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Google Cloud Exams
GCP
Associate Data Practitioner
Plan a standard ML project (e.g., data collection, model training, model evaluation, prediction)
Raw to Prediction Pipeline on Google Cloud

Diagram

Raw to Prediction Pipeline on Google Cloud

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A left-to-right process flow with six numbered steps: archive raw files in Cloud Storage, cleanse with Dataflow, store prepared tables in BigQuery, train with CREATE MODEL, evaluate with ML.EVALUATE, and predict business decisions with ML.PREDICT.

In the Domain

How Collection and Preparation Fit Together

How Collection and Preparation Fit Together is the ordered workflow that moves data from archival to training through three services. The workflow preserves the raw record, cleans it in a pipeline, and stores the result where models can use it directly.

Archive raw data: Data flows from operational sources such as databases, event streams, and files into Cloud Storage for preservation.
Process and cleanse data: Data flows from Cloud Storage or live sources into a Dataflow pipeline, which filters, maps, and formats records step by step.
Store prepared data: Clean data flows from Dataflow into BigQuery tables with consistent types, columns, and feature definitions.
Train and predict: BigQuery ML models read directly from the prepared BigQuery tables to train, evaluate, and generate predictions.

Read it in the Domain summary

Domain 3 of 6

Plan a standard ML project (e.g., data collection, model training, model evaluation, prediction)

Read the Domain summary

Practice this Domain

26 practice questions

Practice this Domain

Last updated October 23, 2026

Domain 3 of 6

Plan a standard ML project (e.g., data collection, model training, model evaluation, prediction)

Read the Domain summary

Practice this Domain

26 practice questions

Practice this Domain

Practice questions in this domain

1.Your team is preparing to build a machine learning model to predict customer churn. You have collected raw historical transaction records and uploaded the CSV files into a Google Cloud Storage bucket. You now plan to ingest and organize this structured data into BigQuery for data exploration and machine learning preparation. What foundational resource must you create in your Google Cloud project before you can load the data into BigQuery tables?
2.A data engineering team is establishing the data collection and preparation architecture for a machine learning project on Google Cloud. The architecture must satisfy three specific requirements: Transform and cleanse large batches of raw data at scale using parallel processing pipelines. Store the prepared structured feature tables in an analytical warehouse where models can also be trained directly using SQL. Archive the original raw input files long-term in a highly durable, cost-effective object store. Which combination of Google Cloud services should the team select?
3.A data analyst at an e-commerce company wants to build a machine learning model to predict customer lifetime spending using tabular transaction data stored in BigQuery. The analyst needs to follow the standard machine learning lifecycle directly within the data warehouse using SQL statements. Which sequence of BigQuery ML SQL statements correctly follows the end-to-end lifecycle to train the model, assess its regression performance metrics (such as Mean Squared Error), and generate predictions on new customer records?

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Home
Google Cloud Exams
GCP
Associate Data Practitioner
Plan a standard ML project (e.g., data collection, model training, model evaluation, prediction)
Raw to Prediction Pipeline on Google Cloud

Diagram

Raw to Prediction Pipeline on Google Cloud

X LinkedIn Facebook Reddit

In the Domain

How Collection and Preparation Fit Together

How Collection and Preparation Fit Together is the ordered workflow that moves data from archival to training through three services. The workflow preserves the raw record, cleans it in a pipeline, and stores the result where models can use it directly.

Archive raw data: Data flows from operational sources such as databases, event streams, and files into Cloud Storage for preservation.
Process and cleanse data: Data flows from Cloud Storage or live sources into a Dataflow pipeline, which filters, maps, and formats records step by step.
Store prepared data: Clean data flows from Dataflow into BigQuery tables with consistent types, columns, and feature definitions.
Train and predict: BigQuery ML models read directly from the prepared BigQuery tables to train, evaluate, and generate predictions.

Read it in the Domain summary

Domain 3 of 6

Plan a standard ML project (e.g., data collection, model training, model evaluation, prediction)

Read the Domain summary

Practice this Domain

26 practice questions

Practice this Domain

Last updated October 23, 2026

Domain 3 of 6

Plan a standard ML project (e.g., data collection, model training, model evaluation, prediction)

Read the Domain summary

Practice this Domain

26 practice questions

Practice this Domain

Practice questions in this domain

1.Your team is preparing to build a machine learning model to predict customer churn. You have collected raw historical transaction records and uploaded the CSV files into a Google Cloud Storage bucket. You now plan to ingest and organize this structured data into BigQuery for data exploration and machine learning preparation. What foundational resource must you create in your Google Cloud project before you can load the data into BigQuery tables?
2.A data engineering team is establishing the data collection and preparation architecture for a machine learning project on Google Cloud. The architecture must satisfy three specific requirements: Transform and cleanse large batches of raw data at scale using parallel processing pipelines. Store the prepared structured feature tables in an analytical warehouse where models can also be trained directly using SQL. Archive the original raw input files long-term in a highly durable, cost-effective object store. Which combination of Google Cloud services should the team select?
3.A data analyst at an e-commerce company wants to build a machine learning model to predict customer lifetime spending using tabular transaction data stored in BigQuery. The analyst needs to follow the standard machine learning lifecycle directly within the data warehouse using SQL statements. Which sequence of BigQuery ML SQL statements correctly follows the end-to-end lifecycle to train the model, assess its regression performance metrics (such as Mean Squared Error), and generate predictions on new customer records?