Question 1

Your company has recently expanded its cloud footprint to include multiple Azure and AWS accounts across different departments. The FinOps team has decided to implement CloudCheckr for unified cost analysis and optimization. However, the team is unsure how to handle the differences in reserved instance (RI) purchasing options between Azure and AWS to maximize cost savings across both platforms. What would be the best approach to optimize reserved instance purchases using CloudCheckr in a multi-cloud setup?

Options :

A : Use CloudCheckr's Reserved Instance (RI) Planner to evaluate AWS and Azure purchases separately to account for different pricing and terms.

B : Allocate all resources under a single department’s account to simplify RI purchases across both platforms.

C : Set up cross-cloud reservations using CloudCheckr to ensure RI discounts apply across both Azure and AWS workloads.

D : Rely on AWS Savings Plans as a substitute for RIs in both AWS and Azure to avoid dealing with separate RI management processes.

Answer: A

Question 2

Which of the following strategies is the most effective when developing a budget for a multi-cloud environment that has unpredictable usage spikes?

Options :

A : Allocating a fixed budget for each cloud service provider based on past expenditure

B : Assigning equal budget limits to each cloud environment regardless of service type or region

C : Utilizing a single budget management tool for all cloud environments without considering differences in provider pricing models

D : Implementing a dynamic budgeting model with continuous reallocation based on real-time usage data

Answer: D

Question 3

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 4

Which of the following best describes an advanced tagging strategy that improves cost allocation accuracy for multi-cloud environments?

(Select two correct answers)

Options :

A : Assign dynamic tags based on workload usage patterns and auto-provisioning.

B : Implement cross-cloud tagging only for services that generate more than 10% of total costs.

C : Rely solely on vendor-managed default tags for cost tracking.

D : Use a single global tag naming convention across all cloud platforms and services.

E : Use tags to differentiate between production, development, and testing environments.

Answer: D,E

Question 5

A multinational enterprise has adopted a hybrid strategy, using both cloud-native FinOps tools (e.g., AWS Cost Explorer and Azure Cost Management) and third-party tools (e.g., CloudCheckr) for cost analysis across multiple cloud providers. The FinOps team wants to enhance cost visibility and identify underutilized resources across the hybrid cloud environment. However, they are facing data discrepancies between the cloud-native tools and CloudCheckr. Which two actions would best help the team resolve these discrepancies and optimize cloud resource usage?

Options :

A : Rely solely on CloudCheckr for all cost analysis needs to avoid discrepancies caused by using multiple tools.

B : Standardize cost allocation tags across all cloud environments to ensure consistent data mapping between cloud-native tools and CloudCheckr.

C : Integrate CloudCheckr data with each cloud-native tool to cross-reference cost and utilization metrics, helping identify discrepancies and verify data accuracy.

D : Use AWS Cost Explorer and Azure Cost Management only for detailed utilization data, while relying on CloudCheckr for aggregated cost visibility across all clouds.

Answer: B,D

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