Google Professional Machine Learning Engineer Exam Questions
Exam number/code:
Professional Machine Learning Engineer
Release/Update Date:
25 Apr, 2026
Available Number of Questions: Maximum of
283 Questions
Exam Name: Google Professional Machine Learning Engineer
Exam Duration: 120 Minutes
Related Certification(s):
Google Cloud Certified, Google Cloud Certified - Cloud Engineer Certifications
Google Professional Machine Learning Engineer Exam Topics - You’ll Be Tested in Actual Exam
The Google Professional-Machine-Learning-Engineer exam is a comprehensive assessment designed to evaluate your expertise in various aspects of machine learning. It covers a wide range of topics, including machine learning foundations, which delve into the core concepts and techniques that form the basis of ML, such as supervised and unsupervised learning, neural networks, and deep learning. The exam also assesses your understanding of TensorFlow, Google's powerful open-source machine learning framework, including its architecture, APIs, and best practices for building and deploying ML models. Additionally, you'll need to demonstrate your proficiency in machine learning systems design, covering topics like system architecture, data processing pipelines, and strategies for handling large-scale ML projects. Data analysis and interpretation are crucial skills tested in this exam, as you'll be expected to analyze and visualize data, identify patterns, and make informed decisions based on your findings. The exam also evaluates your ability to build and optimize machine learning models, including model training, hyperparameter tuning, and model evaluation techniques. Furthermore, it assesses your knowledge of machine learning operations (MLOps), which involves the deployment, monitoring, and maintenance of ML models in production environments. Lastly, the exam covers ethical considerations and biases in machine learning, emphasizing the importance of responsible ML practices and addressing potential biases and fairness issues.
Google Professional Machine Learning Engineer Exam Short Quiz
Attempt this Google Professional Machine Learning Engineer exam quiz to self-assess your preparation for the actual Google Professional Machine Learning Engineer exam. CertBoosters also provides premium Google Professional Machine Learning Engineer exam questions to pass the Google Professional Machine Learning Engineer exam in the shortest possible time. Be sure to try our free practice exam software for the Google Professional Machine Learning Engineer exam.
1of 0 questions |
Google Professional Machine Learning Engineer Exam Quiz
✓ 0 answered
🔖 0 bookmarked
GoogleProfessional Machine Learning Engineer
Q1:
You need to train a ControlNet model with Stable Diffusion XL for an image editing use case. You want to train this model as quickly as possible. Which hardware configuration should you choose to train your model?
○
AConfigure one a2-highgpu-1g instance with an NVIDIA A100 GPU with 80 GB of RAM. Use float32 precision during model training.
○
BConfigure one a2-highgpu-1g instance with an NVIDIA A100 GPU with 80 GB of RAM. Use bfloat16 quantization during model training.
○
CConfigure four n1-standard-16 instances, each with one NVIDIA Tesla T4 GPU with 16 GB of RAM. Use float32 precision during model training.
○
DConfigure four n1-standard-16 instances, each with one NVIDIA Tesla T4 GPU with 16 GB of RAM. Use float16 quantization during model training.
GoogleProfessional Machine Learning Engineer
Q2:
Your organization's marketing team is building a customer recommendation chatbot that uses a generative AI large language model (LLM) to provide personalized product suggestions in real time. The chatbot needs to access data from millions of customers, including purchase history, browsing behavior, and preferences. The data is stored in a Cloud SQL for PostgreSQL database. You need the chatbot response time to be less than 100ms. How should you design the system?
○
AUse BigQuery ML to fine-tune the LLM with the data in the Cloud SQL for PostgreSQL database, and access the model from BigQuery.
○
BReplicate the Cloud SQL for PostgreSQL database to AlloyDB. Configure the chatbot server to query AlloyDB.
○
CTransform relevant customer data into vector embeddings and store them in Vertex AI Search for retrieval by the LLM.
○
DCreate a caching layer between the chatbot and the Cloud SQL for PostgreSQL database to store frequently accessed customer data. Configure the chatbot server to query the cache.
GoogleProfessional Machine Learning Engineer
Q3:
You are an AI engineer working for a popular video streaming platform. You built a classification model using PyTorch to predict customer churn. Each week, the customer retention team plans to contact customers identified as at-risk for churning with personalized offers. You want to deploy the model while minimizing maintenance effort. What should you do?
○
AUse Vertex AI's prebuilt containers for prediction. Deploy the container on Cloud Run to generate online predictions.
○
BUse Vertex AI's prebuilt containers for prediction. Deploy the model on Google Kubernetes Engine (GKE), and configure the model for batch prediction.
○
CDeploy the model to a Vertex AI endpoint, and configure the model for batch prediction. Schedule the batch prediction to run weekly.
○
DDeploy the model to a Vertex AI endpoint, and configure the model for online prediction. Schedule a job to query this endpoint weekly.
GoogleProfessional Machine Learning Engineer
Q4:
You are an AI architect at a popular photo-sharing social media platform. Your organization's content moderation team currently scans images uploaded by users and removes explicit images manually. You want to implement an AI service to automatically prevent users from uploading explicit images. What should you do?
○
ADevelop a custom TensorFlow model in a Vertex AI Workbench instance. Train the model on a dataset of manually labeled images. Deploy the model to a Vertex AI endpoint. Run periodic batch inference to identify inappropriate uploads and report them to the content moderation team.
○
BTrain an image clustering model using TensorFlow in a Vertex AI Workbench instance. Deploy this model to a Vertex AI endpoint and configure it for online inference. Run this model each time a new image is uploaded to identify and block inappropriate uploads.
○
CCreate a dataset using manually labeled images. Ingest this dataset into AutoML. Train an image classification model and deploy it to a Vertex AI endpoint. Integrate this endpoint with the image upload process to identify and block inappropriate uploads. Monitor predictions and periodically retrain the model.
○
DSend a copy of every user-uploaded image to a Cloud Storage bucket. Configure a Cloud Run function that triggers the Cloud Vision API to detect explicit content each time a new image is uploaded. Report the classifications to the content moderation team for review.
GoogleProfessional Machine Learning Engineer
Q5:
You work for a company that sells corporate electronic products to thousands of businesses worldwide. Your company stores historical customer data in BigQuery. You need to build a model that predicts customer lifetime value over the next three years. You want to use the simplest approach to build the model. What should you do?
○
AAccess BigQuery Studio in the Google Cloud console. Run the create model statement in the SQL editor to create an ARIMA model.
○
BCreate a Vertex Al Workbench notebook. Use IPython magic to run the create model statement to create an ARIMA model.
○
CAccess BigQuery Studio in the Google Cloud console. Run the create model statement in the SQL editor to create an AutoML regression model.
○
DCreate a Vertex Al Workbench notebook. Use IPython magic to run the create model statement to create an AutoML regression model.
🎉 Google Professional Machine Learning Engineer Quiz Complete!
