Question 1

After analyzing a normalized FOCUS dataset, an analyst notices that a product team consistently has higher storage costs per GiB-hour than other teams. All teams use the same cloud provider and similar storage classes. What is the most data-driven next step to validate the cause?

Options :

A : Recommend the product team move to a cheaper provider without further analysis

B : Assume the team has inefficient storage practices and escalate to finance leadership

C : Compare only total monthly storage costs across teams to simplify the insight

D : Review resource-level metadata for differences in data access patterns or retention policies

Answer: D

Question 2

During a monthly FinOps review, the team notices sudden spikes in compute costs. Upon investigation, they find malformed usage entries in the dataset. Which of the following best aligns with FOCUS data quality assurance practices to proactively prevent such issues?

Options :

A : Wait for the cloud provider to fix the data before continuing analysis

B : Suppress anomalous values from reporting dashboards

C : Implement schema validation rules during data ingestion

D : Configure dashboards to ignore sudden cost changes under 10%

Answer: C

Question 3

A FOCUS Analyst prepares a report showing an unexpected 25% increase in storage costs across multiple business units. Which of the following is the most appropriate way to present these findings to both finance and engineering stakeholders?

Options :

A : Share raw UsageRecord data exports with all stakeholders for transparency

B : Present a summary of the CostRecords grouped by service and include potential usage drivers with supporting AllocationRecords

C : Provide only the overall cost delta and suggest teams investigate on their own

D : Create a heatmap of regional storage SKUs without contextual explanation

Answer: B

Question 4

A FinOps team is building an automated pipeline to ingest billing data from multiple cloud providers into their internal analytics platform. One challenge they face is inconsistent metadata fields (e.g., label vs. tag, project vs. account) across providers. To align with FOCUS standards and enable consistent reporting, which step should be prioritized during pipeline development?

Options :

A : Apply custom business logic after data is visualized

B : Build provider-specific dashboards to work around differences

C : Normalize incoming data to a common FOCUS schema before storage

D : Ignore provider-specific fields to avoid complexity in the pipeline

Answer: C

Question 5

A financial analyst is tasked with comparing team-level cloud spend across AWS, Azure, and Google Cloud using the FOCUS data model. Each provider has different usage types, pricing models, and tagging conventions. What is the most effective first step to address these multi-cloud allocation challenges using FOCUS-aligned practices?

Options :

A : Apply the cost allocation strategy used for the largest cloud provider to all others

B : Normalize metadata (e.g., labels and tags) across providers before applying allocation rules

C : Consolidate all data sources into a single spreadsheet and manually group costs

D : Convert all costs to a single currency and aggregate by service name

Answer: B