Timed Mode Diagnostic Set – Google Certified Professional Cloud Architect

You are leading a cloud migration for a company with a rapidly growing dataset exceeding multiple petabytes.

The dataset must be available at all times and support SQL-based analytics for business reporting.

The company requires a fully managed solution that can scale automatically and minimize operational overhead.

Which solution should you implement?

You work for a major energy provider that manages thousands of smart meters installed in homes and commercial buildings across various regions. These meters continuously capture real-time data such as power usage, voltage levels, and equipment health, and they send updates every few seconds.

You’re responsible for designing a system that can collect and store this constant flow of data. The operations team also needs to link the meter data with customer billing records and regional service zones to monitor usage patterns and detect anomalies.

What type of database architecture is best suited to handle this use case?

You are working for a large e-commerce company that is recently migrating its infrastructure to the cloud. The company needs to optimize its data processing workflows and better understand customer behavior in real-time. The company’s data stream includes transactional data from customers and inventory updates, which require analysis as soon as they arrive. Additionally, some of the data needs to be processed in batches for monthly reports. Your team is looking for a solution that can handle both live-stream processing and periodic batch jobs efficiently.

Which Google Cloud service should you use to analyze the data stream and process both batch and live data?

You are working at a global logistics company that handles massive amounts of data for tracking shipments, managing inventory, and optimizing routes. As the company expands, you foresee a sharp increase in the size and complexity of the Apache Spark and Hadoop jobs that are critical for processing logistics data. You need a cloud-based solution to scale these workloads quickly without having to spend time on managing clusters or rewriting your existing code. Your goal is to reduce the operational complexity and minimize the amount of work required to handle this growth.

Which Google Cloud service should you use to run your Apache Spark and Hadoop jobs with the least amount of operational work and code changes?

You manage a global SaaS platform serving thousands of customers across various industries. As the company expands, you’ve decided to outsource parts of your operations functions to a third-party provider. Your team frequently pushes new features and fixes to the cloud-based application. You need a solution that enables developers to stage new application versions quickly and allows the outsourced operations team to promote those versions to production autonomously. The goal is to minimize the operational overhead and ensure that the new versions can be deployed smoothly and efficiently, without requiring constant intervention from your internal teams.

Which Google Cloud service should you migrate to to streamline the version deployment process and minimize operational overhead?

Your company is migrating its on-premises application to Google Cloud Platform (GCP). The business stakeholders want to ensure that the transition causes as little disruption as possible for end-users. Additionally, the security team has strict policies about managing user credentials, and they require that passwords not be stored within Google Cloud.

Which authentication method should be used to meet these requirements?

You manage a Google Cloud project leveraging BigQuery for large-scale data analytics. Cloud VPN establishes a secure connection between your on-premises system and Google Cloud. Additionally, the team has considered using Google Cloud Identity to secure access management.

The security team is concerned about the risks of data exfiltration and external unauthorized access, particularly in the event of compromised user credentials or accidental misconfigurations.

What would be the best solution to mitigate these risks?

You are working on a real-time recommendation engine for an e-commerce platform. The engine must process large volumes of data generated from user interactions, such as product views, searches, and purchases. The data is ingested continuously at 10,000 events per minute, with occasional bursts of up to 12,000 events per second. This data must be stored and made available for future predictive analysis and personalized recommendations.

Which GCP storage infrastructure should you choose to handle this high-throughput and real-time data while supporting analytical workloads?

You are tasked with storing critical corporate data in Google Cloud Storage buckets. You also plan to use Cloud Data Loss Prevention (Cloud DLP) to monitor and protect sensitive information.

The data is updated frequently, but previous versions must be retained for future reference. A complete history of modifications must be maintained, and the ability to quickly recover from unintended edits or deletions is essential.

Which Cloud Storage feature should you enable to meet these requirements?

You are developing a complex cloud-based platform with multiple services, where each service needs to securely access a central database. The platform comprises numerous independent components, each requiring the database credentials to function. You need to ensure that the credentials are stored in a secure manner while still being easily accessible by the components that need them.

Which solution will best meet the security requirements for storing the database credentials?