QUESTION: 105

You work for a gaming company that develops massively multiplayer online (MMO) games. You built a TensorFlow model that predicts whether players will make in-app purchases of more than $10 in the next two weeks. The model’s predictions will be used to adapt each user’s game experience. User data is stored in BigQuery. How should you serve your model while optimizing cost, user experience, and ease of management?

Import the model into BigQuery ML. Make predictions using batch reading data from BigQuery, and push the data to Cloud SQL
Deploy the model to Vertex AI Prediction. Make predictions using batch reading data from Cloud Bigtable, and push the data to Cloud SQL.
Embed the model in the mobile application. Make predictions after every in-app purchase event is published in Pub/Sub, and push the data to Cloud SQL.
Embed the model in the streaming Dataflow pipeline. Make predictions after every in-app purchase event is published in Pub/Sub, and push the data to Cloud SQL.

Answer(s): A

Reveal Solution Next Question

QUESTION: 106

You are building a linear regression model on BigQuery ML to predict a customer’s likelihood of purchasing your company’s products. Your model uses a city name variable as a key predictive component. In order to train and serve the model, your data must be organized in columns. You want to prepare your data using the least amount of coding while maintaining the predictable variables. What should you do?

Use TensorFlow to create a categorical variable with a vocabulary list. Create the vocabulary file, and upload it as part of your model to BigQuery ML.
Create a new view with BigQuery that does not include a column with city information
Use Cloud Data Fusion to assign each city to a region labeled as 1, 2, 3, 4, or 5, and then use that number to represent the city in the model.
Use Dataprep to transform the state column using a one-hot encoding method, and make each city a column with binary values.

Answer(s): B

Reveal Solution Next Question

QUESTION: 107

You are an ML engineer at a bank that has a mobile application. Management has asked you to build an ML-based biometric authentication for the app that verifies a customer’s identity based on their fingerprint. Fingerprints are considered highly sensitive personal information and cannot be downloaded and stored into the bank databases. Which learning strategy should you recommend to train and deploy this ML mode?

Data Loss Prevention API
Federated learning
MD5 to encrypt data
Differential privacy

Answer(s): C

Reveal Solution Next Question

QUESTION: 108

You are experimenting with a built-in distributed XGBoost model in Vertex AI Workbench user-managed notebooks. You use BigQuery to split your data into training and validation sets using the following queries:

CREATE OR REPLACE TABLE ‘myproject.mydataset.training‘ AS
(SELECT * FROM ‘myproject.mydataset.mytable‘ WHERE RAND() <= 0.8);

CREATE OR REPLACE TABLE ‘myproject.mydataset.validation‘ AS
(SELECT * FROM ‘myproject.mydataset.mytable‘ WHERE RAND() <= 0.2);

After training the model, you achieve an area under the receiver operating characteristic curve (AUC ROC) value of 0.8, but after deploying the model to production, you notice that your model performance has dropped to an AUC ROC value of 0.65. What problem is most likely occurring?

There is training-serving skew in your production environment.
There is not a sufficient amount of training data.
The tables that you created to hold your training and validation records share some records, and you may not be using all the data in your initial table.
The RAND() function generated a number that is less than 0.2 in both instances, so every record in the validation table will also be in the training table.

Answer(s): A

Reveal Solution Next Question

Free Professional Machine Learning Engineer Exam Braindumps (page: 27)

QUESTION: 105

QUESTION: 106

QUESTION: 107

QUESTION: 108