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You are building a data lake on Google Cloud for your Internet of Things (loT) application. The loT application has millions of sensors that are constantly streaming structured and unstructured data to your backend in the cloud. You want to build a highly available and resilient architecture based on Google-recommended practices. What should you do?
(You are deploying an application to Google Kubernetes Engine (GKE). The application needs to make API calls to a private Cloud Storage bucket. You need to configure your application Pods to authenticate to the Cloud Storage API, but your organization policy prevents the usage of service account keys. You want to follow Google-recommended practices. What should you do?)
The organization policy explicitly prevents the use of service account keys, so options A and B, which involve requesting exceptions to create them, are not in line with the policy and Google's recommended practices for secure authentication.
Your company uses Cloud Storage to store application backup files for disaster recovery purposes. You want to follow Google's recommended practices. Which storage option should you use?
https://cloud.google.com/blog/products/gcp/introducing-coldline-and-a-unified-platform-for-data-storage
Cloud Storage Coldline: a low-latency storage class for long-term archiving Coldline is a new Cloud Storage class designed for long-term archival and disaster recovery. Coldline is perfect for the archival needs of big data or multimedia content, allowing businesses to archive years of data. Coldline provides fast and instant (millisecond) access to data and changes the way that companies think about storing and accessing their cold data.
You want to configure a solution for archiving data in a Cloud Storage bucket. The solution must be cost-effective. Data with multiple versions should be archived after 30 days. Previous versions are accessed once a month for reporting. This archive data is also occasionally updated at month-end. What should you do?
Nearline Storage is ideal for data you plan to read or modify on average once per month or less. And this option archives just the noncurrent versions which is what we want to do.
Ref:https://cloud.google.com/storage/docs/storage-classes#nearline
You are configuring service accounts for an application that spans multiple projects. Virtual machines (VMs) running in the web-applications project need access to BigQuery datasets in crm-databases-proj. You want to follow Google-recommended practices to give access to the service account in the web-applications project. What should you do?
bigquery.dataViewer role provides permissions to read the datasets metadata and list tables in the dataset as well as Read data and metadata from the datasets tables. This is exactly what we need to fulfil this requirement and follows the least privilege principle.
Ref:https://cloud.google.com/iam/docs/understanding-roles#bigquery-roles
