Free FinOps FinOps-Certified-Engineer Exam Questions

Question 1

Scenario: A gaming company operates a serverless backend on Google Cloud Platform (GCP) to support a global multiplayer game. They use Google Cloud Functions to handle API requests from players and store game state data in Google Cloud Firestore. Recently, costs have surged due to increased player interactions, leading to frequent Cloud Function invocations and high Firestore read/write charges. The development team wants to optimize the serverless architecture to reduce these expenses while maintaining real-time game response times for a smooth player experience.What is the most cost-effective approach to optimize the architecture while meeting the performance requirements?

A : Switch Cloud Functions to a higher memory allocation to reduce execution time, and add Firebase Realtime Database for game state storage.

B : Utilize Google Cloud Pub/Sub to batch API requests before sending them to Cloud Functions and consolidate Firestore reads with data caching.

C : Migrate from Cloud Functions to Google App Engine and maintain the same Firestore configuration for storage.

D : Reduce Firestore costs by switching to on-demand mode and decreasing Cloud Functions’ maximum timeout to prevent long-running calls.

Answer: B

Question 2

Scenario:You are a FinOps engineer for a rapidly growing e-commerce platform. Your organization experiences fluctuations in cloud spending due to seasonal sales and marketing campaigns. Recently, you implemented machine learning (ML) models for forecasting cloud costs, which use historical spending data, usage patterns, and business activity data. However, your stakeholders are concerned about the accuracy of these forecasts, especially for peak periods.Question:What actions should you take to improve the accuracy of cost forecasts using ML models in this scenario?

A : Use a simpler linear model with fewer data inputs to make the model more understandable for stakeholders.

B : Incorporate additional data features like seasonal indicators, campaign schedules, and product release dates to refine the model’s inputs.

C : Set a conservative cost buffer in the forecasted budget to compensate for any potential inaccuracies in the model’s predictions.

D : Regularly retrain the ML model on recent data to ensure that it adapts to new spending patterns and usage trends.

Answer: B,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?

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

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?

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 5

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?

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