Data Engineering y BigQuery V1

What should you do?

• Use Prophet on Vertex AI Training to build a custom model.

• Use Vertex AI Forecast to build a NN-based model.

• Use BigQuery ML to build a statistical ARIMA_PLUS model.

• Use TensorFlow on Vertex AI Training to build a custom model.

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How should you implement the preprocessing workflow?

• Implement a preprocessing pipeline by using Apache Spark, and run the pipeline on Dataproc. Save the preprocessed data as CSV files in a Cloud Storage bucket

• Load the data into a pandas DataFrame. Implement the preprocessing steps using pandas transformations, and train the model directly on the DataFrame.

• Perform preprocessing in BigQuery by using SQL. Use the BigQueryClient in TensorFlow to read the data directly from BigQuery.

• Implement a preprocessing pipeline by using Apache Beam, and run the pipeline on Dataflow. Save the preprocessed data as CSV files in a Cloud Storage bucket.

What should you do?

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You are developing a model to help your company create more targeted online advertising campaigns. You need to create a dataset that you will use to train the model. You want to avoid creating or reinforcing unfair bias in the model. What should you do? (Choose two.)

• Include a comprehensive set of demographic features.

• Include only the demographic groups that most frequently interact with advertisements.

• Collect a random sample of production traffic to build the training dataset.

• Collect a stratified sample of production traffic to build the training dataset.

• Conduct fairness tests across sensitive categories and demographics on the trained model.

You work at a gaming startup that has several terabytes of structured data in Cloud Storage. This data includes gameplay time data, user metadata, and game metadata. You want to build a model that recommends new games to users that requires the least amount of coding. What should you do?

• Load the data in BigQuery. Use BigQuery ML to train an Autoencoder model.

• Load the data in BigQuery. Use BigQuery ML to train a matrix factorization model.

• Read data to a Vertex AI Workbench notebook. Use TensorFlow to train a two-tower model.

• Read data to a Vertex AI Workbench notebook. Use TensorFlow to train a matrix factorization model.

How should you configure this workflow?

• Write the transformations into Spark that uses the spark-bigquery-connector, and use Dataproc to preprocess the data.

• Write SQL queries to transform the data in-place in BigQuery.

• Add the transformations as a preprocessing layer in the TensorFlow models.

• Create a Dataflow pipeline that uses the BigQuerylO connector to ingest the data, process it, and write it back to BigQuery.

What should you do?

• Use the TRANSFORM clause with the ML.ONE_HOT_ENCODER function on the categorical features at model creation and select the categorical and non-categorical features.

• Use the ML.ONE_HOT_ENCODER function on the categorical features and select the encoded categorical features and non-categorical features as inputs to create your model.

• Use the CREATE MODEL statement and select the categorical and non-categorical features.

• Use the ML.MULTI_HOT_ENCODER function on the categorical features, and select the encoded categorical features and non-categorical features as inputs to create your model.

You want to train an AutoML model to predict house prices by using a small public dataset stored in BigQuery. You need to prepare the data and want to use the simplest, most efficient approach. What should you do?

• Write a query that preprocesses the data by using BigQuery and creates a new table. Create a Vertex AI managed dataset with the new table as the data source.

• Use Dataflow to preprocess the data. Write the output in TFRecord format to a Cloud Storage bucket.

• Write a query that preprocesses the data by using BigQuery. Export the query results as CSV files, and use those files to create a Vertex AI managed dataset.

• Use a Vertex AI Workbench notebook instance to preprocess the data by using the pandas library. Export the data as CSV files, and use those files to create a Vertex AI managed dataset.

How should you speed up your model's predictions?

• Attach an NVIDIA P100 GPU to your deployed model’s instance.

• Use a low latency database for the customers’ historic purchase behavior.

• Deploy your model to more instances behind a load balancer to distribute traffic.

• Create a materialized view in BigQuery with the necessary data for predictions.