Data Science Model Deployments and Cloud Computing on GCP - Lab - Develop and Submit PySpark Job

The video tutorial covers monitoring metrics in a web UI, explaining Pyspark code for processing Stack Overflow data using BigQuery, and comparing it with an equivalent SQL query. It also provides instructions for submitting the Pyspark job to Google Cloud's Dataproc serverless, including handling potential issues with CPU quota on a trial account.

What can you monitor on the homepage under the monitoring section?

• Network bandwidth

• YARN memory and CPU utilization

• User login history

• Database connections

Which component can be accessed via the web UI?

• Yarn resource manager

• Database manager

• Network monitor

• User profile manager

What is the first step in the PySpark code?

• Reading a table from BigQuery

• Writing data to a file

• Sending an email notification

• Creating a new database

What transformation is performed on the creation_date column?

• Extracting year and month

• Encrypting the data

• Converting to uppercase

• Splitting into multiple columns

Where is the transformed data written after processing?

• A local file system

• Directly to the console

• An email attachment

• A folder inside a specified bucket

What command is used to submit a PySpark job to Dataproc serverless?

• gcloud dataproc batches

• kubectl spark deploy

• aws dataproc submit

• azure spark run

What should you do if you encounter a CPU quota error?

• Contact customer support

• Increase the memory allocation

• Restart the computer

• Change the region or ensure only one job is running