Google Associate Cloud Engineer - Practice Test 1
Your organization is developing a new microservices-based application and plans to deploy it on Google Kubernetes Engine (GKE). You need to ensure that the GKE cluster can automatically scale its underlying infrastructure to accommodate future growth and the deployment of additional applications, minimizing manual intervention. What is the most appropriate action to take?
The question specifically asks how to scale the 'cluster' to accommodate future applications, implying the underlying infrastructure (nodes). Node pool autoscaling ensures that the cluster can automatically add or remove nodes based on workload demand, which is crucial for handling future growth without manual intervention. HorizontalPodAutoscalers scale pods, not the cluster's node capacity, and VerticalPodAutoscalers adjust resource requests for individual pods.
A 5 TB AVRO file is stored in a Cloud Storage bucket. Your data analysts are proficient only in SQL and require immediate access to this data. You need to provide a cost-effective solution quickly. What is the most appropriate action to take?
Creating external tables in BigQuery allows SQL-proficient analysts to query data directly from Cloud Storage without needing to load it into BigQuery, which is both cost-effective and fast. This approach avoids the expense and time of data ingestion into BigQuery or the complexity of setting up a Hadoop cluster for a one-off or ad-hoc query.
You are using Looker Studio to visualize data from a BigQuery table that serves as your data warehouse. Throughout the day, new data is appended to this warehouse. Each night, a daily summary is generated by overwriting the entire table. You've noticed that your Looker Studio charts are now displaying incorrectly or are broken. What is the most appropriate first step to diagnose this issue?
The problem describes broken charts in Looker Studio, which is fed by a BigQuery table that is overwritten nightly. The most direct cause for broken charts after a nightly overwrite would be an issue with that nightly job itself. Therefore, inspecting the BigQuery job logs and status is the most logical first step to identify data integrity or processing errors.
Your organization has multiple development teams, all based in the United States. Each team manages its own Google Cloud project. You need to enforce a policy that restricts all development teams to only create Google Cloud resources within US regions. What is the most effective way to achieve this?
Organization Policies are designed to enforce restrictions on 'what' can be configured within Google Cloud resources, such as limiting resource locations. IAM policies, in contrast, focus on 'who' can do 'what'. By creating a folder to group the projects and applying an Organization Policy at the folder level, the restriction will inherit down to all contained projects, ensuring all dev teams can only create resources in the US.
A cloud engineer needs to determine the exact time a specific Google Cloud service account was created. Which of the following actions should be performed in the Google Cloud Console to find this information?
Service account creation is a configuration change within Google Cloud. Therefore, to find this event, you should filter the Activity log by the 'Configuration' category. Additionally, since you are looking for a service account, the resource type should be filtered to 'Service Account'.