AI-300 — frequently asked questions

URI_FOLDER indicates a directory containing multiple files, suitable for datasets like image classification folders. FILE indicates a single specific file location.

SDK v2 uses 'entities' for standardized definitions of inputs, outputs, and assets. 'data' is associated with the older SDK v1 Dataset abstraction.

The dashboard focuses on interpretability and fairness. Causal analysis is a separate, more complex statistical field not included in the standard dashboard components.

Explanation ranks feature importance based on current data, while Counterfactuals simulates hypothetical changes to inputs to predict different outcomes.

Models are outputs, not workflows. Components define the reusable process logic that produces those outputs.

Environments define software dependencies, while Components wrap code, environments, and I/O into a reusable, versioned task.

FAILED means the run ran into an error during execution. KILLED means the run was stopped manually or by the system before completion.

No. Cancelled or aborted runs have the status KILLED. Only runs that errored out have the status FAILED.

A Data Asset registers the raw local files within the Foundry workspace. The Vector Index operates on these registered assets to chunk and embed them; without the asset, there is no source data to index.

No. The Vector Index is a component that resides within an Azure AI Search resource (the Search Resource). You must provision the search capability first before defining how data is indexed within it.

Logs provide the actual retrieved context needed by evaluators to score relevance and groundedness. Without logs, there is no data to measure.

No. Vector embeddings are generated based on chunks. Changing the chunking strategy requires rebuilding the index so the new segments are embedded correctly.

User feedback is unstructured text or ratings that vary by individual interpretation. Measurable evaluation requires consistent, algorithmic scoring of attributes like Groundedness against a ground truth dataset.

Use automated evaluation metrics such as Groundedness (support from source), Relevance (alignment with query), and Faithfulness to quantitatively assess RAG performance changes.

Factory methods create new definitions from paths. Since the asset is already registered, you must load the existing asset reference instead.

Yes, mltable.load() works with registered assets regardless of the underlying file format, as long as the asset is properly defined in the workspace.