During a high traffic portion of the day, one of your relational databases crashes, but the replica is never promoted to a master. You want to avoid this in the future. What should you do?
Answer(s): D
The correct answer is D. Implement routinely scheduled failovers of your databases . Here's why:The scenario describes a failure during a high-traffic period where a database replica failed to automatically take over as the master after the original master crashed. This indicates a potential issue with the automatic failover mechanism, not necessarily a problem with database size or underlying technology. Option A, switching to a different database entirely, is a drastic and often unnecessary measure, adding considerable cost and complexity. Option B, increasing instance sizes, might improve performance but won't directly address the failed failover. Option C, creating more frequent snapshots, is for backups and restoration, not active failover during a live outage.Regularly scheduled failovers, as suggested in option D, will ensure that the replica is actively tested and prepared to become the master. By routinely practicing the failover process, you can identify and rectify any potential configuration problems or issues that might prevent the automatic promotion during a real crash. This approach also gives teams practice and familiarity with the failover process. This proactive approach to ensuring high availability through testing and verification directly addresses the problem of a replica failing to take over.Authoritative Links:Google Cloud Documentation on Failover: https://cloud.google.com/sql/docs/mysql/high-availability#failover (This link specifically discusses failover in Cloud SQL, but the concept is broadly applicable to other relational databases) Google Cloud Architecture Center - High Availability: https://cloud.google.com/architecture/high-availability (General best practices for high availability on GCP.) Google Cloud Documentation on Database Replication: https://cloud.google.com/sql/docs/mysql/replication (Understanding replication is key to understanding failover in database systems.)By implementing routinely scheduled failovers, you're proactively verifying your failover configuration,mitigating the risk of a failure during a crucial time. This is a best practice for ensuring high availability and business continuity.
Your organization requires that metrics from all applications be retained for 5 years for future analysis in possible legal proceedings. Which approach should you use?
The correct answer is D. Configure Stackdriver Monitoring for all Projects, and export to Google Cloud Storage . Here's why:Option D aligns with the requirement for long-term metric retention (5 years) for legal purposes. Google Cloud Storage (GCS) offers durable and cost-effective storage suitable for archiving data for extended periods. Stackdriver (now Google Cloud Monitoring) collects metrics, and exporting them to GCS provides a robust solution to fulfill the organization's data retention policy. GCS buckets can be configured with specific retention policies to ensure the data remains available as required.Option A is incorrect. While granting the security team access to logs is important, it doesn't address the metric retention requirement. Logs and metrics are different data types. Logs may contain information related to an application, but metrics quantify resource usage or performance. Access control is not a solution for retention.Option B, exporting to BigQuery, is a valid way for analysis, but it's primarily meant for querying and not long-term archival. While BigQuery is great for data exploration, it's more costly than GCS for holding data over 5 years. Additionally, BigQuery does not automatically archive the data, requiring configuration for retention.Option C uses default Stackdriver Monitoring retention policies. These are typically much shorter than 5 years and are not meant for legal evidence. They primarily serve operational monitoring needs. Therefore, it is unsuitable for the stated requirement.In summary, exporting the metrics to a persistent and cost-effective storage service like GCS, with specific retention policies configured, is the correct approach for meeting the organization's requirement for 5-year metric retention.Authoritative links:Google Cloud Storage: https://cloud.google.com/storage Google Cloud Monitoring: https://cloud.google.com/monitoring Exporting Metrics from Cloud Monitoring: https://cloud.google.com/monitoring/exporting-metrics Cloud Storage Retention policies: https://cloud.google.com/storage/docs/bucket-lock
Your company has decided to build a backup replica of their on-premises user authentication PostgreSQL database on Google Cloud Platform. The database is 4 TB, and large updates are frequent. Replication requires private address space communication. Which networking approach should you use?
Answer(s): A
The correct answer is A. Google Cloud Dedicated Interconnect.Here's why:Dedicated Interconnect offers a direct, private, and high-bandwidth connection between your on-premises network and Google Cloud. This is crucial for replicating a large, frequently updated database (4TB) where consistent and reliable data transfer is paramount. The private connectivity ensures secure communication and avoids traversing the public internet, reducing latency and improving performance. The high bandwidth provided by Dedicated Interconnect can accommodate the frequent, large updates without bottlenecks. While options like Cloud VPN (B and D) offer secure connections, they are typically less performant and have higher latency due to internet routing. Option C, a NAT and TLS gateway, adds complexity and isn’t the recommended approach for database replication. For a large database, the reliability and performance of a dedicated link greatly outweigh the cost considerations of Dedicated Interconnect, making it the most suitable solution.Key concepts:Dedicated Interconnect: Provides private, high-bandwidth connectivity. It is a physically direct connection between Google's network and your on-premises network. Cloud VPN: Creates an encrypted tunnel across the public internet, offering secure connectivity but with lower performance than Dedicated Interconnect. Latency: The time delay before a data transfer begins, a critical factor for real-time applications. Bandwidth: The data transfer rate, impacting how quickly large amounts of data can be moved. Private Address Space: Network addresses not accessible through the public internet, vital for security.Authoritative Links:Google Cloud Dedicated Interconnect: https://cloud.google.com/network-connectivity/docs/interconnect/concepts/dedicated-interconnect Google Cloud VPN: https://cloud.google.com/network-connectivity/docs/vpn/concepts/overview Choosing a Network Connectivity Product: https://cloud.google.com/network-connectivity/docs/overview/choose-connectivity-product
Auditors visit your teams every 12 months and ask to review all the Google Cloud Identity and Access Management (Cloud IAM) policy changes in the previous 12 months. You want to streamline and expedite the analysis and audit process.What should you do?
Answer(s): B
The best solution is B. Enable Logging export to Google BigQuery and use ACLs and views to scope the data shared with the auditor. This approach offers the most efficient and secure way to provide auditors with the necessary information while minimizing administrative overhead. Exporting Cloud Logging data to BigQuery allows for powerful querying and analysis using SQL, which is ideal for extracting specific IAM policy changes over a 12-month period. BigQuery's access control lists (ACLs) and view functionality enable you to precisely control what data the auditors can access, ensuring they only see relevant IAM logs and not other sensitive information. This granular control is crucial for maintaining security and data privacy. The solution is scalable, able to handle large volumes of log data. Moreover, BigQuery is designed for data analysis and reporting, aligning perfectly with the requirements of an audit. Options A, C, and D are less optimal. Stackdriver alerts (A) are reactive and don't provide historical analysis. Cloud SQL (C) isn't designed for log analysis, and transferring data adds unnecessary complexity. GCS exports (D) would necessitate further processing to analyze log data.Authoritative Links:Export logs to BigQuery: https://cloud.google.com/logging/docs/export/configure_export_v2#bigquery-export BigQuery Access Control: https://cloud.google.com/bigquery/docs/access-control BigQuery Views: https://cloud.google.com/bigquery/docs/views
You are designing a large distributed application with 30 microservices. Each of your distributed microservices needs to connect to a database back-end. You want to store the credentials securely. Where should you store the credentials?
Answer(s): C
The correct answer is C. In a secret management system. Storing credentials directly in source code (A) is a major security vulnerability, as it exposes them to anyone with access to the codebase. Environment variables (B), while better than source code, can still be insecure, especially if not properly managed or if access to the environment is compromised. Configuration files with restricted ACLs (D) also present challenges because ACL management can be complex and prone to human error, and the files could still be accidentally exposed. A dedicated secret management system (C) offers a centralized, secure, and auditable way to store and access sensitive credentials. These systems typically provide encryption at rest and in transit, fine-grained access controls, versioning, and audit logs, allowing for better security and compliance. Secret management systems also integrate with applications, allowing them to retrieve credentials at runtime without having them hardcoded or stored in other easily compromised locations. Google Cloud Platform offers Secret Manager, a service designed explicitly for this purpose, providing robust security and simplified management. Utilizing a dedicated secret management system aligns with the best security practices for cloud applications.Authoritative Links:Google Cloud Secret Manager Documentation: https://cloud.google.com/secret-manager/docs NIST Special Publication 800-63B: https://pages.nist.gov/800-63-3/ (Relevant for authentication and secrets management) OWASP Top 10: https://owasp.org/Top10/ (Specifically mentions Insecure Cryptographic Storage)
A lead engineer wrote a custom tool that deploys virtual machines in the legacy data center. He wants to migrate the custom tool to the new cloud environment. You want to advocate for the adoption of Google Cloud Deployment Manager. What are two business risks of migrating to Cloud Deployment Manager? (Choose two.)
Answer(s): E,F
The correct answer is EF because they represent genuine business risks associated with adopting Cloud Deployment Manager (CDM).E: Cloud Deployment Manager can be used to permanently delete cloud resources: This is a significant risk. Infrastructure-as-code tools like CDM, while powerful for automation, also carry the potential for accidental or malicious deletion of critical resources. Misconfigurations in CDM templates or unintended executions could lead to data loss and service disruptions, causing significant business impact. It requires careful design, testing, and access control to mitigate this risk. This is not a problem unique to Deployment Manager, it is inherent to infrastructure-as-code tools.F: Cloud Deployment Manager only supports automation of Google Cloud resources: This represents a business risk when a company has assets beyond Google Cloud. If the company maintains resources in other cloud providers or on-premise, CDM cannot directly manage them. This could result in a more fragmented operational model. For instance, the legacy datacenter mentioned in the problem statement could not be managed using CDM alone and would likely require separate management processes, negating some of the benefits of moving to a unified system for resource provisioning.Option C, while plausible, is more of a temporary obstacle (and mitigatable through training) than an actual business risk if addressed adequately. Option D is a necessary requirement for CDM and not a risk. Option B, while a general concern with any cloud service is still not a specific business risk of using CDM as its unlikely that Google would deprecate the service as its part of their core offering. Option A is irrelevant as the coding language is technical and doesn't represent a business risk. Therefore, E and F most directly relate to business level concerns regarding resource destruction and lack of multi-cloud support.For further research, you can refer to the official Google Cloud documentation:Cloud Deployment Manager Overview: https://cloud.google.com/deployment-manager/docs/overview Best practices for Deployment Manager: https://cloud.google.com/deployment-manager/docs/best-practices
A development manager is building a new application. He asks you to review his requirements and identify what cloud technologies he can use to meet them. The application must: 1. Be based on open-source technology for cloud portability 2. Dynamically scale compute capacity based on demand 3. Support continuous software delivery 4. Run multiple segregated copies of the same application stack 5. Deploy application bundles using dynamic templates 6. Route network traffic to specific services based on URL Which combination of technologies will meet all of his requirements?
The correct answer is A because it provides a comprehensive solution that addresses all the stated requirements. Google Kubernetes Engine (GKE) leverages open-source Kubernetes, fulfilling the portability mandate. GKE's inherent capabilities automatically scale compute resources based on demand, handling dynamic workloads effectively. Jenkins, a widely adopted open-source CI/CD tool, facilitates continuous software delivery pipelines. Kubernetes namespaces within GKE enable the creation of isolated application environments, supporting multiple segregated instances of the application stack. Helm, a package manager for Kubernetes, allows for the deployment of application bundles using reusable charts and dynamic templates. While GKE doesn't directly handle URL-based traffic routing, this can be achieved within the Kubernetes context using Ingress controllers, often configured with specialized add-ons like NGINX Ingress.Option B is incorrect as it lacks the tooling for CI/CD and dynamic template deployments. Option C is inadequate because Cloud Deployment Manager lacks support for dynamic traffic routing and CI/CD pipelines. Option D omits Helm, which is crucial for managing application bundles with dynamic templates. Therefore, the combination of GKE, Jenkins, and Helm offers the most complete solution by addressing all functional needs.Here are authoritative links for further research:Google Kubernetes Engine: https://cloud.google.com/kubernetes-engine Jenkins: https://www.jenkins.io/ Helm: https://helm.sh/Kubernetes Ingress: https://kubernetes.io/docs/concepts/services-networking/ingress/
You have created several pre-emptible Linux virtual machine instances using Google Compute Engine. You want to properly shut down your application before the virtual machines are preempted. What should you do?
The correct answer is C. Google Compute Engine allows you to specify a shutdown script that executes when a virtual machine instance is about to be terminated, including during preemption. This is achieved by using instance metadata. The metadata key shutdown-script accepts the script's content as its value. When the instance is preempted or shut down, the script defined in this metadata entry is executed, allowing for graceful shutdown procedures like saving application state, closing connections, or sending notifications. Option A is incorrect because the described location is not used for Compute Engine specific shutdown scripts, instead it's a location for runlevel scripts, which are unrelated to the shutdown event caused by GCE. Options B and D are also incorrect; while xinetd can manage services, it is not the standard method to implement a shutdown script for GCP preemptible VMs. Furthermore, the shutdown-script-url metadata key refers to a URL containing the script not the URL of a service. Instead, shutdown-script directly takes the script's content. Using metadata is the prescribed and supported way to execute scripts during the GCE shutdown process. Metadata is a flexible mechanism to configure instances; it is a simple approach and it's directly integrated into the Compute Engine infrastructure.Further research can be done here: https://cloud.google.com/compute/docs/metadata/startup-shutdown-scriptshttps://cloud.google.com/compute/docs/instances/preemptible
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90% of questions was there but i failed the exam, i marked the answers as per the guide but looks like they are not accurate , if not i would have passed the exam given that i saw about 45 of 50 questions from dump