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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #20

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…

The correct answer is C. Embed the client on the website, deploy the gateway on App Engine, deploy the database on. Option C is correct because the three-tier architecture - client → App Engine gateway → database → AI Platform Prediction - satisfies all stated constraints simultaneously. The App Engine gateway is critical for security, as it prevents direct public exposure of the model…

Submitted by asante_acc· Apr 18, 2026ML pipeline operationalization

Question

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?

Options

  • AEmbed the client on the website, and then deploy the model on AI Platform Prediction.
  • BEmbed the client on the website, deploy the gateway on App Engine, and then deploy the model
  • CEmbed the client on the website, deploy the gateway on App Engine, deploy the database on
  • DEmbed the client on the website, deploy the gateway on App Engine, deploy the database on

How the community answered

(43 responses)
  • A
    5% (2)
  • B
    7% (3)
  • C
    74% (32)
  • D
    14% (6)

Explanation

Option C is correct because the three-tier architecture - client → App Engine gateway → database → AI Platform Prediction - satisfies all stated constraints simultaneously. The App Engine gateway is critical for security, as it prevents direct public exposure of the model endpoint while managing authentication and request routing. A database layer (typically Cloud Bigtable) is required to serve precomputed navigation-context features at the speed needed to meet the 300ms@p99 latency target across thousands of banner candidates.

Why the distractors fail:

  • A skips the gateway entirely, exposing the model endpoint directly to the web client - a clear security violation.
  • B adds the gateway (fixing the security issue) but omits the database, making it impossible to efficiently retrieve navigation-context features at scale within the latency budget.
  • D likely differs from C in a detail that breaks either the latency, security, or simplicity requirement (e.g., a more complex serving infrastructure than necessary).

Memory tip: Think "Security → Speed → Simplicity." The gateway enforces security, the database enables speed (pre-fetched features beat real-time computation), and AI Platform Prediction is the managed, simplest-to-operate serving layer. If any tier is missing, one of these three pillars collapses.

Topics

#ML Serving Architecture#API Gateway#Database Integration#Low-latency ML

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