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Showing 20 results for “databricks”. More results are available below.
AI-300 · Databricks lakehouse concepts
A lakehouse workflow can separate raw ingestion from validated and business-ready datasets. Each layer should have a clear contract rather than merely a different folder name. Preserve enoug…
DBX-CTX · Databricks lakehouse concepts
A lakehouse workflow can separate raw ingestion from validated and business-ready datasets. Each layer should have a clear contract rather than merely a different folder name. Preserve enoug…
DBX-CTX · Generative AI systems
A generative model produces output based on its learned patterns and the context it receives. Retrieval can supply relevant documents, but the model may still misunderstand or invent details…
DBX-CTX · Machine-learning engineering
Data leakage occurs when training or model selection uses information that would not be available at prediction time. Split data in a way that respects time and related entities, and fit lea…
DBX-CTX · Responsible AI evaluation
Aggregate accuracy can hide poor performance for particular users or situations. Evaluate representative subgroups and realistic failure cases, and consider privacy, misuse and human oversig…
DBX-DAA · Data pipeline design
A data pipeline needs defined inputs, transformations, outputs and checkpoints. Failures can leave partial results, so decide how processing resumes without losing or duplicating records. Tr…
DBX-DAA · Data quality
A value can have the right data type and still be wrong for the business. Data quality checks should cover required values, uniqueness, valid ranges and relationships, along with freshness a…
DBX-DAA · Databricks lakehouse concepts
A lakehouse workflow can separate raw ingestion from validated and business-ready datasets. Each layer should have a clear contract rather than merely a different folder name. Preserve enoug…
DBX-DEA · Data pipeline design
A data pipeline needs defined inputs, transformations, outputs and checkpoints. Failures can leave partial results, so decide how processing resumes without losing or duplicating records. Tr…
DBX-DEA · Databricks lakehouse concepts
A lakehouse workflow can separate raw ingestion from validated and business-ready datasets. Each layer should have a clear contract rather than merely a different folder name. Preserve enoug…
DBX-DEA · SQL querying
SQL joins determine which rows are combined, filters determine which remain, and aggregation summarizes the result. Confirm the intended grain before calculating a total. Joining one parent …
DBX-DEP · Data governance
Data governance defines who is accountable for data meaning, quality, access and retention. A catalog can describe datasets and lineage, but decisions still need responsible owners. Establis…
DBX-DEP · Databricks lakehouse concepts
A lakehouse workflow can separate raw ingestion from validated and business-ready datasets. Each layer should have a clear contract rather than merely a different folder name. Preserve enoug…
DBX-DEP · Streaming data systems
Streaming systems receive events continuously, sometimes late or out of order. Event time records when something happened; processing time records when the system handled it. Windowing and l…
DBX-GAI · Databricks lakehouse concepts
A lakehouse workflow can separate raw ingestion from validated and business-ready datasets. Each layer should have a clear contract rather than merely a different folder name. Preserve enoug…
DBX-GAI · Generative AI systems
A generative model produces output based on its learned patterns and the context it receives. Retrieval can supply relevant documents, but the model may still misunderstand or invent details…
DBX-GAI · Machine-learning engineering
Data leakage occurs when training or model selection uses information that would not be available at prediction time. Split data in a way that respects time and related entities, and fit lea…
DBX-GAI · Responsible AI evaluation
Aggregate accuracy can hide poor performance for particular users or situations. Evaluate representative subgroups and realistic failure cases, and consider privacy, misuse and human oversig…
DBX-MLA · Databricks lakehouse concepts
A lakehouse workflow can separate raw ingestion from validated and business-ready datasets. Each layer should have a clear contract rather than merely a different folder name. Preserve enoug…
DBX-MLA · Generative AI systems
A generative model produces output based on its learned patterns and the context it receives. Retrieval can supply relevant documents, but the model may still misunderstand or invent details…
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