Free PROFESSIONAL MACHINE LEARNING ENGINEER Exam Braindumps
Professional Machine Learning Engineer Questions 166–170 (page: 35)

You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 20 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?

You have deployed a customer chum prediction model to an Agent Platform endpoint. You want to implement a continuous training pipeline that retrains the model when the statistical distribution of the input features in production deviates significantly from the baseline training data.
What should you do?

You are an ML engineer at a retail company. You need to automate a recurring retraining workflow for a demand forecasting model. The workflow involves dependencies across hybrid environments and must execute the following sequential steps:
1. Trigger a shell script on an on-premises server to extract legacy sales data and send it to a Cloud Storage bucket.
2. Trigger a pipeline in Agent Platform Pipelines to train and validate the new model.
3. Push the validation metrics to a third-party API.
You need a fully managed orchestration service that provides native connectors to handle these cross-platform dependencies. You want to use the most stable, robust solution.
What should you do?

You work for a company that manages a ticketing platform for a large chain of cinemas. Customers use a mobile app to search for movies they’re interested in and purchase tickets in the app. Ticket purchase requests are sent to Pub/Sub and are processed with a Dataflow streaming pipeline configured to conduct the following steps:
1. Check for availability of the movie tickets at the selected cinema.
2. Assign the ticket price and accept payment.
3. Reserve the tickets at the selected cinema.
4. Send successful purchases to your database.
Each step in this process has low latency requirements (less than 50 milliseconds). You have developed a logistic regression model with BigQuery ML that predicts whether offering a promo code for free popcorn increases the chance of a ticket purchase, and this prediction should be added to the ticket purchase process. You want to identify the simplest way to deploy this model to production while adding minimal latency.
What should you do?

You are training an ML model using data stored in BigQuery that contains several values that are considered Personally Identifiable Information (PII). You need to reduce the sensitivity of the dataset before training your model. Every column is critical to your model. How should you proceed?