GenAI evaluation answers a difficult question: is the application getting better in ways that matter to users? Traditional software tests can verify deterministic rules, but language-model applications also need to measure relevance, correctness, groundedness, safety, completeness, retrieval quality, tool behavior, and sometimes conversational experience. MLflow gives Databricks teams a way to connect those measurements to traces, datasets, scorers, application versions, and human feedback. The current Generative AI Engineer exam includes evaluation and monitoring as a distinct section and expects engineers to use MLflow scoring and tracing, select monitoring metrics, understand…

Vector search is useful when an application needs to retrieve records by semantic similarity rather than by exact keywords alone. On Databricks, the capability is now branded Databricks AI Search, while the current Generative AI Engineer certification guide still uses the term Vector Search in several objectives. The underlying engineering questions remain the same: what data becomes an index, how embeddings are created, how the index stays current, which metadata can filter results, and how retrieval quality is measured against real questions. The current Generative AI Engineer exam expects candidates…

An LLM chain makes a generative AI application easier to reason about by turning one large prompt-driven task into a sequence of explicit transformations. A chain might validate input, retrieve supporting context, build a prompt, call a model, parse a structured response, apply business rules, and return a final result. The current Generative AI Engineer exam still expects candidates to understand LLM chains, including selecting chain components, coding simple chains, using pre- and post-processing, retrieval, registration, and deployment. The practical goal inside Databricks GenAI is to make each stage testable…

An agent workflow is a controlled sequence in which a language model can gather context, choose or invoke tools, preserve state, and produce an answer or action. On Databricks, that workflow can combine governed data, AI Search retrieval, model endpoints, Unity Catalog tools, managed or external MCP servers, MLflow tracing and evaluation, and a user-facing application. The difficult part is not connecting every available feature. It is deciding which steps the agent is allowed to take and what evidence proves that each step behaved correctly. The current Generative AI Engineer…

Generative AI on Databricks is no longer just a model-calling exercise. A production application has to prepare and govern source data, choose an appropriate model, retrieve context, orchestrate tools or agent steps, serve the application behind a reliable interface, evaluate quality, and monitor live behavior. Databricks brings those concerns onto one platform through Unity Catalog, AI Search, Model Serving, Databricks Apps and agent tooling, plus MLflow for tracing, evaluation, versioning, and production observability. The current Generative AI Engineer Associate exam reflects that broader lifecycle. The March 18, 2026 exam guide…

Databricks Lakehouse Engineering is the production discipline of turning raw files, streams, operational changes, and analytical requirements into governed data products that can be trusted, refreshed, debugged, and evolved. The work spans ingestion, Spark transformations, Delta Lake tables, declarative pipelines, workflow orchestration, CI/CD, data quality, performance, and governance. Treating those as separate features misses the reason a lakehouse platform is useful: each layer should reinforce the reliability of the next. The practical center of the platform is data engineering rather than one storage format or one runtime. Databricks certifications include…

  A Delta table can look deceptively simple from the outside: data files sit in cloud object storage and Spark reads them as a table. The feature that changes the behavior of those files is the transaction log. It records the ordered sequence of committed changes that defines which data files belong to each valid table version. The current Databricks Certified Data Engineer Associate exam covers ingestion, transformation, modeling, optimization, governance, and the Databricks platform. Delta Lake sits underneath many of those tasks because reliable pipelines need more than a…

  Bronze, silver, and gold are easy labels to memorize. The value of medallion architecture comes from something deeper: each layer represents a different level of trust, structure, and intended use. The pattern is useful only when those boundaries reduce ambiguity for engineers and downstream consumers. The current Databricks Certified Data Engineer Associate exam covers data ingestion, transformation, modeling, optimization, and governance. Medallion architecture connects those tasks because it gives a pipeline a clear progression from source-faithful ingestion to validated data and finally to business-ready outputs. Databricks describes the pattern…

  Developers who come to PySpark from ordinary Python often try to reason about a DataFrame as if it were a local collection of rows. That mental model creates inefficient code and confusing performance behavior. A Spark DataFrame is better understood as a distributed, declarative computation plan over structured data. The current Databricks Certified Data Engineer Associate exam includes ETL work in SQL and PySpark. The most important conceptual shift is not memorizing method names. It is understanding that transformations describe what should happen, Spark builds a plan, and execution…

  Partitioning has long been taught as a standard performance technique for large analytical tables. On current Databricks platforms, that advice needs an important update. Databricks now recommends liquid clustering for managed tables and states that most tables under 100 TB do not need traditional partitioning. The current Databricks Certified Data Engineer Associate exam still requires candidates to understand troubleshooting and optimization. The durable skill is therefore not “partition every large table.” It is learning how data layout affects scanning, pruning, file sizes, maintenance, and query performance—and knowing which layout…

  File ingestion looks simple when there are ten files in a folder: list the directory, read everything, and write the result. The design changes when files keep arriving for months or years. A production pipeline needs to discover only new input, remember what it has processed, survive restarts, handle schema change, and scale without repeatedly scanning an ever-growing history. Databricks Auto Loader is built for that incremental problem. It exposes a Structured Streaming source named cloudFiles that discovers new files in cloud object storage and processes them as they…

  Data access becomes difficult to govern when every workspace, storage location, table, and team invents its own permissions. Unity Catalog addresses that problem by providing a common governance layer across Databricks data and AI assets. It centralizes the object model, access control, discovery, lineage, auditing, and other governance capabilities instead of leaving each workload to build them independently. Governance and security account for a meaningful part of the current Databricks Certified Data Engineer Associate exam. The useful mental model is broader than memorizing GRANT statements. Unity Catalog is a…

  Reliable data pipelines are not defined by how quickly a notebook can turn raw files into a dashboard. They are defined by whether the same pipeline can keep producing trustworthy results when source systems change, late records arrive, a task fails halfway through, volumes grow, and several downstream teams begin depending on the output. That is why the bronze-silver-gold pattern is useful: it gives each stage of the pipeline a distinct responsibility instead of allowing ingestion, cleanup, business logic, and reporting to blur together. The current Databricks Certified Data…

  Retrieval-augmented generation is easy to demo and surprisingly difficult to evaluate. A response can sound fluent while citing weak evidence, retrieve the right passage but answer incompletely, or be factually correct for reasons unrelated to the supplied context. The current Databricks Generative AI Engineer Associate exam and Generative AI Engineer Associate certification treat retrieval evaluation, agent scoring, SME feedback, tracing, and monitoring as core engineering work because “looks good to me” cannot support reliable iteration. A useful evaluation system separates the stages that can fail. Retrieval asks whether the…

  Embedding models are often chosen with a single line of configuration, but that choice shapes what a semantic search system can retrieve. Context length, vector dimension, language coverage, domain fit, normalization, latency, and cost all influence the result. The current Databricks Generative AI Engineer Associate exam and Generative AI Engineer Associate certification explicitly connect embedding-model selection to source documents, expected queries, optimization strategy, vector search, and retrieval evaluation. The most important lesson is that a larger or newer embedding model is not automatically better for a particular RAG system….