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 -
Domain 1: Architecting low-code ML solutions (~12% of the exam)
1.1 Developing ML models by using BigQuery ML. Considerations include:
- Building the appropriate BigQuery ML model (e.g., linear and binary classification,
- regression, time-series, matrix factorization, boosted trees, autoencoders) based on the business problem
- Feature engineering or selection by using BigQuery ML
- Generating predictions by using BigQuery ML
1.2 Building AI solutions by using ML APIs. Considerations include:
- Building applications by using ML APIs (e.g., Cloud Vision API, Natural Language API,
- Cloud Speech API, Translation)
- Building applications by using industry-specific APIs (e.g., Document AI API, Retail API)
1.3 Training models by using AutoML. Considerations include:
Preparing data for AutoML (e.g., feature selection, data labeling, Tabular Workflows on AutoML)
- Using available data (e.g., tabular, text, speech, images, videos) to train custom models
- Using AutoML for tabular data
- Creating forecasting models using AutoML
- Configuring and debugging trained models
Domain 2: Collaborating within and across teams to manage data and models (~16% of the exam)
2.1 Exploring and preprocessing organization-wide data (e.g., Cloud Storage, BigQuery,
- Spanner, Cloud SQL, Apache Spark, Apache Hadoop). Considerations include:
- Organizing different types of data (e.g., tabular, text, speech, images, videos) for efficient training
- Managing datasets in Vertex AI
- Data preprocessing (e.g., Dataflow, TensorFlow Extended [TFX], BigQuery)
- Creating and consolidating features in Vertex AI Feature Store
- Privacy implications of data usage and/or collection (e.g., handling sensitive data such as personally identifiable information [PII] and protected health information [PHI])
2.2 Model prototyping using Jupyter notebooks. Considerations include:
- Choosing the appropriate Jupyter backend on Google Cloud (e.g., Vertex AI Workbench, notebooks on Dataproc)
- Applying security best practices in Vertex AI Workbench Using Spark kernels
- Integration with code source repositories
- Developing models in Vertex AI Workbench by using common frameworks (e.g., TensorFlow, PyTorch, sklearn, Spark, JAX)
2.3 Tracking and running ML experiments. Considerations include:
- Choosing the appropriate Google Cloud environment for development and experimentation (e.g., Vertex AI Experiments, Kubeflow Pipelines, Vertex AI
- TensorBoard with TensorFlow and PyTorch) given the framework
Domain 3: Scaling prototypes into ML models (~18% of the exam)
3.1 Building models. Considerations include:
- Choosing ML framework and model architecture
- Modeling techniques given interpretability requirements
3.2 Training models. Considerations include:
- Organizing training data (e.g., tabular, text, speech, images, videos) on Google Cloud (e.g., Cloud Storage, BigQuery)
- Ingestion of various file types (e.g., CSV, JSON, images, Hadoop, databases) into training
- Training using different SDKs (e.g., Vertex AI custom training, Kubeflow on Google Kubernetes Engine, AutoML, tabular workflows) Using distributed training to organize reliable pipelines Hyperparameter tuning
- Troubleshooting ML model training failures
3.3 Choosing appropriate hardware for training. Considerations include:
- Evaluation of compute and accelerator options (e.g., CPU, GPU, TPU, edge devices)
- Distributed training with TPUs and GPUs (e.g., Reduction Server on Vertex AI, Horovod)
Domain 4: Serving and scaling models (~19% of the exam)
4.1 Serving models. Considerations include:
- Batch and online inference (e.g., Vertex AI, Dataflow, BigQuery ML, Dataproc)
- Using different frameworks (e.g., PyTorch, XGBoost) to serve models
- Organizing a model registry
- A/B testing different versions of a model
4.2 Scaling online model serving. Considerations include:
- Vertex AI Feature Store
- Vertex AI public and private endpoints
- Choosing appropriate hardware (e.g., CPU, GPU, TPU, edge)
- Scaling the serving backend based on the throughput (e.g., Vertex AI Prediction, containerized serving)
- Tuning ML models for training and serving in production (e.g., simplification techniques, optimizing the ML solution for increased performance, latency, memory, throughput)
Domain 5: Automating and orchestrating ML pipelines (~21% of the exam)
5.1 Developing end-to-end ML pipelines. Considerations include:
- Data and model validation
- Ensuring consistent data pre-processing between training and serving
- Hosting third-party pipelines on Google Cloud (e.g., MLFlow)
- Identifying components, parameters, triggers, and compute needs (e.g., Cloud Build, Cloud Run)
- Orchestration framework (e.g., Kubeflow Pipelines, Vertex AI Pipelines, Cloud Composer)
- Hybrid or multicloud strategies
- System design with TFX components or Kubeflow DSL (e.g., Dataflow)
5.2 Automating model retraining. Considerations include:
- Determining an appropriate retraining policy
- Continuous integration and continuous delivery (CI/CD) model deployment (e.g., Cloud Build, Jenkins)
5.3 Tracking and auditing metadata. Considerations include:
- Tracking and comparing model artifacts and versions (e.g., Vertex AI Experiments, Vertex ML Metadata)
- Hooking into model and dataset versioning
- Model and data lineage
Domain 6: Monitoring ML solutions (~14% of the exam)
6.1 Identifying risks to ML solutions. Considerations include:
- Building secure ML systems (e.g., protecting against unintentional exploitation of data or models, hacking)
- Aligning with Google’s Responsible AI practices (e.g., biases)
- Assessing ML solution readiness (e.g., data bias, fairness)
- Model explainability on Vertex AI (e.g., Vertex AI Prediction)
6.2 Monitoring, testing, and troubleshooting ML solutions. Considerations include:
- Establishing continuous evaluation metrics (e.g., Vertex AI Model Monitoring, Explainable AI)
- Monitoring for training-serving skew
- Monitoring for feature attribution drift
- Monitoring model performance against baselines, simpler models, and across the time dimension
- Common training and serving errors
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
