Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

• A.1. Create a Pub/Sub topic for each user
  2 Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
• B.1. Create a Pub/Sub topic for each user
  2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold
• C.1 Build a notification system on Firebase
  2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold
• D.1. Build a notification system on Firebase
  2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold

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You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?

• A.Ensure that training is reproducible
• B.Ensure that all hyperparameters are tuned
• C.Ensure that model performance is monitored
• D.Ensure that feature expectations are captured in the schema

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You need to design an architecture that serves asynchronous predictions to determine whether a particular mission-critical machine part will fail. Your system collects data from multiple sensors from the machine. You want to build a model that will predict a failure in the next N minutes, given the average of each sensor's data from the past 12 hours. How should you design the architecture?

• A.1. Export your data to Cloud Storage using Dataflow.
  2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.
  3. Export the batch prediction job outputs from Cloud Storage and import them into Cloud SQL.
• B.1. Export the data to Cloud Storage using the BigQuery command-line tool
  2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data.
  3. Export the batch prediction job outputs from Cloud Storage and import them into BigQuery.
• C.1. Events are sent by the sensors to Pub/Sub, consumed in real time, and processed by a Dataflow stream processing pipeline.
  2. The pipeline invokes the model for prediction and sends the predictions to another Pub/Sub topic.
  3. Pub/Sub messages containing predictions are then consumed by a downstream system for monitoring.
• D.1. HTTP requests are sent by the sensors to your ML model, which is deployed as a microservice and exposes a REST API for prediction
  2. Your application queries a Vertex AI endpoint where you deployed your model.
  3. Responses are received by the caller application as soon as the model produces the prediction.

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A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user.
The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.
Which strategy will allow the data scientist to identify fraudulent accounts?

• A.Execute the built-in FindDuplicates Amazon Athena query.
• B.Create a FindMatches machine learning transform in AWS Glue.
• C.Create an AWS Glue crawler to infer duplicate accounts in the source data.
• D.Search for duplicate accounts in the AWS Glue Data Catalog.

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You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?

• A.Build a classification model
• B.Build a knowledge-based filtering model
• C.Build a collaborative-based filtering model
• D.Build a regression model using the features as predictors

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