Question 1

A FinOps team is building a custom integration to aggregate and analyze cloud spend from AWS, Azure, and GCP. They aim to create a unified dashboard for real-time cost insights across all cloud environments. The team needs to decide on the appropriate tools and configurations for data retrieval and aggregation to support both historical and real-time analysis. Which two of the following approaches should they take to ensure reliable, scalable cost data ingestion and analysis?

Options :

A : Set up AWS Lambda functions to retrieve cost data from AWS Cost Explorer API and store it in Amazon RDS for continuous historical and real-time analysis.

B : In GCP, configure the Billing Export to BigQuery for detailed cost information, then build custom SQL queries to extract real-time cost trends and export these summaries to a centralized reporting platform.

C : Leverage Amazon Athena with the AWS Cost and Usage Report (CUR) to perform SQL-based analysis on AWS costs directly, using Athena to feed this data into a centralized dashboard for cross-cloud cost analysis.

D : Use Azure Data Factory (ADF) to schedule and automate the ingestion of data from Azure Cost Management API, storing the data in a centralized data lake such as Azure Data Lake or AWS S3.

Answer: B,D

Question 2

Scenario: You are managing an application on Google Cloud Platform (GCP) that serves a global audience with both static and dynamic content. To improve user experience, you implemented a caching layer in Cloud Memorystore (Redis). However, as usage has increased, so has the cost of maintaining the Redis cache. You notice that the Redis cache hit rate is lower than expected, with many requests still reaching the backend, which drives up costs for both the cache and backend resources.What action would be the most effective way to optimize cache utilization and reduce costs?

Options :

A : Review and update cache keys to ensure that frequently requested data is cached effectively.

B : Increase the Redis instance size to improve cache performance and hit rates.

C : Implement a load balancer with round-robin routing to distribute requests evenly across instances.

D : Set a lower time-to-live (TTL) for cached items to keep the cache fresher.

Answer: A

Question 3

Your company uses Kubecost to manage and allocate Kubernetes cloud costs for multiple teams working within a shared cluster. Recently, the FinOps team noticed unexpected cost increases in certain namespaces. They want to identify the primary cost drivers and adjust settings to better control future expenses. Which of the following actions in Kubecost would provide the most comprehensive insights into cost spikes and help optimize resource usage?

Options :

A : Set up cost alerts to notify the FinOps team when a namespace exceeds its daily cost limit.

B : Examine the "Cost Allocation" view to analyze per-namespace costs and allocate a budget cap to prevent overspending.

C : Use the "Savings Recommendations" feature to identify underutilized resources across namespaces and adjust requests and limits accordingly.

D : Configure resource quotas on each namespace to restrict CPU and memory usage and prevent cost spikes.

Answer: C

Question 4

A cloud engineer is designing an infrastructure that supports auto-scaling for a web application experiencing fluctuating user demand throughout the day. Which of the following approaches will provide the most cost-efficient scaling solution across major cloud providers (AWS, GCP, Azure)?

Options :

A : Deploy auto-scaling with a combination of reserved instances for baseline capacity and spot instances for handling demand spikes.

B : Configure auto-scaling with a fixed number of reserved instances to handle all load scenarios, ensuring consistent resource availability.

C : Use only spot instances for all auto-scaling needs, allowing for the lowest possible costs by taking advantage of spare capacity.

D : Use auto-scaling with on-demand instances only to maintain flexibility in adjusting resources as demand fluctuates.

Answer: A

Question 5

When integrating third-party FinOps tools (e.g., CloudCheckr or Cloudability) with native cloud cost management platforms, what is the primary benefit of using these tools in tandem rather than relying solely on the cloud provider's native tools (e.g., AWS Cost Explorer)?

Options :

A : Improved real-time cost data due to faster API calls compared to native cloud tools

B : Ability to provide granular visibility into containerized workloads across multi-cloud environments

C : Exclusive access to vendor-specific discount programs not available through native tools

D : Automated cost forecasting with higher accuracy based on industry benchmarks

Answer: B