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
  • Registration fee: $200 (plus tax where applicable)
  • 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 following topics - 

Domain 1: Overview of Framing ML problems

1.1 Translating business challenges into ML use cases. Considerations include:

  • Explain choosing the best solution (ML vs. non-ML, custom vs. pre-packaged [e.g., AutoML, Vision API]) based on the business requirements 
  • Explain defining how the model output should be used to solve the business problem
  • Explain deciding how incorrect results should be handled
  • Explain identifying data sources (available vs. ideal)

1.2 Defining ML problems. Considerations include:

  • Explain Problem type (e.g., classification, regression, clustering)
  • Explain Outcome of model predictions
  • Explain Input (features) and predicted output format

1.3 Defining business success criteria. Considerations include:

  • Explain Alignment of ML success metrics to the business problem
  • Explain Determining when a model is deemed unsuccessful

1.4 Identifying risks to feasibility of ML solutions. Considerations include: 

  • Explain Assessing and communicating business impact
  • Explain Assessing ML solution readiness
  • Explain Assessing data readiness and potential limitations
  • Explain Aligning with Google’s Responsible AI practices (e.g., different biases)


Domain 2: Overview of Architecting ML solutions

2.1 Designing reliable, scalable, and highly available ML solutions. Considerations include:

  • Explain Choosing appropriate ML services for the use case (e.g., Cloud Build, Kubeflow)
  • Explain Component types (e.g., data collection, data management)
  • Explain Exploration/analysis
  • Explain Feature engineering
  • Explain Logging/management
  • Explain Automation
  • Explain Orchestration
  • Explain Monitoring
  • Explain Serving

2.2 Choosing appropriate Google Cloud hardware components. Considerations include:

  • Explain evaluation of compute and accelerator options (e.g., CPU, GPU, TPU, edge devices) 


2.3 Designing architecture that complies with security concerns across sectors/industries. 

  • Explain Building secure ML systems (e.g., protecting against unintentional exploitation of data/model, hacking)
  • Explain Privacy implications of data usage and/or collection (e.g., handling sensitive data such as Personally Identifiable Information [PII] and Protected Health Information [PHI])


Domain 3: Overview of Designing data preparation and processing systems

3.1 Exploring data (EDA). Considerations include:

  • Explain visualization
  • Explain statistical fundamentals at scale
  • Explain evaluation of data quality and feasibility
  • Explain establishing data constraints (e.g., TFDV)

3.2 Building data pipelines. Considerations include:

  • Explain Organizing and optimizing training datasets
  • Explain Data validation
  • Explain Handling missing data
  • Explain Handling outliers
  • Explain Data leakage

3.3 Creating input features (feature engineering). Considerations include:

  • Explain Ensuring consistent data pre-processing between training and serving
  • Explain Encoding structured data types
  • Explain Feature selection
  • Explain Class imbalance
  • Explain Feature crosses
  • Explain Transformations (TensorFlow Transform)


Domain 4: Overview of Developing ML models

4.1 Building models. Considerations include:

  • Explain Choice of framework and model
  • Explain Modeling techniques given interpretability requirements
  • Explain Transfer learning
  • Explain Data augmentation
  • Explain Semi-supervised learning
  • Explain Model generalization and strategies to handle overfitting and underfitting

4.2 Training models. Considerations include:

  • Explain Ingestion of various file types into training (e.g., CSV, JSON, IMG, parquet or databases, Hadoop/Spark)
  • Explain Training a model as a job in different environments
  • Explain Hyperparameter tuning
  • Explain Tracking metrics during training
  • Explain Retraining/redeployment evaluation

4.3 Testing models. Considerations include:

  • Explain Unit tests for model training and serving
  • Explain Model performance against baselines, simpler models, and across the time dimension
  • Explain Model explainability on Vertex AI

4.4 Scaling model training and serving. Considerations include:

  • Explain Distributed training
  • Explain Scaling prediction service (e.g., Vertex AI Prediction, containerized serving)


Domain 5: Overview of Automating and orchestrating ML pipelines

5.1 Designing and implementing training pipelines. Considerations include:

  • Explain Identification of components, parameters, triggers, and compute needs (e.g., Cloud Build, Cloud Run)
  • Explain Orchestration framework (e.g., Kubeflow Pipelines/Vertex AI Pipelines, Cloud Composer/Apache Airflow)
  • Explain Hybrid or multicloud strategies
  • Explain System design with TFX components/Kubeflow DSL 

5.2 Implementing serving pipelines. Considerations include:

  • Explain Serving (online, batch, caching)
  • Explain Google Cloud serving options
  • Explain Testing for target performance
  • Explain Configuring trigger and pipeline schedules

5.3 Tracking and auditing metadata. Considerations include:

  • Explain Organizing and tracking experiments and pipeline runs
  • Explain Hooking into model and dataset versioning
  • Explain Model/dataset lineage


Domain 6: Overview of Monitoring, optimizing, and maintaining ML solutions

6.1 Monitoring and troubleshooting ML solutions. Considerations include:

  • Explain Performance and business quality of ML model predictions
  • Explain Logging strategies
  • Explain Establishing continuous evaluation metrics (e.g., evaluation of drift or bias)
  • Explain Understanding Google Cloud permissions model
  • Explain Identification of appropriate retraining policy
  • Explain Common training and serving errors (TensorFlow)
  • Explain ML model failure and resulting biases

6.2 Tuning performance of ML solutions for training and serving in production. 

  • Explain Optimization and simplification of input pipeline for training
  • Explain Simplification techniques 


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