Cloud Digital Leader Practice Test 2 — 10 Questions

Question 1

A hospital runs a patient records system that must remain on-premises due to strict regulatory data residency requirements. However, they also want to use cloud-based AI for diagnostic imaging analysis. Which cloud deployment model best describes their architecture?

Accepted Answer

Hybrid cloud — combining on-premises infrastructure with public cloud services.. The hospital must keep patient records on-premises to comply with data residency regulations, but wants to leverage cloud-based AI for diagnostic imaging. A hybrid cloud model combines on-premises infrastructure (for sensitive data) with public cloud services (for AI processing), allowing data to remain resident while compute-intensive tasks are offloaded. This matches the scenario exactly, as hybrid cloud enables workload distribution across private and public environments.

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Public cloud — all workloads run in a provider's infrastructure.

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Private cloud — all workloads run in the hospital's own infrastructure.

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Multi-cloud — using multiple public cloud providers simultaneously.

Question 2

Which term describes the model where the cloud provider is responsible for the security of the cloud infrastructure, while the customer is responsible for security within their own cloud environment (data, applications, access management)?

Accepted Answer

Shared responsibility model. The shared responsibility model defines the division of security responsibilities between the cloud provider and the customer. Google secures the physical infrastructure, hardware, hypervisor, and core services. The customer secures what they put in the cloud: data classification, access control, application security, network configuration, and compliance. The boundary between provider and customer responsibility varies by service model (IaaS vs. PaaS vs. SaaS).

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Zero trust security model

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Defense in depth strategy

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Identity federation model

Question 3

A business intelligence team wants to create interactive dashboards and reports from their BigQuery data without writing code. They need to share reports with stakeholders who don't have GCP accounts. Which Google Cloud tool is most appropriate?

Accepted Answer

Looker Studio (formerly Data Studio). Looker Studio (formerly Data Studio) is the correct choice because it is a no-code, drag-and-drop business intelligence tool that connects directly to BigQuery, enabling the creation of interactive dashboards and reports. It also supports sharing reports via public links or embedded views, allowing stakeholders without GCP accounts to access them without needing IAM permissions.

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Vertex AI Workbench

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Cloud Dataprep by Trifacta

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BigQuery Studio

Question 4

A media company currently licenses proprietary software for video editing that costs $50,000 per seat annually. They are considering a cloud-based SaaS alternative at $5,000 per seat annually. Beyond the licensing cost, which additional financial benefits should they consider when calculating total cost of ownership (TCO)?

Accepted Answer

Eliminated hardware costs, reduced IT maintenance staff, no upgrade cycles, and freed facilities costs — all lowering the true on-premises TCO that should be compared against the SaaS subscription.. Option B is correct because the total cost of ownership (TCO) for on-premises software includes not just the licensing fee but also hardware acquisition, IT staff for maintenance, periodic upgrade costs, and physical facility expenses. By moving to a SaaS model, the company eliminates these variable costs, making the $5,000 per seat subscription a more accurate comparison against the fully-loaded on-premises TCO, which often exceeds the $50,000 license alone.

Answer

Only the licensing cost difference ($45,000 per seat) matters for the financial comparison.

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The SaaS option has an internet dependency risk that may cost more than the savings.

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The vendor's market capitalization, since larger companies are more financially stable.

Question 5

According to the NIST definition of cloud computing, which characteristic allows users to unilaterally provision computing resources such as server time and network storage without requiring human interaction with the service provider?

Accepted Answer

On-demand self-service. NIST's five essential characteristics of cloud computing are: on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. 'On-demand self-service' specifically describes the ability for users to provision capabilities automatically without provider interaction — using a web console or API to spin up VMs, databases, or storage instantly, without calling a salesperson or waiting for manual provisioning.

Answer

Broad network access

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Resource pooling

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Measured service

Question 6

A company uses two different public cloud providers (AWS for their North American operations and Google Cloud for their European operations) to meet data residency requirements and avoid vendor lock-in. Which deployment model does this represent?

Accepted Answer

Multi-cloud. This scenario describes using two distinct public cloud providers (AWS and Google Cloud) to meet data residency and avoid vendor lock-in, which is the definition of a multi-cloud deployment model. Multi-cloud involves using multiple public cloud services from different vendors, as opposed to combining public and private infrastructure (hybrid cloud) or simply deploying across multiple regions within a single provider.

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Hybrid cloud

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Multi-region

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Distributed cloud

Question 7

A retail company wants to build a recommendation engine that suggests products to customers based on their browsing history. The team has ML expertise but wants to use Google's pre-built ML infrastructure to train and deploy models at scale without managing compute resources. Which Google Cloud service should they use?

Accepted Answer

Vertex AI. Vertex AI is the correct choice because it provides a fully managed, unified ML platform that handles the entire ML workflow—from data preparation and training to deployment and monitoring—without requiring the team to manage underlying compute infrastructure. It integrates with Google Cloud's pre-built ML infrastructure, including distributed training, AutoML, and custom model serving, making it ideal for building and scaling a recommendation engine.

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BigQuery ML

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Cloud AI Platform Notebooks (now Vertex AI Workbench)

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Cloud Dataflow

Question 8

A regional insurance company competes with an InsurTech startup that uses cloud-native AI to personalize policies, process claims in minutes, and launch new products weekly. The traditional insurer takes 6 months to launch new products and 2 weeks to process claims. Which cloud-enabled business model advantage does the startup have?

Accepted Answer

Innovation velocity and operational efficiency through cloud-native AI, enabling faster product iteration and dramatically faster customer service delivery.. Option B is correct because the startup leverages cloud-native AI to achieve innovation velocity (weekly product launches vs. 6 months) and operational efficiency (minutes vs. 2 weeks for claims). This is a direct cloud-enabled business model advantage: elastic infrastructure and AI services allow rapid iteration and automated workflows, which traditional on-premises systems cannot match.

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Lower insurance premiums because cloud infrastructure costs less than data centers.

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Better regulatory compliance because cloud providers have more compliance certifications.

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Access to more insurance actuarial data than the traditional insurer.

Question 9

An organization runs its entire infrastructure on a single public cloud provider (Google Cloud). All applications, data, and services live in Google Cloud's infrastructure. Which deployment model describes this?

Accepted Answer

Public cloud. Option B is correct because the organization is using a single public cloud provider, Google Cloud, which delivers computing resources over the public internet on a pay-as-you-go basis. In a public cloud deployment, the infrastructure is owned and operated by the cloud provider and shared across multiple tenants, which matches the scenario where all applications, data, and services reside in Google Cloud's infrastructure.

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Private cloud

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Hybrid cloud

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Community cloud

Question 10

A data analytics team needs to analyze petabytes of structured data using SQL queries without managing any database infrastructure. Query results must return within seconds for most queries. Which Google Cloud service is designed for this use case?

Accepted Answer

BigQuery. BigQuery is a serverless, highly scalable data warehouse designed for analyzing petabytes of data using SQL without any infrastructure management. Its columnar storage and distributed query engine enable sub-second query performance on large datasets, making it ideal for this use case.

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Cloud SQL

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Cloud Bigtable

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Cloud Spanner