What is the fastest, lowest-cost way to detect animals in user photos?

Why the Answer Is Correct

Option A uses the Cloud Vision API's object localization to identify all objects in the image, then filters results against a list of animals. This requires no custom training, no manual labeling, and no model deployment. The Cloud Vision API is a fully managed service, which minimizes both development effort and ongoing operational cost. For 20 photos per day, the pay-per-use pricing is highly economical and the API returns results in near real time.

The comment by the user with 10 likes states that "going with the google hosted service is most times the most likely choice to be correct," which aligns with the exam's emphasis on time and cost minimization. Another comment explicitly mentions that the small image subset makes the Cloud Vision API the best option.

Why the Other Options Are Wrong

Option B requires downloading a TensorFlow model and deploying it to a Vertex AI endpoint. Although feasible, this involves significant engineering effort for deployment, scaling, and monitoring, which contradicts the priority on minimizing development time. It also does not leverage a prebuilt service designed for generic object detection.

Option C requires manually labeling previously submitted images with bounding boxes and training an AutoML object detection model. This is overkill for a simple binary presence/absence question and requires a large, well-labeled dataset to achieve reliable accuracy—more time and cost than necessary.

Option D is the most common distractor because it uses binary image classification with AutoML, which is conceptually simpler than object detection. However, it still requires manual labeling, training, and endpoint deployment. As one comment notes, "Cloud Vision is overkill for a binary classification and it is very expensive," but that comment overlooks the fact that using AutoML also carries training and infrastructure costs. For a low-volume use case, the Cloud Vision API's per-request cost is very low and requires zero upfront ML expertise.

Community Comment Notes

Most community members voted for A, and the top comment points out that while B would also work, Google-hosted services are usually the safest exam choice. A few commenters chose D, but their reasoning typically underestimates the development overhead of AutoML. The commenter for B (2 likes) did not provide reasoning, and the A-voters emphasize the priority on "minimizing time and cost" as the deciding factor. The exam's suggested answer and majority consensus both verify A.