Free IMA CMA Part 1 (Financial Planning, Performance, and Analytics) Technology and Analytics Practice Questions
Technology and Analytics on CMA Part 1 covers information systems (ERP, data warehouses), data governance and quality, technology-enabled finance (robotic process automation, AI, blockchain), data analytics (descriptive, diagnostic, predictive, prescriptive), business intelligence, and data visualization. Added to the IMA Content Specification Outline in 2020 and now contributes 15% of Part 1.
Sample Questions
Question 1
Easy
Which of the following best describes a data lake?
Solution
A is correct. A data lake stores data in its raw, native format (structured tables, semi-structured logs, unstructured documents, images, audio, etc.) and applies a schema only when the data is read for analysis (schema-on-read). This flexibility lets analytics and data-science teams ingest new sources quickly and decide later how to model them.
Question 2
Medium
A finance leader is selecting candidate processes for robotic process automation (RPA). Which process is the strongest fit?
Solution
A is correct. RPA excels at processes that are high-volume, rule-based, repetitive, and operate on structured data from stable interfaces. Intercompany reconciliation fits all four criteria: the rules for matching debits and credits between affiliated entities are deterministic, the source data lives in ledger systems with stable formats, and the volume of transactions makes manual matching tedious. Processes that require judgment, negotiation, strategic framing, or unstructured investigation are poor RPA candidates because the underlying logic cannot be reduced to deterministic rules.
Question 3
Hard
A CFO wants to identify which subscription customers are most likely to cancel next quarter and have those accounts automatically assigned to retention specialists without manual triage. Which combination of capabilities best supports this objective?
Solution
B is correct. The objective combines two distinct requirements. First, identifying customers likely to cancel is a forward-looking probability estimate, which is the definition of predictive analytics: a model is trained on historical churn data to score each active customer's likelihood of cancellation. Second, automatically routing flagged accounts to retention specialists without manual triage is a rule-based, repetitive workflow that is well suited to RPA or a workflow engine. Together, the predictive model produces the signal and the RPA layer executes the action, which is the canonical pattern for technology-enabled finance and operations transformation.