Ngân hàng đề — Google Professional Cloud Architect

You are a cloud architect who is developing a system that needs to trigger a Cloud Function on a regular basis. You've decided to use Cloud Scheduler to achieve this task. The Cloud Function you've developed is designed to automatically update the inventory in a Cloud Firestore database every day at midnight based on information pulled from an external API. Given this scenario, which of the following approaches is the most suitable way to accomplish this task?

Your team is running an analytics application on a custom Compute Engine VM (e2-highmem-4) with a single SSD persistent disk (200 GB). The team has observed that disk I/O latency is higher than expected during peak hours. You are asked to improve disk performance without provisioning a new VM and while keeping costs low. Which action should you take?

For this question, refer to the Altostrat Media case study.

https://services.google.com/fh/files/misc/v6.1_pca_altostrat_media_case_study_english.pdf 

Altostrat wants near–real-time visibility into trending topics and audience sentiment as new media content is published. These insights should be combined with user engagement data already stored in BigQuery to support dynamic pricing and targeted marketing campaigns. The architecture must be cost-efficient and require minimal manual intervention. Which approach best meets Altostrat’s requirements for timely trend detection and insight extraction?

Your organization is preparing to build a complex data science solution on Google Cloud Platform. The solution involves various stages, including data collection, cleaning, analysis, machine learning model training, and deploying models for real-time predictions. The data volume is significant, and the solution will require multiple services on GCP for various stages. As the cloud architect, which of the following architectures will you recommend for managing this data science solution effectively?

A large financial services company is looking to migrate its legacy data warehousing solution to the cloud to reduce costs and improve performance. The data warehousing solution must handle the following requirements:

• store and process petabytes of financial data

• support real-time data ingestion and analysis

• ensure data security and compliance with industry regulations

• provide a flexible and scalable architecture for future growth

Which of the following Google Cloud solutions would best meet these requirements?

You are responsible for designing an infrastructure solution to train a machine learning (ML) model on a dataset that is approximately 1 PB in size. The dataset is stored in Google Cloud Storage (GCS), and you need to ensure high performance during the model training process. The model training jobs require significant computational resources, and there’s a need to minimize both training time and cost. You also need to ensure that the solution can scale based on the data size and computational requirements. Which of the following configurations would be the most suitable for this ML training pipeline?

An insurance company uses several third-party enterprise applications that require special licenses. These licenses are not transferrable to the cloud. The third-party software vendor offers an option to pay a licensing fee based on how long you use the application in the cloud. What is this approach called?

Your organization has a suite of applications that have been containerized. You've been tasked to design a deployment strategy that leverages the power of Google Cloud's managed services, allows for auto-scaling based on demand, provides an ability to deploy updates with zero-downtime, and supports granular IAM roles and policies for your DevOps team. What would be your recommended approach?

An e-commerce company wants to migrate its payment microservice to Google Cloud. The service must be accessible globally with low latency, resilient to DDoS attacks, and must comply with strict security and audit requirements. The team has containerized the application and wants a solution that minimizes management overhead while still providing fine-grained traffic control and autoscaling. What should you do?

For this question, refer to the Cymbal Retail case study.

https://services.google.com/fh/files/misc/v6.1_pca_cymbal_retail_case_study_english.pdf

Cymbal is consolidating customer and product data from multiple databases into Google Cloud to enable unified analytics and personalization. The data includes personally identifiable information (PII) and must comply with regulatory requirements. Cymbal wants fine-grained access control, strong auditing, and consistent security policies across analytics and machine learning workloads, without slowing down development teams. Which approach best meets Cymbal’s data security and compliance requirements while supporting scalable analytics?