Question 271
Your organization uses separate Google Cloud projects for shared services, development, testing, and production.
- The shared services project hosts your private CI/CD runners and a
central Artifact Registry
- The development, testing, and production projects host the GKE
clusters where applications are deployed.
You need to design an architecture that allows the CI/CD runners to connect to the GKE clusters and the clusters to pull images from Artifact Registry, all using private IP addresses. However, direct network traffic between the development, testing, and production environments must be strictly prohibited. What should you do?
Question 272
Case Study: 12 - Altostrat Media
Company Overview
Altostrat is a prominent player in the media industry, with an extensive collection of audio and video content that comprises podcasts, interviews, news broadcasts, and documentaries. Their success in delivering premium content to a diverse audience requires a content management system that can keep pace with the dynamic media landscape.
Solution Concept
Altostrat seeks to modernize its content management and user engagement strategies using Google Cloud's generative AI. They want a platform that empowers customers with personalized recommendations, natural language interactions and seamless self-service support.
Simultaneously, they want to drive revenue growth through dynamic pricing targeted marketing, and personalized product suggestions.
The seamless integration of AI-powered tools into the existing Google Cloud environment will enable Altostrat to efficiently manage their vast media library, enhance user experiences, and unlock new revenue streams. Google Cloud's generative AI will solidify their leadership in the media industry.
Existing Technical Environment
Altostrat's content management and delivery platform leverages GKE for scalability and high availability, essential for handling their vast media library. Their extensive media library spanning various documents, audio and video formats is stored in Cloud Storage. To gain valuable insights into user behavior, content consumption patterns, and audience demographics, Altostrat leverages BigQuery as their primary data warehouse. Additionally, they use Cloud Run functions for serverless execution of event-driven tasks such as video transcoding metadata extraction, and personalized content recommendations.
While Altostrat has made significant strides in cloud adoption, they also maintain some legacy on- premises systems for specific workflows like content ingestion and archival. These systems are slated for modernization and migration to Google Cloud in the near future. User management and authentication are currently handled through a combination of Google Identity and third-party identity providers. For monitoring and observability, Altostrat relies on a mix of native Google Cloud tools like Cloud Monitoring and open-source solutions like Prometheus, with alerts primarily delivered via email notifications.
Business Requirements
- Accelerate and enhance the reliability of operational workflows across all environments. [Google
Cloud + On-premises]
- Simplify infrastructure management for rapid application deployment.
- Optimize cloud storage costs while maintaining high availability and scalability for media
content.
- Enable natural language interaction with the platform with 24/7 user support.
- Automatically generate concise summaries of media content.
- Extract rich metadata from media assets using NLP and computer vision.
- Detect and filter inappropriate content.
- Analyze media content to identify trends and extract insights.
- Inform content strategy and decision making with data.
Technical Requirements
- Modernize CI/CD for containerized deployments with a centralized management platform.
- Secure, high-performance hybrid cloud connectivity for data ingestion.
- Provide scalable, performant kubernetes environments both on-premises and in the cloud.
- Optimize cloud storage costs for growing media volumes.
- Design AI-powered detection of harmful content.
- Ensure that AI systems are auditable and their decisions can be explained.
- Leverage LLMs and conversational AI for personalized experiences and content virality.
- Develop advanced chatbots with natural language understanding to provide personalized
assistance.
- Automated summarization for diverse media.
Executive Statement
At Altostrat, we are embracing the next frontier of artificial intelligence to revolutionize our content strategy. By harnessing the power of generative AI, we will create an unparalleled user experience by empowering our audience with intelligent toots for content discovery, personalized recommendations, and seamless interaction. Reliability and cost management are our top priorities. This strategic initiative will deepen engagement, foster customer loyalty, and unlock new revenue streams through targeted marketing and tailored content offerings. We see a future where Al-driven innovation is central to our business, leading to greater success for our company and delivering exceptional value to our customers.
For this question, refer to the Altostrat Media case study. Altostrat is concerned about sophisticated, multi-vector Distributed Denial of Service (DDoS) attacks targeting various layers of their infrastructure. DDoS attacks could potentially disrupt video streaming and cause financial losses. You need to mitigate this risk. What should you do?
Question 273
Case Study: 7 - Mountkirk Games
Company Overview
Mountkirk Games makes online, session-based, multiplayer games for mobile platforms. They build all of their games using some server-side integration. Historically, they have used cloud providers to lease physical servers.
Due to the unexpected popularity of some of their games, they have had problems scaling their global audience, application servers, MySQL databases, and analytics tools.
Their current model is to write game statistics to files and send them through an ETL tool that loads them into a centralized MySQL database for reporting.
Solution Concept
Mountkirk Games is building a new game, which they expect to be very popular. They plan to deploy the game's backend on Google Compute Engine so they can capture streaming metrics, run intensive analytics, and take advantage of its autoscaling server environment and integrate with a managed NoSQL database.
Business Requirements
Increase to a global footprint.
Improve uptime - downtime is loss of players.
Increase efficiency of the cloud resources we use.
Reduce latency to all customers.
Technical Requirements
Requirements for Game Backend Platform
Dynamically scale up or down based on game activity.
Connect to a transactional database service to manage user profiles and game state.
Store game activity in a timeseries database service for future analysis.
As the system scales, ensure that data is not lost due to processing backlogs.
Run hardened Linux distro.
Requirements for Game Analytics Platform
Dynamically scale up or down based on game activity
Process incoming data on the fly directly from the game servers
Process data that arrives late because of slow mobile networks
Allow queries to access at least 10 TB of historical data
Process files that are regularly uploaded by users' mobile devices
Executive Statement
Our last successful game did not scale well with our previous cloud provider, resulting in lower user adoption and affecting the game's reputation. Our investors want more key performance indicators (KPIs) to evaluate the speed and stability of the game, as well as other metrics that provide deeper insight into usage patterns so we can adapt the game to target users.
Additionally, our current technology stack cannot provide the scale we need, so we want to replace MySQL and move to an environment that provides autoscaling, low latency load balancing, and frees us up from managing physical servers.
For this question, refer to the Mountkirk Games case study. You need to analyze and define the technical architecture for the database workloads for your company, Mountkirk Games.
Considering the business and technical requirements, what should you do?
Question 274
You write a Python script to connect to Google BigQuery from a Google Compute Engine virtual machine. The script is printing errors that it cannot connect to BigQuery. What should you do to fix the script?
Question 275
Your customer wants to do resilience testing of their authentication layer. This consists of a regional managed instance group serving a public REST API that reads from and writes to a Cloud SQL instance.
What should you do?