ARC123

Overview

In a challenge lab you’re given a scenario and a set of tasks. Instead of following step-by-step instructions, you will use the skills learned from the labs in the course to figure out how to complete the tasks on your own! An automated scoring system (shown on this page) will provide feedback on whether you have completed your tasks correctly.

When you take a challenge lab, you will not be taught new Google Cloud concepts. You are expected to extend your learned skills, like changing default values and reading and researching error messages to fix your own mistakes.

To score 100% you must successfully complete all tasks within the time period!

Setup

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources are made available to you.

This hands-on lab lets you do the lab activities in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

• Access to a standard internet browser (Chrome browser recommended).

• Time to complete the lab—remember, once you start, you cannot pause a lab.

Challenge scenario

Your team manages customer data gathered from online shopping sessions and stored in Cloud Storage. Some data contains sensitive information so it’s important to limit access.

Your challenge

As a junior engineer, you’re asked to help with the project by completing the following tasks:

• Create a BigQuery dataset to store the connection to your BigLake table.
• Create a BigLake table using a Cloud Resource connection.
• Create an aspect and apply to sensitive data in the BigLake table.

Each task is described in detail below, good luck!

Task 1. Create a BigQuery dataset

• Create a BigQuery dataset named ecommerce that is multi-region in the United States.

Click Check my progress to verify the objective. Create a BigQuery dataset

Task 2. Create a BigLake table using a Cloud Resource connection

1. Create a multi-region Cloud Resource connection in the United States, named customer_data_connection with the appropriate service account permissions to read Cloud Storage files in your project.

2. Within the BigQuery dataset named ecommerce, use the Cloud Resource connection to create a BigLake table named customer_online_sessions.

• When creating the table, load data from the following Cloud Storage file using schema auto-detection:
  • gs://-bucket/customer-online-sessions.csv

Click Check my progress to verify the objective. Create a BigLake table using a Cloud Resource connection

Task 3. Create an aspect and apply it to the BigLake table

1. Create a multi-region aspect in the United States, named Sensitive Data Aspect with two fields:

• Boolean field named Has Sensitive Data.
• Enumerated field named Sensitive Data Type that contains three values: Location Info, Contact Info, and None.

2. Apply the aspect to the BigLake table as containing sensitive data using both enumerated fields:

• Has Sensitive Data: TRUE
• Sensitive Data Type: Location Info

Click Check my progress to verify the objective. Create an aspect and apply it to the BigLake table

Congratulations!

You completed the Enrich Metadata and Discovery of BigLake Data skill badge!

Earn your next skill badge

This self-paced lab is part of the Enrich Metadata and Discovery of BigLake Data skill badge course. Completing this skill badge course earns you the badge above, to recognize your achievement. Share your badge on your resume and social platforms, and announce your accomplishment using #GoogleCloudBadge.

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Manual Last Updated December 18, 2025

Lab Last Tested November 06, 2025

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