Google GCP-PMLE (Professional Machine Learning Engineer) Certification Exam Syllabus

GCP-PMLE Dumps Questions, GCP-PMLE PDF, Professional Machine Learning Engineer Exam Questions PDF, Google GCP-PMLE Dumps Free, Professional Machine Learning Engineer Official Cert Guide PDFThe Google GCP-PMLE exam preparation guide is designed to provide candidates with necessary information about the Professional Machine Learning Engineer exam. It includes exam summary, sample questions, practice test, objectives and ways to interpret the exam objectives to enable candidates to assess the types of questions-answers that may be asked during the Google Cloud Platform - Professional Machine Learning Engineer (GCP-PMLE) exam.

It is recommended for all the candidates to refer the GCP-PMLE objectives and sample questions provided in this preparation guide. The Google Professional Machine Learning Engineer certification is mainly targeted to the candidates who want to build their career in Cloud domain and demonstrate their expertise. We suggest you to use practice exam listed in this cert guide to get used to with exam environment and identify the knowledge areas where you need more work prior to taking the actual Google Professional Machine Learning Engineer exam.

Google GCP-PMLE Exam Summary:

Exam Name
Google Professional Machine Learning Engineer
Exam Code GCP-PMLE
Exam Price $200 USD
Duration 120 minutes
Number of Questions 60
Passing Score Pass / Fail (Approx 70%)
Recommended Training / Books Google Cloud training
Google Cloud documentation
Google Cloud solutions
Schedule Exam PEARSON VUE
Sample Questions Google GCP-PMLE Sample Questions
Recommended Practice Google Cloud Platform - Professional Machine Learning Engineer (GCP-PMLE) Practice Test

Google Professional Machine Learning Engineer Syllabus:

Section Objectives

Framing ML problems

Translating business challenges into ML use cases. Considerations include: - Choosing the best solution (ML vs. non-ML, custom vs. pre-packaged [e.g., AutoML, Vision API]) based on the business requirements
- Defining how the model output should be used to solve the business problem
- Deciding how incorrect results should be handled
- Identifying data sources (available vs. ideal)
Defining ML problems. Considerations include: - Problem type (e.g., classification, regression, clustering)
- Outcome of model predictions
- Input (features) and predicted output format
Defining business success criteria. Considerations include: - Alignment of ML success metrics to the business problem
- Key results
- Determining when a model is deemed unsuccessful
Identifying risks to feasibility of ML solutions. Considerations include: - Assessing and communicating business impact
- Assessing ML solution readiness
- Assessing data readiness and potential limitations
- Aligning with Google's Responsible AI practices (e.g., different biases)

Architecting ML solutions

Designing reliable, scalable, and highly available ML solutions. Considerations include: - Choosing appropriate ML services for the use case (e.g., Cloud Build, Kubeflow)
- Component types (e.g., data collection, data management)
- Exploration/analysis
- Feature engineering
- Logging/management
- Automation
- Orchestration
- Monitoring
- Serving
Choosing appropriate Google Cloud hardware components. Considerations include: - Evaluation of compute and accelerator options (e.g., CPU, GPU, TPU, edge devices)
Designing architecture that complies with security concerns across sectors/industries. Considerations include: - Building secure ML systems (e.g., protecting against unintentional exploitation of data/model, hacking)
- Privacy implications of data usage and/or collection (e.g., handling sensitive data such as Personally Identifiable Information [PII] and Protected Health Information [PHI])

Designing data preparation and processing systems

Exploring data (EDA). Considerations include: - Visualization
- Statistical fundamentals at scale
- Evaluation of data quality and feasibility
- Establishing data constraints (e.g., TFDV)
Building data pipelines. Considerations include: - Organizing and optimizing training datasets
- Data validation
- Handling missing data
- Handling outliers
- Data leakage
Creating input features (feature engineering). Considerations include: - Ensuring consistent data pre-processing between training and serving
- Encoding structured data types
- Feature selection
- Class imbalance
- Feature crosses
- Transformations (TensorFlow Transform)

Developing ML models

Building models. Considerations include: - Choice of framework and model
- Modeling techniques given interpretability requirements
- Transfer learning
- Data augmentation
- Semi-supervised learning
- Model generalization and strategies to handle overfitting and underfitting
Training models. Considerations include: - Ingestion of various file types into training (e.g., CSV, JSON, IMG, parquet or databases, Hadoop/Spark)
- Training a model as a job in different environments
- Hyperparameter tuning
- Tracking metrics during training
- Retraining/redeployment evaluation
Testing models. Considerations include: - Unit tests for model training and serving
- Model performance against baselines, simpler models, and across the time dimension
- Model explainability on AI Platform
Scaling model training and serving. Considerations include: - Distributed training
- Scaling prediction service (e.g., AI Platform Prediction, containerized serving)

Automating and orchestrating ML pipelines

Designing and implementing training pipelines. Considerations include: - Identification of components, parameters, triggers, and compute needs (e.g., Cloud Build, Cloud Run)
- Orchestration framework (e.g., Kubeflow Pipelines/AI Platform Pipelines, Cloud Composer/Apache Airflow)
- Hybrid or multicloud strategies
- System design with TFX components/Kubeflow DSL
Implementing serving pipelines. Considerations include: - Serving (online, batch, caching)
- Google Cloud serving options
- Testing for target performance
- Configuring trigger and pipeline schedules
Tracking and auditing metadata. Considerations include: - Organizing and tracking experiments and pipeline runs
- Hooking into model and dataset versioning
- Model/dataset lineage

Monitoring, optimizing, and maintaining ML solutions

Monitoring and troubleshooting ML solutions. Considerations include: - Performance and business quality of ML model predictions
- Logging strategies
- Establishing continuous evaluation metrics (e.g., evaluation of drift or bias)
- Understanding Google Cloud permissions model
- Identification of appropriate retraining policy
- Common training and serving errors (TensorFlow)
- ML model failure and resulting biases
Tuning performance of ML solutions for training and serving in production. Considerations include: - Optimization and simplification of input pipeline for training
- Simplification techniques
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