Vertex AI & GenAI: Foundations for the Modern Cloud Architect
In 2026, the PCA exam has evolved to include heavy emphasis on Generative AI (GenAI). As an architect, you're expected to build scalable, secure, and responsible AI systems using Vertex AI.
The Vertex AI Ecosystem
Vertex AI is Google's unified platform for machine learning. For the exam, you must understand how to leverage Model Garden, Generative AI Studio, and Vertex AI Search and Conversation.
Key GenAI Components for PCA
- Foundation Models: Understanding Gemini, PaLM 2 (deprecated but good for context), and Chirp. When to use multimodal vs. text-only models.
- Vector Search: Previously Matching Engine. Crucial for RAG (Retrieval-Augmented Generation) architectures to provide context to LLMs.
- Adapters and Fine-tuning: Knowing when to use prompt engineering, parameter-efficient fine-tuning (PEFT), or full fine-tuning.
- Responsible AI: Implementing safety filters and grounding to prevent hallucinations and bias.
Architecting a RAG Application
A common 2026 exam scenario involves building a customer support bot that answers questions based on private company docs.
- Store Docs: Place documents in Cloud Storage.
- Embeddings: Use Vertex AI Embeddings API to convert text to vectors.
- Indexing: Store vectors in Vertex AI Vector Search.
- Retrieval: Use a query to find the most relevant document chunks.
- Augmentation: Feed the chunks + the user query to a Gemini model for a grounded answer.
Frequently Asked Questions (FAQ)
Which model should I choose for multimodality?
The Gemini family (Gemini 1.5 Pro or Flash) is the primary recommendation for multimodal tasks involving text, images, video, and code.
What is Vertex AI Vector Search?
Formerly known as Matching Engine, it is a high-scale, low-latency vector database that allows you to perform semantic search across millions of items based on embeddings.
How do I handle model hallucinations?
By implementing Grounding with Vertex AI Search or using specific system instructions and safety filters within Vertex AI Studio to constrain the model's output.
Is GenAI useful for Mountkirk Games?
Yes, GenAI can be used for automated player support or generating real-time game content based on player behavior — check out the Mountkirk Scaling analysis.
How to optimize AI costs?
Use smaller models like Gemini Flash for simple tasks and reserve capacity for high-volume production needs — see our Cost Optimization guide.
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