Data Science Model Deployments and Cloud Computing on GCP - Lab - Airflow with Serverless PySpark

This video tutorial explains how to set up and configure an Airflow DAG to schedule a PySpark job in a Dataproc serverless environment. It covers the creation of a Google Cloud Composer environment, the structure and components of the DAG script, and the process of uploading and executing the DAG. The tutorial also addresses troubleshooting common issues and re-running the DAG to ensure successful execution.

What is the purpose of the DAX folder in the Google Cloud Composer environment?

• To host web applications

• To manage user permissions

• To upload DAC scripts or Python scripts

• To store temporary files

Which operator is used to create a batch in the Dataproc serverless environment?

• Dataproc launch batch operator

• Dataproc initialize batch operator

• Dataproc start batch operator

• Dataproc create batch operator

What is the role of the 'days_ago' function in the DAC script?

• To determine the frequency of DAG execution

• To set the start date for the DAG

• To calculate the number of days since the last execution

• To specify the time zone for the DAG

How can you customize the schedule interval for a DAG?

• By using a Cron tab expression

• By setting a fixed time interval

• By specifying a time zone

• By defining a start and end date

What is the sequence of tasks in the DAG?

• Create batch, delete batch, get batch

• Create batch, get batch, delete batch

• Delete batch, create batch, get batch

• Get batch, create batch, delete batch

What should you do after uploading the DAC script to the DAX folder?

• Verify its appearance in the Airflow UI

• Restart the Google Cloud Composer environment

• Run a test execution of the DAG

• Delete the previous version of the script

What is the default DAG created during the Google Cloud Composer environment setup?

• Airflow Monitoring

• Dataproc Serverless Monitoring

• Airflow Onsco Monitoring

• Dataproc Batch Monitoring

What was the reason for the batch failure in the video?

• Insufficient permissions

• Incorrect Python script path

• Output file from previous execution not cleared

• Network connectivity issues

How can you rerun a failed DAG in Airflow?

• By clearing all previous logs

• By restarting the Airflow server

• By clicking the 'trigger DAG' button

• By modifying the DAG script

What indicates a successful execution of the Pyspark job in the video?

• The output is visible in the GCS bucket

• The Airflow UI shows an error message

• The batch remains in the Dataproc environment

• The DAG is listed as 'failed' in Airflow