Exam Professional Machine Learning Engineer topic 1 question 49 discussion

Security => not A. B: doesn't handle processing with banner inventory. D: deployment on GKE is less simple than on AI Platform. Besides, MemoryStore is in-memory while banners are stored persistently. Ans: C

ANS: C GAE + IAP https://medium.com/google-cloud/secure-cloud-run-cloud-functions-and-app-engine-with-api-key-73c57bededd1 Bigtable at low latency https://cloud.google.com/bigtable#section-2

B - right answer

They affirm that navigation context is a good predictor for your model. Therefore you need to be able to perform the prediction and write the new context (if you get more data you will get a better model) and read (to use it for your prediction). On one hand, BigQuery is a OLAP method so for writings and readings could take it around 2 seconds. On the other hand, BigTable is a OLTP method and can make writings and readings in about 9 milliseconds Conclusion: As one of the requerements is that the latency requirements have to be below 300ms your only choice is using BigTable https://galvarado.com.mx/post/comparaci%C3%B3n-de-bases-de-datos-en-google-cloud-datastore-vs-bigtable-vs-cloud-sql-vs-spanner-vs-bigquery/

C) Big Table for low latency

Was torn between B and C, but decided for B, because the question states how we should configure the PREDICTION pipeline! Since the exploratory analysis already identified navigation context as good predictor, the focus should be on the prediction model itself.

agree with Paul_Dirac

look at Q80

I was torn between B and C. But I really don't see the need for a DB

Embed the client on the website, deploy the gateway on App Engine, and then deploy the model on AI Platform Prediction.

secuirity (gateway) + Simplest(ai, not DB)

Bigtable is recommended for storage in the case scenario.

B is also a possible solution, but it does not include a database for storing and retrieving the user's navigation context. This means that every time a user visits a page, the gateway would need to query the website to retrieve the navigation context, which could be slow and inefficient. By using Cloud Bigtable to store the navigation context, the gateway can quickly retrieve the context from the database and pass it to the model for prediction. This makes the overall prediction pipeline more efficient and scalable. Therefore, C is a better option compared to B.

B is correct, C introduces computational overhead, unnecessarily increasing serving latency.

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 https://cloud.google.com/architecture/minimizing-predictive-serving-latency-in-machine-learning#choosing_a_nosql_database Typical use cases for Bigtable are: * Ad prediction that leverages dynamically aggregated values over all ad requests and historical data.

Bigtable is a massively scalable NoSQL database service engineered for high throughput and for low-latency workloads. It can handle petabytes of data, with millions of reads and writes per second at a latency that's on the order of milliseconds. Typical use cases for Bigtable are: Fraud detection that leverages dynamically aggregated values. Applications in Fintech and Adtech are usually subject to heavy reads and writes. Ad prediction that leverages dynamically aggregated values over all ad requests and historical data. Booking recommendation based on the overall customer base's recent bookings.

Went with C