Font size
WorksheetsData Engineering y BigQuery V1
Total questions: 10
Worksheet time: 50mins
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.
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?
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.
