Google Professional Machine Learning Engineer Practice Exam
Google Professional Machine Learning Engineer Practice Exam
About Google Professional Machine Learning Engineer Exam
The Google Professional Machine Learning Engineer exam has been developed to evaluate the candidates ability to design, build and productionize ML models for solving business challenges. Together with the ability to use Google Cloud technologies and knowledge and skills of proven ML models and techniques.
Skills Needed
The Google Professional Machine Learning Engineer is responsible -
- To perform AI throughout the ML development process
- To collaborates closely with other job roles to ensure long-term success of models.
Knowledge Required
The ML Engineer should have -
- Proficiency in all aspects of model architecture, data pipeline interaction, and metrics interpretation.
- Familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance.
- Thorough understanding of training, retraining, deploying, scheduling, monitoring, and improving models
- Skills to design and create scalable solutions for optimal performance.
Exam Evaluates
The exam assesses your ability to -
- Frame ML problems
- Develop ML models
- Architect ML solutions
- Automate and orchestrate ML pipelines
- Design data preparation and processing systems
- Monitor, optimize, and maintain ML solutions
Exam Details
- Exam Duration: 2 hours
- Language: English
- Exam format: 50-60 multiple choice and multiple select questions
- Prerequisites: None
Exam Delivery Method
- Online-proctored exam from a remote location
- Onsite-proctored exam at a testing center
Recommended experience
Candidate is required to have more than 3 years of industry experience including 1 or more years designing and managing solutions using Google Cloud.
Exam Course Outline
The Google Professional Machine Learning Engineer Practice Exam covers the latest and updated topics -
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
What do we offer?
- Full-Length Mock Test with unique questions in each test set
- Practice objective questions with section-wise scores
- In-depth and exhaustive explanation for every question
- Reliable exam reports evaluating strengths and weaknesses
- Latest Questions with an updated version
- Tips & Tricks to crack the test
- Unlimited access
What are our Practice Exams?
- Practice exams have been designed by professionals and domain experts that simulate real-time exam scenario.
- Practice exam questions have been created on the basis of content outlined in the official documentation.
- Each set in the practice exam contains unique questions built with the intent to provide real-time experience to the candidates as well as gain more confidence during exam preparation.
- Practice exams help to self-evaluate against the exam content and work towards building strength to clear the exam.
- You can also create your own practice exam based on your choice and preference
