A global retailer is building a pipeline to process photographs that shoppers upload with product reviews. For each image the team must do two things: detect inappropriate or unsafe visual content before the photo is published, and read any free-form text printed on packaging visible in the photo so it can be indexed for search. There is no fixed form layout, the team has minimal machine-learning expertise, and they want to lean on managed Google Cloud AI APIs rather than train a custom model. Which TWO Google Cloud capabilities together best satisfy these two requirements? (Select TWO.)
- AUse the Cloud Vision API Safe Search detection feature to flag adult, violent, or otherwise unsafe imagery before each uploaded photo is published. Correct
- BUse Document AI with the invoice parser processor to extract the packaging text, since Document AI is the managed service for reading text from images.
- CUse the Cloud Vision API text detection feature to extract whatever free-form text is printed on the packaging in each photo as plain strings for indexing. Correct
- DUse the Cloud Translate API to convert the uploaded images into searchable text so the packaging wording can be indexed across languages.
- EDeploy a custom image-classification model trained in Model Garden specifically to label each photo as safe or unsafe and to transcribe its text.
Why A is correct: Vision API Safe Search returns likelihood scores for unsafe categories such as adult and violence, which is the purpose-built managed capability for screening user-uploaded imagery before publication.
Why B is wrong: Document AI reads text, but its parsers target structured documents with known layouts such as invoices, so it is the wrong fit for arbitrary free-form text on packaging in an unstructured photo.
Why C is correct: Vision API text detection performs OCR on arbitrary images with no fixed layout, returning the printed text as plain strings, which matches the need to read packaging text for search indexing.
Why D is wrong: Translate converts text between languages and feels relevant for a global retailer, but it accepts text not images, so it cannot read or extract the packaging wording from a photograph.
Why E is wrong: A bespoke trained model could in principle do both jobs, but it contradicts the team's minimal-ML, managed-API preference when ready-made Vision API features already cover both needs.