Exam Professional Machine Learning Engineer topic 1 question 105 discussion

it seens A (not sure) - https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-tensorflow

The hint is "You built a TensorFlow model that predicts whether players will make in-app purchases of more than $10 in the next two weeks." this means that for this particular use case prediction is not realtime and batch is in fact suitable. Furthermore, BQML allows you to load tensorflow model for serving. This makes BQML the best choice for cost consideration.

it's an online gaming service- you in to stream data in realtime, not batch processing

BigQuery ML is a useful tool for certain machine learning tasks, it's not the right tool for serving a complex TensorFlow model and integrating it into a game's user experience adaptation system. Vertex AI Prediction is a better choice for this scenario due to its superior support for serving complex models, its optimized infrastructure for serving, and its ease of management.

Why D is the Best Choice: It provides real-time predictions, which is crucial for a good user experience in an MMO setting. It leverages Google Cloud’s managed services (Dataflow, Pub/Sub, Cloud SQL) to reduce operational overhead and simplify management. It allows you to centrally manage your model and easily update it without requiring changes to client applications. It optimizes cost by using a pay-as-you-go, autoscaling service rather than running large-scale batch jobs or deploying models on individual user devices. Option A: Import model into BigQuery ML and do batch predictions. User Experience: Batch predictions are not real-time. This approach introduces a significant delay between data ingestion and predictions. Not ideal if you need to adapt the user experience quickly based on recent behavior.

Make predictions after every in-app purchase it it not necessary -> A

Embedding the model in a streaming Dataflow pipeline allows low latency predictions on real-time events published to Pub/Sub. This provides a responsive user experience. Dataflow provides a managed service to scale predictions and integrate with Pub/Sub, without having to manage servers. Streaming predictions only when events occur optimizes cost compared to bulk or client-side prediction. Pushing results to Cloud SQL provides a managed database for persistence. In contrast, options A and B use inefficient batch predictions. Option C increases mobile app size and cost.

D could be correct

These were my reasonings to choose D as best option: B -> Vertex AI would not minimize cost C -> Would not optimize user experience (this may lead to slow running of the game (lag)?) A- > Would not optimize ease of management / automatization D -> Best choice?

For "used to adapt each user's game experience" points out to non-batch, hence excludes A & B, and embedding the model in the mobile app would not necessarily "optimize cost". Plus, the classical streaming solution builds on Dataflow along with Pub/Sub and BigQuery, embedding ML in Dataflow is low-code https://cloud.google.com/blog/products/data-analytics/latest-dataflow-innovations-for-real-time-streaming-and-aiml and apparently a modified version of the question points to the same direction https://mikaelahonen.com/en/data/gcp-mle-exam-questions/

Yeah its A

Answer C