Free Practice Questions for Google Cloud-Digital-Leader Exam

Cloud Logging is a fully managed service that allows you to store, search, analyze, monitor, and alert on logging data and events from Google Cloud and Amazon Web Services

This approach carries over as much custom components as possible from the source system and minimizes initial reengineering efforts.

The correct answer is B. Organizations do not need to make upfront capital investments in cloud security. Here's why:

Context of the Question: The question asks about the benefits of the operational expenditure (OpEx) model for cloud security.

Google Cloud Product Relevance:

The OpEx model involves paying for cloud resources and services on a pay-as-you-go basis, avoiding the need for large upfront capital expenditures (CapEx) for security hardware or software.

In the cloud, security services (such as firewalls, encryption, and monitoring tools) are provided as managed services, which organizations can subscribe to and pay for based on usage. This model allows for more flexibility and scalability in managing security needs and costs.

Why Not Other Options:

A. Organizations can deploy custom security hardware: In the cloud, most security needs are met through managed services rather than deploying custom hardware.

C. Organizations do not need to configure any security settings for cloud resources: This is incorrect, as organizations are responsible for configuring security settings for their applications and data.

D. The cloud provider guarantees security: The cloud provider guarantees the security of the infrastructure, but the customer is responsible for securing their applications, data, and access controls.

Google Cloud Digital Leader References:

Refer to Google Cloud's shared responsibility model documentation for more information on OpEx benefits for cloud security.

Google's purpose-built servers are specifically designed and optimized for Google's workloads, making them more efficient than standard off-the-shelf servers. These servers are tailored for specific tasks, such as handling large-scale data processing or machine learning workloads, providing better performance and energy efficiency.

Option C: They are optimized for specific tasks making them more efficient is correct because it describes the primary benefit of Google's custom server hardware.

https://cloud.google.com/sql/docs/postgres/using-query-insights

The correct answer is C. A Preemptible or Spot VM on Compute Engine. Here's why:

Context of the Question: The organization runs a batch data analysis workload that is not time-critical and can be restarted without losing work, and they want to choose the lowest-cost option.

Google Cloud Product Relevance:

Preemptible VMs (also known as Spot VMs) are short-lived compute instances that offer a significantly lower cost than standard VMs. They are ideal for non-time-critical workloads that can be interrupted and restarted, such as batch data analysis.

Spot VMs are a cost-effective choice for workloads that can tolerate interruptions, providing substantial cost savings compared to standard VMs.

Why Not Other Options:

A. A standard VM in a pay-as-you-go model on Compute Engine: This would be more expensive than using preemptible or spot VMs.

B. A Cloud Function with a small memory limit: Cloud Functions are designed for event-driven tasks, not for long-running batch data analysis.

D. A custom VM in a pay-as-you-go model on Compute Engine: This would also be more expensive compared to using preemptible or spot VMs.

Google Cloud Digital Leader References:

Refer to Compute Engine Spot VMs documentation to understand how to use and benefit from Spot VMs for cost savings.

A data lake can store all types of data with no fixed limitation on account size or file and with no specific purpose defined yet. The data comes from disparate sources and can be structured, semi-structured, or even unstructured. Data-lake data can be queried as needed.

https://cloud.google.com/learn/what-is-a-data-lake

A data lake is a centralized repository designed to store, process, and secure large amounts of structured, semistructured, and unstructured data. It can store data in its native format and process any variety of it, ignoring size limits.