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| Section | Objectives |
|---|---|
| Topic 1: Designing and planning a cloud solution architecture | - Designing solution infrastructure that meets business requirements
|
| Topic 2: Ensuring solution and operations reliability | - Evaluating quality control measures - Assisting with support
|
| Topic 3: Designing for security and compliance | - Designing for security
|
| Topic 4: Analyzing and optimizing technical and business processes | - Analyzing and defining business processes
|
| Topic 5: Managing implementation | - Advising development/operation team(s) to ensure successful deployment
|
| Topic 6: Managing and provisioning a solution infrastructure | - Configuring network topologies
|
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NEW QUESTION # 166
For this question, refer to the Cymbal Retail case study. Cymbal has a centralized project that supports large video files for Vertex Al model training. Standard storage costs have suddenly increased this month, and you need to determine why. What should you do?
Answer: A
Explanation:
According to Google Cloud Storage documentation , the soft-delete policy is a feature enabled by default on new buckets (starting in 2024) to protect against accidental or malicious deletion. It retains deleted objects for a specified retention period (default is seven days). For workloads involving large files-such as the video files Cymbal uses for Vertex AI training-this policy can lead to unexpected cost spikes.
When soft-delete is active, objects that are deleted are not immediately removed from the billing cycle; instead, they continue to incur charges at the same rate as " live " objects until the retention period expires. In an AI training environment where datasets are frequently updated, replaced, or temporary " scratch " files are created and deleted, the volume of soft-deleted bytes can quickly double or triple the effective storage footprint.
Options B and D are incorrect because moving from dual-region or multi-region to a single region would actually reduce storage costs, as regional storage is priced lower. Option A is incorrect because disabling the policy would stop the retention of deleted bytes, thereby lowering the bill. Therefore, checking for an active or recently enabled soft-delete policy is the standard troubleshooting step for sudden increases in storage costs associated with high-churn, large-file datasets.
Topic 12, Altostrat Media Case Study
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.
NEW QUESTION # 167
Case Study: 9 - Helicopter Racing League
Company overview
Helicopter Racing League (HRL) is a global sports league for competitive helicopter racing. Each year HRL holds the world championship and several regional league competitions where teams compete to earn a spot in the world championship. HRL offers a paid service to stream the races all over the world with live telemetry and predictions throughout each race.
Solution concept
HRL wants to migrate their existing service to a new platform to expand their use of managed AI and ML services to facilitate race predictions. Additionally, as new fans engage with the sport, particularly in emerging regions, they want to move the serving of their content, both real-time and recorded, closer to their users.
Existing technical environment
HRL is a public cloud-first company; the core of their mission-critical applications runs on their current public cloud provider. Video recording and editing is performed at the race tracks, and the content is encoded and transcoded, where needed, in the cloud. Enterprise-grade connectivity and local compute is provided by truck-mounted mobile data centers. Their race prediction services are hosted exclusively on their existing public cloud provider. Their existing technical environment is as follows:
- Existing content is stored in an object storage service on their existing public cloud provider.
- Video encoding and transcoding is performed on VMs created for each job.
- Race predictions are performed using TensorFlow running on VMs in the current public cloud
provider.
Business requirements
HRL's owners want to expand their predictive capabilities and reduce latency for their viewers in emerging markets. Their requirements are:
- Support ability to expose the predictive models to partners.
- Increase predictive capabilities during and before races:
*Race results
*Mechanical failures
*Crowd sentiment
- Increase telemetry and create additional insights.
- Measure fan engagement with new predictions.
- Enhance global availability and quality of the broadcasts.
- Increase the number of concurrent viewers.
- Minimize operational complexity.
- Ensure compliance with regulations.
- Create a merchandising revenue stream.
Technical requirements
- Maintain or increase prediction throughput and accuracy.
- Reduce viewer latency.
- Increase transcoding performance.
- Create real-time analytics of viewer consumption patterns and engagement.
- Create a data mart to enable processing of large volumes of race data.
Executive statement
Our CEO, S. Hawke, wants to bring high-adrenaline racing to fans all around the world. We listen to our fans, and they want enhanced video streams that include predictions of events within the race (e.g., overtaking). Our current platform allows us to predict race outcomes but lacks the facility to support real-time predictions during races and the capacity to process season-long results.
For this question, refer to the Helicopter Racing League (HRL) case study. Helicopter Racing League (HRL) wants to create and update predictions on the results of the championships, with data that collects during the rages. HRL wants to create long-term Forecasts with the data from video collected both while taking (first processing) and during streaming for users. HLR want to exploit also existing video content that is stored in object storage with their existing cloud provider. On the advice of the Cloud Architects, they decided to use the following strategies:
1. Creating experimental forecast models with minimal code in the
powerful GCP environment, using also the data already collected
2. The ability and culture to develop highly customized models that are continuously improved with the data that it gradually collects. They plan to try multiple open source frameworks
3. To Integrate teamwork and create/optimize MLOps processes
4. To Serve the models in an optimized environment
Which of the following GCP services do you think are the best given these requirements?
Answer: D
NEW QUESTION # 168
For this question, refer to the Dress4Win case study.
You want to ensure Dress4Win's sales and tax records remain available for infrequent viewing by auditors for at least 10 years. Cost optimization is your top priority. Which cloud services should you choose?
Answer: C
Explanation:
Reference:
References: https://cloud.google.com/storage/docs/storage-classes
NEW QUESTION # 169
You are implementing a single Cloud SQL MySQL second-generation database that contains business-critical transaction data. You want to ensure that the minimum amount of data is lost in case of catastrophic failure. Which two features should you implement? (Choose two.)
Answer: B,C
Explanation:
Backups help you restore lost data to your Cloud SQL instance. Additionally, if an instance is having a problem, you can restore it to a previous state by using the backup to overwrite it.
Enable automated backups for any instance that contains necessary data. Backups protect your data from loss or damage.
Enabling automated backups, along with binary logging, is also required for some operations, such as clone and replica creation.
Reference: https://cloud.google.com/sql/docs/mysql/backup-recovery/backups
NEW QUESTION # 170
Your customer is moving their corporate applications to Google Cloud Platform. The security team wants detailed visibility of all projects in the organization. You provision the Google Cloud Resource Manager and set up yourself as the org admin. What Google Cloud Identity and Access Management (Cloud IAM) roles should you give to the security team'?
Answer: A
Explanation:
A is not correct because Project owner is too broad. The security team does not need to be able to make changes to projects.
B is correct because:
- Org viewer grants the security team permissions to view the organization's display name.
- Project viewer grants the security team permissions to see the resources within projects.
C is not correct because Org admin is too broad. The security team does not need to be able to make changes to the organization.
D is not correct because Project owner is too broad. The security team does not need to be able to make changes to projects.
https://cloud.google.com/resource-manager/docs/access-control-org#using_predefined_roles
NEW QUESTION # 171
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