Harnessing Google Vertex AI Practice Exam
Harnessing Google Vertex AI Practice Exam
About Harnessing Google Vertex AI Exam
The Google Vertex AI Certification Exam evaluates an individual's expertise in leveraging Vertex AI to build, deploy, and manage machine learning (ML) models efficiently within Google Cloud. This certification is designed for professionals who work with ML pipelines, MLOps, and cloud-based AI solutions, validating their ability to streamline model development and deployment using Vertex AI’s powerful tools and automation capabilities.
Skills Required
Candidates taking the exam should have a strong understanding of the following:
- Understanding Vertex AI’s capabilities and its role in the Google Cloud ecosystem.
- Navigating the Vertex AI dashboard and its key components.
- Importing, transforming, and preparing structured and unstructured datasets.
- Utilizing AutoML for automated feature engineering.
- Training custom models using Vertex AI Notebooks and AutoML.
- Deploying models using Vertex AI’s managed endpoints.
- Leveraging hyperparameter tuning to optimize model performance.
- Automating ML workflows with Vertex AI Pipelines.
- Implementing CI/CD practices for ML models.
- Monitoring and managing model drift and performance.
- Managing compute resources, including TPU and GPU optimization.
- Implementing security best practices, including IAM policies for AI workloads.
- Assessing model performance using built-in evaluation tools.
- Understanding explainability techniques in Vertex AI to interpret model predictions.
Who should take the Exam?
This certification is ideal for:
- Machine Learning Engineers aiming to validate their expertise in Google Cloud’s AI offerings.
- Data Scientists looking to enhance their ML workflows and streamline model deployment.
- Cloud AI Practitioners who work with Google Cloud and want to integrate AI/ML capabilities into business solutions.
- AI/ML Developers seeking to automate and optimize their machine learning models using Google Vertex AI.
Course Outline
The Harnessing Google Vertex AI Exam covers the following topics -
Domain 1. Course Overview and Prerequisites
- Introduction to the Course and Learning Objectives
- Required Skills and Prerequisites for Effective Learning
Domain 2. Setting Up the Development Environment & Google Cloud Platform
- Overview of Development Environment Configuration and API Cost Considerations
- Configuring Google Cloud for Vertex AI Integration
- Hands-on: Generating Sentence Embeddings Using Vertex AI for Initial Testing
Domain 3. Deep Dive into Vertex AI Text Embedding API and Embeddings Fundamentals
- Understanding Vertex AI and Its Key Capabilities
- Optional: Foundational Concepts of Embeddings
- Applications of Embeddings in Generative AI and Large Language Models (LLMs)
- Exploring the Embeddings API: Text-Based vs. Multimodal Embeddings
- Key Use Cases, Task Types, and Advantages of Embeddings
- Visual Representation: Multimodal Embeddings Architecture
- Hands-on: Understanding Embedding Dimensions and Length
- Hands-on: Performing Cosine Similarity Search on Various Sentences
- Hands-on: Visualizing Embedding Representations
Domain 4. Generating Text with Vertex AI Text Embedding API
- Implementing Text Generation Using the Bison Model
- Hands-on: Text Classification Use Case for Real-World Applications
- Hands-on: Extracting Structured Information into Tables and JSON Formats
- Hands-on: Controlling Model Temperature for Output Variability
- Hands-on: Adjusting Top-K and Top-P for Optimized Text Generation
- Hands-on: Summarizing and Extracting Key Information from Transcripts
Domain 5. Practical Applications and Real-World Use Cases of Embeddings
- Visualizing Stack Overflow Q&A Clusters in a 2D Space
- Hands-on: Developing a Retrieval-Augmented Generation (RAG) System with Stack Overflow Data
- Scaling Search Performance Using Approximate Nearest Neighbor (ANN) Techniques: HNSW vs. Cosine Similarity
