Professional Machine Learning Engineer Exam - Question 49

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?

Examice · Accepted Answer

Given the requirements of the problem, where model latency must be 300ms@p99, the inventory of web banners is substantial, and navigation context is a significant predictor, embedding the client on the website is a given operational step. Deploying the gateway on App Engine ensures that requests can be efficiently managed. Using Cloud Bigtable as the database for reading and writing the user's navigation context is crucial because Bigtable supports high throughput with low latency, making it suitable for real-time prediction scenarios. Finally, deploying the model on AI Platform Prediction allows for the necessary computational resources and infrastructure for the machine learning model. This configuration offers a strong balance between simplicity, efficiency, and meeting latency requirements.

Paul_Dirac · Answer

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

Celia20210714 · Answer

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

tavva_prudhvi · Answer

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.

Sum_Sum · Answer

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

rightcd · Answer

look at Q80

AnnaR · Answer

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.

fredcaram · Answer

The volume is too low for a Bigtable scenario

M25 · Answer

Went with C

CloudKida · Answer

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.

Voyager2 · Answer

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.

friedi · Answer

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

Liting · Answer

Bigtable is recommended for storage in the case scenario.

harithacML · Answer

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

Mickey321 · Answer

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

gscharly · Answer

agree with Paul_Dirac

PhilipKoku · Answer

C) Big Table for low latency

ccb23cc · 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/

Professional Machine Learning Engineer Exam - Question 49

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