Google Cloud Professional Machine Learning Engineer — Question 105
You work for an online travel agency that also sells advertising placements on its website to other companies. You have been asked to predict the most relevant web banner that a user should see next. Security is important to your company. The model latency requirements are 300ms@p99, the inventory is thousands of web banners, and your exploratory analysis has shown that navigation context is a good predictor. You want to Implement the simplest solution. How should you configure the prediction pipeline?
Answer options
- A. Embed the client on the website, and then deploy the model on AI Platform Prediction.
- B. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Firestore for writing and for reading the user’s navigation context, and then deploy the model on AI Platform Prediction.
- C. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Cloud Bigtable for writing and for reading the user’s navigation context, and then deploy the model on AI Platform Prediction.
- D. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Memorystore for writing and for reading the user’s navigation context, and then deploy the model on Google Kubernetes Engine.
Correct answer: C
Explanation
Option C is correct because Cloud Bigtable is well-suited for high-throughput and low-latency access to large amounts of data, which fits the requirement for reading the user's navigation context. The other options either use less optimal databases for the given use case or involve unnecessary complexity, which goes against the goal of implementing the simplest solution.