Passing information between jobs
[D] Store and share data with workflow artifacts
Use artifacts to share data between jobs in a workflow and store data once that workflow has completed.
[E] You can define outputs to pass information from one job to another.
Reference:
https://docs.github.com/en/actions/tutorials/store-and-share-data
https://docs.github.com/en/actions/how-tos/write-workflows/choose-what-workflows-do/pass-job- outputs

Monitoring and troubleshooting self-hosted runners
You can monitor your self-hosted runners to view their activity and diagnose common issues.
[A] Reviewing the self-hosted runner application log files
You can monitor the status of the self-hosted runner application and its activities. Log files are kept in the _diag directory where you installed the runner application, and a new log is generated each time the application is started. The filename begins with Runner_, and is followed by a UTC timestamp of when the application was started.
[D] You can choose one of the standard GitHub-hosted runner options or, if you are on the GitHub Team or GitHub Enterprise Cloud plan, you can provision a runner with more cores, or a runner that's powered by a GPU processor. These machines are referred to as "larger runner." Reference:
https://docs.github.com/en/actions/how-tos/manage-runners/self-hosted-runners/monitor-and- troubleshoot
https://docs.github.com/en/actions/concepts/runners/github-hosted-runners

Creating a Docker container action
Create a dockerfile
Dockerfile support for GitHub Actions
When creating a Dockerfile for a Docker container action, you should be aware of how some Docker instructions interact with GitHub Actions and an action's metadata file.
Creating an action metadata file
Create a new action.yml file.
Reference:
https://docs.github.com/en/actions/tutorials/use-containerized-services/create-a-docker-container- action
https://docs.github.com/en/actions/reference/workflows-and-actions/dockerfile-support

To handle a 10 MB dataset securely in GitHub Actions, you should use GitHub Actions Secrets for the key and a symmetric encryption tool (like GPG or OpenSSL) for the file.
Follow these steps:
[B] 1. Encrypt the Dataset Locally
Since GitHub Actions secrets have a size limit of 48 KB, you cannot store a 20 MB dataset directly as a secret. Instead, encrypt the file locally using GPG before committing it.
[A] 2. Store the Encryption Key in GitHub
Store the passphrase you just created as a GitHub Actions Secret so the workflow can access it securely without exposing it in the code.
[G] 3. Commit the Encrypted Dataset
Now, add only the encrypted version of your data to your repository.
Add the encrypted file: git add large_dataset.csv.gpg
Important: Ensure the original unencrypted large_dataset.csv is added to your .gitignore so it is never committed.
Commit and push the encrypted file to your repository.
4. Decrypt the Dataset in the Workflow
In your GitHub Actions workflow file (e.g., .github/workflows/main.yml), add a step to decrypt the file using the secret passphrase.
Incorrect:
[Not D]
Since GitHub Secrets have a size limit (64 KB), you cannot store the dataset itself as a secret.
Reference:
https://docs.github.com/en/[email protected]/actions/how-tos/write-workflows/choose-what- workflows-do/use-secrets

GitHub Actions automatically redacts secret values from workflow logs so if a command prints a secret the value is masked in the output. This built in protection helps prevent accidental exposure of credentials during job execution and it applies without additional configuration.