Your company has decided to make a major revision of their API in order to create better experiences for their developers. They need to keep the old version of the API available and deployable, while allowing new customers and testers to try out the new API. They want to keep the same SSL and DNS records in place to serve both APIs. What should they do?
Answer(s): D
The correct answer is D. Use separate backend pools for each API path behind the load balancer. This approach leverages the capabilities of a load balancer to intelligently route traffic based on the URL path.Here's a detailed justification:1. Maintaining Existing Infrastructure: The company aims to keep the same SSL and DNS records, indicating a desire to minimize disruption to existing users. Creating separate load balancers (Option A) would necessitate changes to DNS and SSL, violating this requirement. 2. Granular Control: By using separate backend pools, each pool is associated with either the old or the new API. This allows for independent management and scaling of each version. 3. Path-Based Routing: Modern load balancers can be configured to examine the path component of a URL (e.g., /v1/users for the old API, /v2/users for the new API). They then route requests to the corresponding backend pool. 4. No Client Modification: Option B, requiring old clients to use a new endpoint, is undesirable as it forces all clients to upgrade, rather than a phased rollout as indicated in the prompt. 5. Avoids Complex Forwarding: Option C, having the old API forward to the new API based on the path, is inefficient and creates unnecessary dependencies. The old API shouldn't need to be aware of the new API's logic. 6. Scalability and Isolation: Each backend pool can be independently scaled, meaning increased traffic to the new API will not impact the performance of the old API. This ensures better resource utilization and prevents cascading failures.Using path-based routing with separate backend pools is a common best practice in microservices and API management. It ensures seamless transitions, maintains existing client experience, and permits for independent management and scaling, all using a single, shared load balancer.Authoritative Links:1. Google Cloud Load Balancing: https://cloud.google.com/load-balancing 2. Backend services: https://cloud.google.com/load-balancing/docs/backend-service 3. URL maps (path based routing): https://cloud.google.com/load-balancing/docs/url-map
Your company plans to migrate a multi-petabyte data set to the cloud. The data set must be available 24hrs a day. Your business analysts have experience only with using a SQL interface. How should you store the data to optimize it for ease of analysis?
Answer(s): A
The optimal solution for storing a multi-petabyte dataset, requiring 24/7 availability and accessible via SQL by business analysts, is Google BigQuery (Option A). BigQuery is a fully-managed, serverless data warehouse designed for large-scale analytics. It inherently handles petabyte-scale datasets, ensuring high availability and scalability. Unlike Cloud SQL (Option B), which is a transactional database not optimized for large analytical workloads, BigQuery is built for analysis. While Cloud Storage (Option C) provides scalable object storage, it requires additional tools for querying the data with SQL. Google Cloud Datastore (Option D) is a NoSQL database not suitable for SQL-based analysis. BigQuery offers a standard SQL interface, enabling analysts familiar with SQL to readily query and analyze the data. Moreover, BigQuery's serverless nature removes operational overhead, allowing businesses to focus solely on data analysis. It optimizes query performance through various techniques like columnar storage and query optimization. Its cost-effectiveness makes it suitable for large datasets. Using other options would necessitate additional efforts in setting up infrastructure or translating SQL to other querying mechanisms. BigQuery, being designed for analytics with SQL support, perfectly aligns with the stated requirements for accessibility and analysis.Authoritative Links:Google BigQuery Documentation: https://cloud.google.com/bigquery/docs Google Cloud SQL Documentation: https://cloud.google.com/sql/docs Google Cloud Storage Documentation: https://cloud.google.com/storage/docs Google Cloud Datastore Documentation: https://cloud.google.com/datastore/docs
The operations manager asks you for a list of recommended practices that she should consider when migrating a J2EE application to the cloud. Which three practices should you recommend? (Choose three.)
Answer(s): C,D,E
The correct answer, CDE, aligns with best practices for migrating a J2EE application to the cloud, focusing on observability, automation, and a solid deployment pipeline. Option C, instrumenting with StackdriverDebugger (now Cloud Debugger), is crucial for gaining insights into application behavior and identifying performance bottlenecks post-migration, aligning with cloud observability principles. [ https://cloud.google.com/debugger/docs] . Option D, selecting an automation framework (like Terraform or Deployment Manager) for provisioning infrastructure, ensures consistent, repeatable deployments and reduces manual configuration errors, a hallmark of Infrastructure as Code (IaC). [ https://cloud.google.com/solutions/infrastructure-as-code] . Finally, option E, deploying a CI/CD tool (like Cloud Build) with automated testing, is essential for maintaining code quality and enabling rapid releases in a cloud environment. This practice embraces DevOps methodologies. [ https://cloud.google.com/build] . Options A, B, and F are not the most appropriate recommended practices for initial migration. While porting to App Engine (A) is a valid destination, it's a significant architectural change that isn't a prerequisite for cloud migration generally. Integrating Cloud Dataflow (B) is specific to batch or stream data processing needs and not a general migration practice. Migrating to NoSQL (F) is also a large change, not always necessary for all J2EE migrations. Therefore, the best initial focus is on monitoring, infrastructure management automation, and deployment automation, as suggested by CDE. These choices ensure stability and maintainability in the cloud.
A news feed web service has the following code running on Google App Engine. During peak load, users report that they can see news articles they already viewed. What is the most likely cause of this problem?
It's A. AppEngine spins up new containers automatically according to the load. During peak traffic, HTTP requests originated by the same user could be served by different containers. Given that the variable `sessions` is recreated for each container, it might store different data.The problem here is that this Flask app is stateful. The `sessions` variable is the state of this app. And stateful variables in AppEngine / Cloud Run / Cloud Functions are problematic.A solution would be to store the session in some database (e.g. Firestore, Memorystore) and retrieve it from there. This way the app would fetch the session from a single place and would be stateless.
An application development team believes their current logging tool will not meet their needs for their new cloud-based product. They want a better tool to capture errors and help them analyze their historical log data. You want to help them find a solution that meets their needs. What should you do?
Answer(s): C
The best course of action is C. Help them define their requirements and assess viable logging tools . Here's why:Option A is premature. While Stackdriver (now Cloud Logging) is a powerful tool, jumping directly to a specific solution without understanding the team's needs is inefficient. Option B, while useful, doesn't address the immediate need for tool selection. Option D assumes their current tool can even be upgraded to meet new cloud-specific requirements which is often not the case.Option C is the most logical starting point. It follows a structured problem-solving approach. By first defining requirements, you ensure that any solution is a good fit. This involves understanding: the volume of logs, required retention periods, desired granularity, analysis needs, alert configuration, and budget. Once these requirements are clear, viable logging tools can be assessed. This might include Cloud Logging, or other third-party logging solutions, considering integrations and manageability. This approach aligns with best practices for solution architecture and avoids selecting a solution based on assumptions. Starting with clear requirements is essential for effective cloud solution design. This approach provides a solid foundation for the application development team to choose the appropriate solution.Authoritative Links:Google Cloud Logging Overview: https://cloud.google.com/logging/docs/ Google Cloud Architecture Framework: Operational Excellence: https://cloud.google.com/architecture/framework/operational-excellence (This framework emphasizes understanding requirements before choosing solutions.)
You need to reduce the number of unplanned rollbacks of erroneous production deployments in your company's web hosting platform. Improvement to the QA/ Test processes accomplished an 80% reduction. Which additional two approaches can you take to further reduce the rollbacks? (Choose two.)
Answer(s): A,C
The question aims to identify strategies to further reduce unplanned rollbacks after initial QA process improvements. Option A, introducing a green-blue deployment model, significantly mitigates rollback risks. This pattern allows for deploying a new application version (blue) alongside the current production version (green). Traffic is gradually shifted to the new version, enabling real-time monitoring and immediate rollback to the green environment if issues arise. This minimizes downtime and risk of production failures. [ https://cloud.google.com/architecture/application-deployment-strategies#green-blue_deployments] Option C, fragmenting the monolithic platform into microservices, contributes by enabling independent deployments of smaller application components. Instead of deploying a large, complex system, developers can push updates to individual microservices, significantly reducing the scope and potential blast radius of any deployment failure. This makes identifying, containing, and rolling back issues faster and more targeted. [ https://cloud.google.com/architecture/microservices-on-gke] Option B, replacing the QA environment with canary releases, is not the best choice, as canary releases are meant for production environments and should follow a rigorous QA process; skipping this step increases risk. Options D and E, focusing on database changes, are also less effective since the problem lies in deployment procedures, not database technology, and they do not directly address the root cause of rollbacks. Therefore, a green-blue deployment model alongside a microservices architecture are the most effective options to further reduce unplanned production rollbacks following QA improvements.
To reduce costs, the Director of Engineering has required all developers to move their development infrastructure resources from on-premises virtual machines (VMs) to Google Cloud Platform. These resources go through multiple start/stop events during the day and require state to persist. You have been asked to design the process of running a development environment in Google Cloud while providing cost visibility to the finance department. Which two steps should you take? (Choose two.)
Answer(s): A,D
Here's a detailed justification for why options A and D are the correct choices, while B, C, E, and F are incorrect, based on the need for cost reduction, state persistence, and cost visibility in a GCP development environment.Correct Options Justification:A: Use the --no-auto-delete flag on all persistent disks and stop the VM: When a Compute Engine VMinstance is terminated (deleted), its attached persistent disks are, by default, also deleted (auto-delete). Using the --no-auto-delete flag when creating or updating a disk ensures that the disk is not automatically deleted when the VM is terminated or deleted. This allows developers to preserve the disk containing their development environment's state, while incurring storage costs (which are usually lower than compute costs), and it's a core aspect of persisting data across start/stop cycles. This flag is crucial for the described requirement of state persistence while reducing compute costs by stopping VMs. This strategy aligns perfectly with the scenario as the VMs need to be stopped and started during the day. For authoritative information, review the persistent disk documentation. https://cloud.google.com/compute/docs/disks/persistent-disks#delete_a_persistent_diskD: Use Google BigQuery billing export and labels to associate cost to groups: BigQuery billing export provides a detailed, granular view of Google Cloud costs. By applying labels to your Google Cloud resources (like the Compute Engine VMs), you can then filter and aggregate cost data in BigQuery based on these labels. This allows the finance department to see how much each development group or team is spending on Google Cloud resources, providing cost visibility. Labels can be assigned based on teams, projects, or any other logical grouping. This is essential for managing and controlling costs effectively. For more on using labels with billing, consult the following documentation: https://cloud.google.com/billing/docs/how-to/label-billing-data and https://cloud.google.com/billing/docs/how-to/export-data-bigqueryIncorrect Options Justification:B: Use the --auto-delete flag on all persistent disks and terminate the VM: This is incorrect because using the auto-delete flag will delete the persistent disks when the VM is terminated, leading to data loss. This directly contradicts the requirement for state persistence.C: Apply VM CPU utilization label and include it in the BigQuery billing export: While CPU utilization labels can be helpful for performance analysis, they are not specifically useful for cost allocation. Labels used for billing should be aimed at categorizing cost by groups or departments, not internal system metrics.E: Store all state into local SSD, snapshot the persistent disks, and terminate the VM: Local SSDs are ephemeral storage directly attached to the VM and are deleted when the VM is terminated. Relying on snapshots to maintain persistent storage would add extra overhead to the workflow, and it is redundant with the preferred approach of keeping persistent disks. Further, using the snapshots for the state is not directly aligned with the requirements of easy state persistence.F: Store all state in Google Cloud Storage, snapshot the persistent disks, and terminate the VM: Storing all state in GCS would complicate the developers workflow. While GCS is a reliable option for state storage, this is more complicated than storing state directly on the persistent disk as the workflow would be more complex. Snapshots of the persistent disks are redundant with the preferred approach of keeping persistent disks.
Your company wants to track whether someone is present in a meeting room reserved for a scheduled meeting. There are 1000 meeting rooms across 5 offices on 3 continents. Each room is equipped with a motion sensor that reports its status every second. The data from the motion detector includes only a sensor ID and several different discrete items of information. Analysts will use this data, together with information about account owners and office locations. Which database type should you use?
Answer(s): B
The correct answer is B. NoSQL . Here's why:The scenario involves a high volume of rapidly generated, time-series data from motion sensors (sensor ID and discrete information). This data does not require complex joins or transactional consistency typical of relational databases. Instead, efficient storage and retrieval based on the sensor ID and timestamp are crucial. NoSQL databases, specifically time-series databases, are optimized for such use cases. They can handle the ingestion speed and scalability requirements of 1000 sensors reporting every second. Furthermore, NoSQL databases often offer flexible schemas, allowing for potential additions of new data points from the sensors without requiring schema migrations. The analysts’ needs for information about account owners and office locations can be handled by joining the sensor data with separate, smaller datasets using other tools, or pre-aggregating specific data. Flat files are not suitable for querying or analyzing such a large stream of data. Relational databases would struggle to keep up with the write volume and lack the necessary scalability. Blob storage, while great for storing unstructured data, isn’t optimized for querying or time-series analysis.Here are some resources for further reading:Google Cloud Time Series Database: https://cloud.google.com/timeseries Understanding NoSQL Databases: https://aws.amazon.com/nosql/ (Concepts are similar across cloud providers) Choosing the Right Database: https://cloud.google.com/learn/what-is-database
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