Google Cloud Professional ML Engineer Practice Tests | 2026

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July 2026 | Routine Review as per June latest exam guidelines

Jan 2026 | Routine Review Per Latest Exam Guideline & Patterns

Sept 2025 | Additional Questions are added according to latest exam guide till date

*Updated 21 April 2024

*Updated 22 April 2024

*Updated 23 April 2024

*Updated 24 April 2024

*Updated 03 April 2025

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1. This version of the Professional Machine Learning Engineer exam covers tasks related to generative AI, including building AI solutions using Model Garden and Vertex AI Agent Builder, and evaluating generative AI solutions.

2. Note: The exam does not directly assess coding skill. If you have a minimum proficiency in Python and Cloud SQL, you should be able to interpret any questions with code snippets.

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Preparing for the Google Cloud Professional Machine Learning Engineer Certification?
This course provides 2026-aligned, exam-accurate practice tests built directly from Google’s latest exam guide.

These practice tests simulate the real exam format and strengthen your mastery across:

• ML model architecture and training on Google Cloud
• Vertex AI pipelines, training, serving, and monitoring
• AutoML workflows for tabular, text, image, and video data
• BigQuery ML: model building, prediction, feature engineering
• Generative AI: Model Garden, Vertex AI Agent Builder, RAG applications
• Distributed training with CPUs, GPUs, and TPUs
• MLOps practices including CI/CD, validation, lineage, and retraining
• Batch & online inference, A/B testing, registry, scaling, endpoint tuning
• Model explainability, bias detection, fairness, and Responsible AI

Each question includes detailed explanations to help you understand the correct reasoning and avoid common pitfalls seen on the real exam.

This is your complete practice environment to become fully confident before taking the certification.

Benefits of Certification

• Industry Recognition: Validates your skills to employers, potential clients, and peers.

• Career Advancement: Enhances your professional credentials and can lead to career development opportunities.

• Community and Networking: Opens the door to a network of Google Cloud certified professionals.

Don't just dream of becoming a Google Cloud Certified Professional Machine Learning Engineer – make it a reality!

• Start your practice today and take a confident step towards a successful career.

Realistic & Challenging Practice for Real-World Success

• Sharpen Your Skills

  • Put your Google Cloud expertise to the test and identify areas for improvement with meticulously designed practice Exam. Experience exam-like scenarios and challenging questions that closely mirror the official Google Cloud Machine Learning Engineer certification.

About Practice Assessment-

1. Exam Purpose and Alignment

• Clear Objectives: Define exactly what the exam intends to measure (knowledge, skills, judgment). Closely tied to the competencies required for professional practice.

• Alignment with Standards: The exam aligns with relevant latest exam standards, guidelines. This reinforces the validity and relevance of the exam.

2. Questions in assessment

• Relevance: Focus on real-world scenarios and problems that professionals are likely to encounter in their practice.

• Cognitive Level: Include a mix of questions that assess different levels of thinking:

  • Knowledge/Recall

  • Understanding/Application

  • Analysis/Evaluation

• Clarity: Best effort - Questions to be concise, unambiguous, and free from jargon or overly technical language.

• Reliability: Questions to consistently measure the intended knowledge or skill, reducing the chance of different interpretations.

• No Trickery: Avoided "trick" questions for simplicity. Instead, focus on testing genuine understanding.

3. Item Types

• Variety: Incorporated diverse question formats best suited to the knowledge/skill being tested. This could include:

  • Multiple-choice questions

  • Comprehensive explanations

  • Case studies with extended response(Where ever needed)

  • Scenario-based questions

  • Simulations (where applicable)

• Balance: Ensured a balanced mix of item types to avoid over-reliance on any single format.

Key Features & Benefits:

• • Up-to-Date & Exam-Aligned Questions: Updated to reflect the latest exam syllabus, questions mirror the difficulty, format, and content areas of the actual exam.

  • Updated: Practice exam is constantly updated to reflect the latest exam changes and ensure you have the most up-to-date preparation resources.

  • Comprehensive Coverage: Questions span the entire breadth of the certification exam, including:

    • Framing the ML Problem: Defining business objectives, translating them into ML solutions, and evaluating potential solutions.

    • Data Engineering: Ingesting, transforming, cleaning, validating, and storing data for model training and deployment.

    • Modeling: Feature engineering, model selection, hyperparameter tuning, and model evaluation (both technical and business metrics).

    • ML Infrastructure: Building ML pipelines, automating training and retraining, and monitoring deployed models.

    • Operationalizing ML Models: Deploying models to production, A/B testing, model scaling, and continuous evaluation.

  
  

  • Detailed Explanations for Every Answer: We don't just tell you if you got it right or wrong – we provide clear explanations to reinforce concepts and help you pinpoint areas for improvement.

  • Scenario-Based Challenges: Test your ability to apply learned principles in complex real-world scenarios, just like the ones you'll encounter on the exam.

  • Progress Tracking: Monitor your performance and pinpoint specific topics that require further study.

• Why Choose Practice Exam ?

  • Boost Confidence, Reduce Anxiety: Practice makes perfect! Arrive at the exam confident knowing you've faced similarly challenging questions.

  • Cost-Effective Supplement: Practice simulators, when combined with thorough studying, enhance your chances of success and save you from costly exam retakes.

Target Audience

• ML engineers with at least 3 years of industry experience, including at least one year utilizing GCP.

• Data scientists and data engineers with experience in building and deploying ML solutions.

Prerequisites

• There are no formal prerequisites. However, Google recommends the following:

  • Experience with building and managing production-ready ML solutions.

  • Proficiency in Python programming.

  • Basic familiarity with SQL.

  • Working knowledge of GCP services (Compute Engine, BigQuery, Cloud Storage, etc.)

Exam Topics

This version of the Professional Machine Learning Engineer exam covers tasks related to generative AI, including building AI solutions using Model Garden and Vertex AI Agent Builder, and evaluating generative AI solutions.

The exam covers the following key domains:

Section 1: Architecting low-code AI solutions (~13% of the exam)

1.1 Developing ML models using BigQuery ML or AutoML on Gemini Enterprise Agent Platform.

Considerations include:

• Building models in BigQuery ML or Agent Platform AutoML (e.g., classification, regression, forecasting, and clustering) based on the business problem

• Performing feature engineering or selection using BigQuery ML

• Generating predictions using BigQuery ML

• Training models using Agent Platform AutoML

• Fine-tuning Gemini models using BigQuery

1. 2 Building AI solutions using Google Cloud AI APIs or foundational models. Considerations

include:

• Evaluating and selecting the appropriate model for a given task from Gemini Enterprise

• Agent Platform Model Garden

• Building applications using industry-specific APIs (e.g., Document AI API, Vision API, and

• Translate API)

• Building solutions and tuning models for specific use cases (e.g., Gemini, Imagen, Veo, and models as a service in Model Garden)

• Optimizing Gemini-based applications for cost, latency, and availability

Section 2: Collaborating within and across teams to manage data and models (~16% of the exam)

2.1 Exploring and preprocessing data for ML. Considerations include:

• Organizing and exploring different data types (e.g., tabular, text, and images) for efficient experimenting, training, and serving

• Choosing the right tool for data preprocessing based on scale and complexity (e.g.,

• BigQuery [SQL], Dataflow, Apache Spark, and in-memory Python frameworks)

• Creating and consolidating features in Gemini Enterprise Agent Platform Feature Store

• Ensuring data privacy and handling sensitive information (e.g., personally identifiable information [PII])

2.2 Model prototyping using notebooks (e.g., Gemini Enterprise Agent Platform Workbench and Colab Enterprise). Considerations include:

• Applying collaboration and security best practices when setting up and running notebook environments

• Developing models in Agent Platform Workbench or Colab Enterprise notebooks using common frameworks (e.g., PyTorch, sklearn, and JAX)

• Using a variety of foundational and open-source models in Model Garden to create model prototypes in notebook environments

2.3 Tracking and running ML experiments. Considerations include:

• Choosing the appropriate Google Cloud environment for development and experimentation (e.g., Experiments on Gemini Enterprise Agent Platform, Gemini Enterprise Agent Platform Pipelines, and Kubeflow Pipelines) given the framework

• Evaluating predictive and gen AI solutions (e.g., model evaluation metrics and LLM-as-a-judge)

• Tracking and comparing model artifacts, versions, and lineage (e.g., Experiments on Agent Platform and Gemini Enterprise Agent Platform ML Metadata)

Section 3: Scaling prototypes into ML models (~21% of the exam)

3.1 Building models given the task considering cost, complexity, latency, and scalability.

Considerations include:

• Choosing the model type (e.g., ARIMA, DNN, and LLM)

• Choosing the product (e.g., Agent Platform AutoML, BigQuery ML, and Agent Platform Pipelines)

• Choosing the deployment strategy

• Modeling techniques given interpretability requirements

3.2 Training models. Considerations include:

• Organizing training data (e.g., tabular, text, speech, images, and videos) on Google Cloud (e.g., Cloud Storage and BigQuery)

• Ingesting structured and unstructured data from various sources into training pipelines

• Model training using different software development kits (SDKs) (e.g., Agent Platform custom training, Kubeflow on Google Kubernetes Engine [GKE], Agent Platform AutoML, and Tabular Workflows) and organizing training on Google Cloud

• Troubleshooting ML model training failures

• Hyperparameter tuning

• Fine-tuning foundational models from Agent Platform and Model Garden and when tuning should be considered

3.3 Choosing appropriate hardware for training. Considerations include:

• Evaluation of compute and accelerator options (e.g., CPU, GPU, and TPU)

• Understanding the options for distributed training on GPUs and TPUs using data and model parallelism strategies

Section 4: Serving and scaling models (~20% of the exam)

4.1 Serving models. Considerations include:

• Deploying models for batch and online inference using appropriate services (e.g., Agent Platform, Model Garden, Cloud Run, and GKE)

• Packaging and serving models from different frameworks (e.g., PyTorch and XGBoost) using prebuilt and custom containers

• Organizing and versioning models in Gemini Enterprise Agent Platform Model Registry

• Implementing model rollout strategies (e.g., A/B testing and canary deployments) to compare model versions

• Developing solutions for inference preprocessing and postprocessing

4.2 Scaling online model serving. Considerations include:

• Managing and serving features using Agent Platform Feature Store

• Deploying models to public and private endpoints

• Choosing appropriate hardware (e.g., CPU, GPU, TPU, and edge)

• Scaling the serving backend based on the throughput (e.g., Gemini Enterprise Agent Platform Inference and containerized serving)

• Tuning ML models for training and serving in production

Section 5: Automating and orchestrating ML pipelines (~18% of the exam)

5.1 Developing end-to-end ML pipelines. Considerations include:

• Validating data and models

• Building and orchestrating pipelines using managed or unmanaged services and from templates or custom solutions (e.g., Agent Platform Pipelines, Managed Service for Apache Airflow, and Ray on Gemini Enterprise Agent Platform)

• Ensuring consistent data preprocessing between training and serving

5.2 Automating model retraining. Considerations include:

• Determining an appropriate retraining policy

• Deploying models in continuous integration, continuous delivery, and continuous training (CI/CD/CT) pipelines (e.g., Cloud Build)

Section 6: Monitoring AI solutions (~13% of the exam)

6.1 Identifying risks to AI solutions. Considerations include:

• Building secure AI systems by protecting against unintentional exploitation and leaks of data or models (e.g., data exfiltration, malicious prompting, and sharing sensitive data with LLMs) using the appropriate security tool (e.g., Regex, safety filters, and Model Armor)

• Aligning with responsible AI practices (e.g., monitoring for bias)

• Model explainability on Agent Platform (e.g., Agent Platform Inference)

6.2 Monitoring, testing, and troubleshooting AI solutions. Considerations include:

• Configuring and using Model Monitoring on Gemini Enterprise Agent Platform to establish continuous evaluation metrics for production models

• Monitoring for common issues (e.g., training-serving skew, data drift, concept drift, and feature attribution drift)

• Monitoring, testing, and evaluating gen AI solutions

Exam Details

• Length: Two hours

  Registration fee: $200 (plus tax where applicable)

  Languages: English, Japanese

  Exam format: 50-60 multiple choice and multiple select questions

  Exam delivery method:

  a. Take the online-proctored exam from a remote location, review the online testing requirements.

  b. Take the onsite-proctored exam at a testing center, locate a test center near you

  Prerequisites: None

  Recommended experience: 3+ years of industry experience including 1 or more years designing and managing solutions using Google Cloud.

Certification Renewal / Recertification: Candidates must recertify in order to maintain their certification status. Unless explicitly stated in the detailed exam descriptions, all Google Cloud certifications are valid for two years from the date of certification. Recertification is accomplished by retaking the exam during the recertification eligibility time period and achieving a passing score. You may attempt recertification starting 60 days prior to your certification expiration date.