Google Cloud Professional Machine Learning Engineer — Question 10

You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an image contains your company's product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that new data can be fed into your ML models. You also want to use AI Platform's continuous evaluation service to ensure that the models have high accuracy on your test dataset. What should you do?

Answer options

• A. Keep the original test dataset unchanged even if newer products are incorporated into retraining.
• B. Extend your test dataset with images of the newer products when they are introduced to retraining.
• C. Replace your test dataset with images of the newer products when they are introduced to retraining.
• D. Update your test dataset with images of the newer products when your evaluation metrics drop below a pre-decided threshold.

Correct answer: B

Explanation

The correct answer is B because extending your test dataset with images of the newer products ensures that the evaluation remains relevant and accurately reflects the model's performance on the current offerings. Option A is incorrect since it does not account for new products, while option C would lead to a dataset that lacks historical context. Option D delays updates until performance degrades, which is not proactive in maintaining model accuracy.