Category Archives: Technology

For readers studying Databricks GenAI Engineer Associate, the phrase “Mosaic AI Agent Framework” still appears in older Databricks tutorials and architecture discussions, but the current agent platform is broader and more flexible. Databricks now emphasizes building agents with standard frameworks such as OpenAI Agents SDK, LangGraph, LangChain, LlamaIndex, or custom Python, then integrating those agents […]

For readers studying IAPP AIGP, AI procurement fails when the contract describes the commercial service but leaves the operating risks implicit. A provider may process sensitive prompts, change a model without notice, rely on subcontractors, retain customer content, use telemetry to improve its products, or discontinue a capability that a critical workflow depends on. Governance […]

For Databricks GenAI Engineer Associate candidates, Mosaic AI Model Serving remains an important Databricks inference surface, but the surrounding platform has evolved. Current Databricks guidance distinguishes custom model serving, Foundation Model APIs, external models, and newer governed model-access patterns such as Unity Gateway. Teams should therefore start with the workload they need to serve rather […]

For IAPP AIGP candidates, AI vendor due diligence is the investigation that should happen before an organization makes a supplier part of a consequential workflow. The objective is not to prove that a provider has zero risk. It is to understand the service, the data path, the supplier’s controls, the dependencies beneath it, and the […]

When preparing for Databricks GenAI Engineer Associate, Databricks renamed Vector Search to Databricks AI Search in 2026. The queued term “Mosaic AI Vector Search” still describes the technology many teams built against, but current documentation uses AI Search for the managed retrieval service. The rename also reflects a broader product direction: the service is not […]

When preparing for IAPP AIGP, an algorithmic impact assessment is a structured way to examine what an automated or AI-enabled system is intended to do, who may be affected, what harms or benefits are plausible, which controls exist, and whether the organization should proceed. The exact legal form differs by jurisdiction and sector, so an […]

For readers studying Databricks GenAI Engineer Associate, real-time AI systems often need information that changes faster than a model can be retrained. A fraud model needs the customer’s latest transaction count, a recommendation service needs recent engagement, and a personalization layer may need the user’s current account state. An online feature store exists to make […]

For readers studying IAPP AIGP, ISO/IEC 42001:2023 is an international standard for artificial intelligence management systems. ISO describes it as the first global standard specifying how an organization can establish, implement, maintain, and continually improve an AI management system, or AIMS. The standard is designed for organizations that develop, provide, or use AI systems, so […]

For Databricks GenAI Engineer Associate candidates, prompts are executable behavior. Changing a system instruction, few-shot example, output schema, or tool description can alter an AI application as significantly as changing code. The MLflow Prompt Registry gives Databricks teams a managed way to treat prompts as versioned artifacts rather than strings copied across notebooks and deployment […]

When preparing for Databricks GenAI Engineer Associate, retrieval-augmented generation often fails before the vector index has a chance to help. A user asks a conversational, ambiguous, multi-part, or context-dependent question, while the retrieval system expects a concise search query that exposes the entities and concepts most likely to appear in source documents. Query rewriting is […]

For readers studying Databricks GenAI Engineer Associate, retrieval systems usually produce more candidates than an application should place into a model prompt. The first-stage search is optimized to find plausible matches quickly; reranking applies a more expensive relevance judgment to a smaller candidate set and changes their order before context assembly. Databricks AI Search currently […]

For IAPP AIGP candidates, AI systems change even when the business process around them appears stable. A model endpoint is upgraded, a system prompt changes, a retrieval index is rebuilt, a safety filter is tuned, a vendor modifies an API, or fresh data shifts the population reaching the system. Conventional software change control remains necessary, […]

When preparing for IAPP AIGP, training and fine-tuning data can carry copyright, privacy, contractual, confidentiality, database, publicity, and sector-specific obligations. There is no single “AI training right” that resolves all of them. The lawful basis for using a public web page, a licensed media archive, customer support transcripts, employee documents, or synthetic data can differ […]

For readers studying Google Cloud Generative AI Leader, Vertex AI Feature Store has changed substantially from the first generation of the product. In Google’s 2026 platform naming, the capability is documented as Feature Store on Gemini Enterprise Agent Platform. More importantly, the current architecture centers on features prepared in BigQuery and registered through a metadata […]

For Databricks Data Engineer Associate candidates, streaming tables in Databricks provide a declarative way to maintain incrementally updated datasets from continuously arriving data. In the current Lakeflow Spark Declarative Pipelines model, a developer defines the target table and its transformation while the pipeline manages execution, dependencies, checkpointing, retries, and incremental progress. The result is different […]