Associate-Cloud-Engineer Exam Dumps - Google Cloud Certified - Associate Cloud Engineer

https://cloud.google.com/monitoring/alerts#alerting-example

https://cloud.google.com/compute/docs/autoscaler#specifications

Autoscaling works independently from autohealing. If you configure autohealing for your group and an instance fails the health check, the autohealer attempts to recreate the instance. Recreating an instance can cause the number of instances in the group to fall below the autoscaling threshold (minNumReplicas) that you specify.

Since we need the application running at all times, we need a minimum 1 instance.

Only a single instance of the VM should run, we need a maximum 1 instance.

We want the application running at all times. If the VM crashes due to any underlying hardware failure, we want another instance to be added to MIG so that application can continue to serve requests. We can achieve this by enabling autoscaling. The only option that satisfies these three is Set autoscaling to On, set the minimum number of instances to 1, and then set the maximum number of instances to 1.

Ref: https://cloud.google.com/compute/docs/autoscaler

Logged information Within Cloud Audit Logs, there are two types of logs: Admin Activity logs: Entries for operations that modify the configuration or metadata of a project, bucket,or object. Data Access logs: Entries for operations that modify objects or read a project, bucket, or object. There are several sub-types of data access logs: ADMIN_READ: Entries for operations that read the configuration or metadata of a project, bucket, or object. DATA_READ: Entries for operations that read an object. DATA_WRITE: Entries for operations that create or modify an object.https://cloud.google.com/storage/docs/audit-logs#types

What is Google Cloud Memorystore?

Overview. Cloud Memorystore for Redis is a fully managed Redis service for Google Cloud Platform. Applications running on Google Cloud Platform can achieve extreme performance by leveraging the highly scalable, highly available, and secure Redis service without the burden of managing complex Redis deployments.

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Cloud Functions is Google Cloud’s event-driven serverless compute platform. It automatically scales based on the load and requires no additional configuration. You pay only for the resources used.

Ref: https://cloud.google.com/functions

While all other options i.e. Google Compute Engine, Google Kubernetes Engine, Google App Engine support autoscaling, it needs to be configured explicitly based on the load and is not as trivial as the scale up or scale down offered by Google’s cloud functions.

https://cloud.google.com/sql/docs/postgres

https://cloud.google.com/logging/docs/audit

Comprehensive and Detailed In Depth Explanation:

The core requirements are secure access to a specific BigQuery dataset from a Compute Engine instance, using short-lived credentials, adhering to Google's best practices, and minimizing operational overhead.

A. Project-level IAM role: Granting the BigQuery Data Viewer role at the project level gives the service account broad access to all BigQuery datasets within that project. This violates the principle of least privilege, a fundamental security best practice, as the application should only have access to the designated dataset.

B. Hourly new service account with dataset-level role: While this aims to achieve short-lived credentials, the operational burden of creating, attaching, and managing IAM policies for a new service account every hour is significant and not a Google-recommended practice for routine access. It introduces unnecessary complexity and potential for errors.

C. Custom service account with dataset-level IAM role: This is the recommended and most efficient approach. You create a dedicated Google Cloud service account specifically for this application. You then grant this service account the necessary IAM role (e.g., BigQuery Data Viewer, or a more specific custom role) directly on the target BigQuery dataset. When the Compute Engine instance runs as this service account, the Google Cloud client libraries automatically handle the acquisition and rotation of short-lived OAuth 2.0 access tokens from the instance's metadata server. This eliminates the need to manage long-lived credentials (like service account keys) and ensures the application only has access to the intended dataset. This adheres to the principle of least privilege and minimizes operational costs.

D. Hourly new service account with project-level role: This option combines the high operational overhead of frequently creating new service accounts with the security risk of granting overly permissive project-level access. It is not a recommended practice.

Therefore, the most secure, cost-effective, and operationally efficient solution is to create a custom service account, attach it to the Compute Engine instance, and grant it the appropriateBigQuery IAM role specifically on the target dataset. The platform handles the short-lived credentials automatically.

Google Cloud Documentation References:

Creating and enabling service accounts for instances: https://cloud.google.com/compute/docs/access/create-enable-service-accounts-for-instances - Explains how to create and associate service accounts with Compute Engine VMs.  

Granting, changing, and revoking access to resources: https://cloud.google.com/iam/docs/granting-changing-revoking-access - Details how to manage IAM policies and grant roles to service accounts on specific resources (like BigQuery datasets).  

BigQuery IAM roles: https://cloud.google.com/bigquery/docs/control-access - Provides information on the various IAM roles available for controlling access to BigQuery resources at different levels (project, dataset, table, etc.).

Best practices for using service accounts: https://cloud.google.com/iam/docs/best-practices-for-using-service-accounts - Emphasizes the importance of the principle of least privilege and avoiding the management of long-lived service account keys when possible (relying on the metadata server for short-lived tokens).