a routine. Consistent study periods will help you cover the material systematically without feeling rushed as the exam date approaches.

2. **Utilize a Variety of Resources**: Beyond practice questions, use books, online courses, and videos to gain diverse perspectives on machine learning concepts and the Google Cloud platform. This multifaceted approach helps deepen your understanding.

3. **Practice with Real-world Scenarios**: Wherever possible, apply your learning to practical situations. Engage in hands-on projects or case studies that require implementing machine learning solutions using Google Cloud. This practical experience will bridge the gap between theoretical knowledge and real-world application.

Good luck with your studies!

Question 1 Single Choice

Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?

A- 1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station. 2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction.

B- 1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station. 2. Dispatch an available shuttle and provide the map with the required stops based on the prediction.

C- 1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints. 2. Dispatch an appropriately sized shuttle and indicate the required stops on the map.

D- 1. Build a reinforcement learning model with tree-based classification models that predict the presence of passengers at shuttle stops as agents and a reward function around a distance-based metric. 2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.

Question 2 Single Choice

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?

Question 3 Single Choice

You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run. To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?

Question 4 Single Choice

What would be the appropriate configuration to build an ML model for detecting real-time anomalies in sensor data using Pub/Sub to handle incoming requests and storing the results for further analytics and visualization?

Question 5 Single Choice

You are training a deep learning model for semantic image segmentation with reduced training time. While using a Deep Learning VM Image, you receive the following error: The resource 'projects/deeplearning-platforn/zones/europe-west4-c/acceleratorTypes/nvidia-tesla-k80' was not found. What should you do?

Question 6 Single Choice

You trained a model in a Vertex AI Workbench notebook that has good validation RMSE. You defined 20 parameters with the associated search spaces that you plan to use for model tuning. You want to use a tuning approach that maximizes tuning job speed. You also want to optimize cost, reproducibility, model performance, and scalability where possible if they do not affect speed. What should you do?

A- Set up a cell to run a hyperparameter tuning job using Vertex AI Vizier with val_rmse specified as the metric in the study configuration.

B- Using a dedicated Python library such as Hyperopt or Optuna, configure a cell to run a local hyperparameter tuning job with Bayesian optimization.

C- Refactor the notebook into a parametrized and dockerized Python script, and push it to Container Registry. Use the UI to set up a hyperparameter tuning job in Vertex AI. Use the created image and include Grid Search as an algorithm.

D- Refactor the notebook into a parametrized and dockerized Python script, and push it to Container Registry. Use the command line to set up a hyperparameter tuning job in Vertex AI. Use the created image and include Random Search as an algorithm where maximum trial count is equal to parallel trial count.

Question 7 Single Choice

You work on a growing team of more than 50 data scientists who all use Vertex AI. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way. Which strategy should you choose?

Question 8 Single Choice

Your team is building a convolutional neural network (CNN)-based architecture from scratch. The preliminary experiments running on your on-premises CPU-only infrastructure were encouraging, but have slow convergence. You have been asked to speed up model training to reduce time-to-market. You want to experiment with virtual machines (VMs) on Google Cloud to leverage more powerful hardware. Your code does not include any manual device placement and has not been wrapped in Estimator model-level abstraction. Which environment should you train your model on?

Question 9 Single Choice

You used Vertex AI Workbench user-managed notebooks to develop a TensorFlow model. The model pipeline accesses data from Cloud Storage, performs feature engineering and training locally, and outputs the trained model in Vertex AI Model Registry. The end-to-end pipeline takes 10 hours on the attached optimized instance type. You want to introduce model and data lineage for automated re-training runs for this pipeline only while minimizing the cost to run the pipeline. What should you do?

A- 1. Use the Vertex AI SDK to create an experiment for the pipeline runs, and save metadata throughout the pipeline. 2. Configure a scheduled recurring execution for the notebook. 3. Access data and model metadata in Vertex ML Metadata.

B- 1. Use the Vertex AI SDK to create an experiment, launch a custom training job in Vertex training service with the same instance type configuration as the notebook, and save metadata throughout the pipeline. 2. Configure a scheduled recurring execution for the notebook. 3. Access data and model metadata in Vertex ML Metadata.

C- 1. Create a Cloud Storage bucket to store metadata. 2. Write a function that saves data and model metadata by using TensorFlow ML Metadata in one time-stamped subfolder per pipeline run. 3. Configure a scheduled recurring execution for the notebook. 4. Access data and model metadata in Cloud Storage.

D- 1. Refactor the pipeline code into a TensorFlow Extended (TFX) pipeline. 2. Load the TFX pipeline in Vertex AI Pipelines, and configure the pipeline to use the same instance type configuration as the notebook. 3. Use Cloud Scheduler to configure a recurring execution for the pipeline. 4. Access data and model metadata in Vertex AI Pipelines.

Question 10 Single Choice

You started working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?

Page: 1 / 46

Frequently Asked Questions

How realistic are the practice questions for the Professional Machine Learning Engineer exam?

The practice questions closely mirror the format and difficulty of the actual certification exam, providing a valuable way to assess your knowledge and readiness.

How should I use the practice questions to prepare for the exam?

You should regularly take practice questions, review the explanations for both correct and incorrect answers, and focus on areas where you struggle to enhance your understanding.

How many practice questions should I complete before taking the real exam?

It's recommended to complete as many practice questions as possible to build confidence and identify any weak areas, ideally aiming for at least 80-100 questions.

Can I track my progress while taking the practice exam?

Yes, most practice exam platforms provide tracking features to monitor your performance, helping you identify strengths and weaknesses over time.

Is it beneficial to retake the practice exam multiple times?

Absolutely, retaking the practice exam helps reinforce knowledge, improve retention, and increases familiarity with the question types you'll encounter in the actual exam.

Professional Machine Learning Engineer - Google Cloud Certified Exam Questions

Total Questions

Last Updated

1st Try Guaranteed