Data Science Model Deployments and Cloud Computing on GCP - Lab - Data Validation Using App Engine

The video tutorial covers using BigQuery to access a public ML dataset for fraud detection. It explains the dataset's structure, including 31 anonymized columns and a class column for fraud detection. The tutorial outlines data validation rules, such as ensuring all columns are present and class values are valid. A Python script is used for validation, and the deployment process on Google App Engine is demonstrated. Future steps involve chaining applications using GCP Workflow.

What is the name of the table used for training and development in the BigQuery public data set?

• Fraud Analysis Table

• ULB Fraud Detection

• Credit Card Transactions

• ML Training Data

How many attributes does the ULB Fraud Detection table contain?

• 28

• 31

• 35

• 40

What is the first step in the use case described in the video?

• Data Collection

• Data Validation

• Model Training

• Model Deployment

What is the first rule applied during data validation?

• Verify data types

• Check for missing values

• Ensure data is anonymized

• Validate the number of attributes

What does the function 'validate data set attributes' do in the Python script?

• Checks for missing values

• Validates data types

• Counts the total number of columns

• Analyzes data distribution

What is the purpose of the 'validate output variable' function?

• To validate data types

• To count the number of rows

• To ensure class column values are only 0 or 1

• To check for missing values

Which environment is used for deploying the application?

• AWS Lambda

• Google App Engine Flexible

• Azure Functions

• Google Cloud Functions