An enterprise AI platform team needs to implement a secure software supply chain for their AI models. The team wants to ensure that model artifacts (trained model files, ONNX exports) have not been tampered with between training and deployment. Which Azure capability supports model artifact integrity verification?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — c is correct because when AI models are packaged in container images (as is common for inference deployments), Azure Container Registry with Docker Content Trust (DCT) using Notary V1/V2 enables cryptographic signing of container images. Verifying SHA256 digests at deployment time ensures the model container has not been tampered with between the registry and deployment.
Full explanation below image
Full Explanation
C is correct because when AI models are packaged in container images (as is common for inference deployments), Azure Container Registry with Docker Content Trust (DCT) using Notary V1/V2 enables cryptographic signing of container images. Verifying SHA256 digests at deployment time ensures the model container has not been tampered with between the registry and deployment. A is incorrect because checksum comparison in Blob Storage can detect changes but does not provide cryptographic signing with a trusted key; a compromised pipeline could also update the stored checksum. B is incorrect because Azure Machine Learning Model Registry does not currently support code signing of model artifacts via Key Vault certificates as a built-in feature. D is incorrect because manual approval gates provide a process control but not cryptographic integrity verification of the model artifact.