Google Professional Machine Learning Engineer Practice Exam

Google Professional Machine Learning Engineer Practice Exam

A Professional Machine Learning Engineer utilizes Google Cloud technologies and expertise in established models and techniques to construct, assess, deploy, and enhance ML models. This individual manages intricate, extensive datasets and develops code that is both repeatable and reusable. Throughout the ML model development process, considerations of responsible AI and fairness are integral, with close collaboration with other roles to ensure the sustained success of ML-driven applications.

The ML Engineer possesses robust programming abilities and proficiency with data platforms and distributed data processing tools. Proficiency extends to model architecture, as well as the creation and interpretation of data and ML pipelines and metrics. Familiarity with fundamental MLOps concepts, application development, infrastructure management, data engineering, and data governance is also part of the ML Engineer's skill set. Additionally, the ML Engineer facilitates ML accessibility and enables cross-organizational teams by undertaking tasks such as model training, retraining, deployment, scheduling, monitoring, and enhancement, thereby crafting scalable, high-performance solutions.

The Professional Machine Learning Engineer exam evaluates your proficiency in:

• Designing low-code ML solutions
• Cooperating within and between teams to oversee data and models
• Expanding prototypes into ML models
• Deploying and expanding the reach of models
• Automating and coordinating ML pipelines
• Supervising ML solutions

Who should take the exam?

Google Professional Machine Learning Engineer is best for those with 3+ years of industry experience including 1 or more years designing and managing solutions using Google Cloud.

Exam Details

• Exam Name: Google Professional Machine Learning Engineer
• Exam Questions: 50-60
• Time Duration: 2 hours
• Exam Language: English

Google Professional Machine Learning Engineer Exam Course Outline 

The Exam covers the given topics  - 

Section 1: Learn Architecting low-code ML solutions (12%)

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

Section 2: Understand about Collaborating within and across teams to manage data and models (16%)

2.1 Exploring and preprocessing organization-wide data (e.g., Cloud Storage, BigQuery, Cloud 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
  

Section 3: Learn about Scaling prototypes into ML models (18%)

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)
  

Section 4: Understand about Serving and scaling models (19%)

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)
  

Section 5: Learn Automating and orchestrating ML pipelines (21%)

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

Section 6: Learn about Monitoring ML solutions (14%)

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