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 will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that 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).

Note: Use an Incognito or private browser window to run this lab. This prevents any conflicts between your personal account and the Student account, which may cause extra charges incurred to your personal account.

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

Note: If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your account.

Challenge scenario

You are just starting your junior data engineer role. So far you have been helping teams create and manage BigLake assets.

You are expected to have the skills and knowledge for these tasks.

Your challenge

You are asked to help a newly formed development team with some of their initial work on a new project. Specifically, they need to create a new BigLake table from Cloud Storage and tag data using a new tag template to label sensitive data for online shopping sessions by customers; you receive the following request to complete 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 a tag template to label sensitive data and apply tags to the BigLake table.

Some standards you should follow:

• Ensure that any needed APIs (such as Data Catalog and BigQuery Connection API) are successfully enabled and that necessary service accounts have the appropriate permissions.
• Create all resources in the region, unless otherwise directed.

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 Cloud Resource connection 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 a tag template and attach a tag to the BigLake table

1. Create a public tag template named Sensitive Data Template 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. Use the tag template to tag 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 a tag template and attach a tag to the BigLake table

Congratulations!

Earn your next skill badge

This self-paced lab is part of the Tag and Discover 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 October 18, 2023

Lab Last Tested May 23, 2023

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