The cloud landscape is evolving rapidly, and the PCA renewal exam reflects this shift. This comprehensive course, spanning 78 in-depth lessons, is meticulously designed to bridge the gap between traditional cloud infrastructure and the cutting-edge AI and data processing solutions you need to know today.
Whether you are a certified architect looking to renew your credential, or a data professional wanting to master Vertex AI and Dataflow, this course provides a deep, hands-on understanding of Google Cloud’s most powerful tools.
What You Will Learn:
1. Advanced Data Engineering with Cloud Dataflow & Apache Beam:
- Design and deploy highly scalable batch and streaming data pipelines.
- Master complex event-time processing using Windowing, Watermarks, and Triggers.
- Understand the Beam Portability Framework and cross-language transforms.
- Optimize pipeline performance with the Dataflow Shuffle and Streaming Engines.
2. E-Commerce Innovation with Vertex AI Search for Retail:
- Build revenue-driving recommendation engines (“Frequently Bought Together”, “Recommended for You”).
- Manage real-time catalog updates and user event ingestion.
- Customize the shopper experience using Serving Configurations, Boost/Bury controls, and dynamic faceting.
- Prove your AI’s ROI using Attribution Tokens and rigorous A/B testing methodologies.
3. The New Era of Generative AI & MLOps:
- Transition from traditional predictive machine learning to Generative AI workflows.
- Master the Large Language Model (LLM) ecosystem: Data Sources, Prompt Templates, Memory, Tools, and Guardrails.
- Discover, test, and tune foundation models using Vertex AI Model Garden and Generative AI Studio.
- Automate your ML pipelines to achieve a mature, production-ready MLOps environment.
4. Advanced Model Evaluation (AutoSxS):
- Overcome the unique challenges of evaluating open-ended, creative LLM outputs.
- Implement Computation-based metrics (ROUGE, BLEU, F1) for standardized benchmarking.
- Leverage Google’s Auto Side-by-Side (AutoSxS) pipeline to use an LLM-as-a-judge, generating Confidence Scores and Chain-of-Thought explanations that align with human preference.
- Mitigate model bias, prevent data contamination, and implement techniques like Dropout to prevent overfitting.
