QUESTION: 41

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 customers’ 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?

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.
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.
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.
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.

Answer(s): D

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QUESTION: 42

You work for an advertising company and want to understand the effectiveness of your company’s latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table, and then manipulate the results of that query with a pandas dataframe in an AI Platform notebook. What should you do?

Use AI Platform Notebooks’ BigQuery cell magic to query the data, and ingest the results as a pandas dataframe.
Export your table as a CSV file from BigQuery to Google Drive, and use the Google Drive API to ingest the file into your notebook instance.
Download your table from BigQuery as a local CSV file, and upload it to your AI Platform notebook instance. Use pandas.read_csv to ingest he file as a pandas dataframe.
From a bash cell in your AI Platform notebook, use the bq extract command to export the table as a CSV file to Cloud Storage, and then use gsutil cp to copy the data into the notebook. Use pandas.read_csv to ingest the file as a pandas dataframe.

Answer(s): A

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QUESTION: 43

You are an ML engineer at a global car manufacture. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

Thee individual features: binned latitude, binned longitude, and one-hot encoded car type.
One feature obtained as an element-wise product between latitude, longitude, and car type.
One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type.
Two feature crosses as an element-wise product: the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type.

Answer(s): C

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QUESTION: 44

You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time. How should you build the model?

Use the AI Platform Training built-in algorithms to create a custom model.
Use AutoMlL Natural Language to extract custom entities for classification.
Use the Cloud Natural Language API to extract custom entities for classification.
Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm.

Answer(s): A

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Free Professional Machine Learning Engineer Exam Braindumps (page: 11)

QUESTION: 41

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QUESTION: 44