Author Archives: Allen Rodriguez

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, […]

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 […]

When preparing for Databricks Data Engineer Associate, Unity Catalog column masks let Databricks return a transformed value instead of the stored value when a protected column is queried. The transformation is implemented through a user-defined function that can evaluate the current user, group membership, tags, or other policy logic and then decide whether to reveal, […]

For readers studying Databricks Data Engineer Associate, Unity Catalog row filters let Databricks apply a policy function to each row and return only the rows a user is authorized to see. The stored table can remain shared while the visible row set changes according to identity, group membership, tags, or other policy inputs. This is […]

Databricks Clean Rooms are designed for organizations that need to analyze or combine data across organizational boundaries without giving each participant unrestricted access to the other participants’ raw datasets. The clean room creates a controlled collaboration environment in which approved data, notebooks, and computations can be made available under rules that all parties understand. The […]

Databricks administrators have long used cluster policies to put guardrails around how users create Spark clusters. In the current product interface and documentation, the preferred name is compute policies. The older “cluster policy” label still appears in API and CLI surfaces, so both terms matter operationally: the concept has evolved, but existing automation does not […]

When preparing for Databricks Data Engineer Professional, optimized writes address a simple but expensive problem in distributed data engineering: a parallel job can produce far more files than downstream readers want. Every task may write into multiple partitions, and a large shuffle can leave a table with thousands of small objects. The data is correct, […]

VACUUM is one of the most consequential maintenance operations in Delta Lake because it turns logical history into physical deletion. A Delta table can stop referencing old data files long before those files disappear from object storage. That delay is intentional: it supports time travel, rollback, long-running readers, and recovery from recent mistakes. VACUUM removes […]

For Databricks Data Engineer Professional candidates, Databricks renamed Delta Sharing to OpenSharing in June 2026 following the release of the open-source OpenSharing standard. The queue topic “Delta Sharing Governance” still reflects terminology that many teams, integrations, and runbooks use, so the useful way to handle the transition is to connect the names rather than pretend […]

When preparing for Databricks GenAI Engineer Associate, the Databricks feature historically called Mosaic AI Agent Evaluation now sits inside the broader MLflow 3 generative-AI workflow. That change is more than branding. Current Databricks guidance exposes evaluation through the mlflow.genai namespace, combines offline testing with tracing and production monitoring, and treats evaluation datasets, scorers, human feedback, […]

For Databricks Data Engineer Professional candidates, deletion vectors change the cost model of row-level modification in Delta Lake. Without them, changing or deleting even a small number of rows can require rewriting the Parquet files that contain those rows. With deletion vectors enabled, Databricks can record which rows are logically removed or replaced in metadata […]

A Databricks compute policy becomes valuable when it expresses a workload decision that the organization has already made. The policy should answer questions such as: which runtime families are supportable, which access modes are allowed, how large may a cluster become, which tags are mandatory, which libraries are trusted, and how much hourly cost can […]

For Google Cloud Generative AI Leader candidates, Google’s managed agent hosting service has changed names. What many teams still know as Vertex AI Agent Engine is now documented as Agent Runtime on Gemini Enterprise Agent Platform. The rename matters because architecture diagrams, Terraform modules, SDK examples, console labels, and older runbooks can use different generations […]

Google Cloud’s current agent platform naming changed materially in April 2026. Vertex AI became part of Gemini Enterprise Agent Platform, Agent Builder moved into that platform, Agent Engine became Agent Runtime, Agent Builder Sessions became Agent Platform Sessions, and Memory Bank became Agent Platform Memory Bank. Agent Builder in Generative AI Leader should be evaluated […]