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Performance Monitoring & Optimization Flashcards

7 cards from real CDO practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.

Read the first 7 Performance Monitoring & Optimization flashcards as text
  1. A data analytics program costs $2 million and delivers $5 million in measurable benefits over the same period. What is its ROI?

    Answer: 150%

    ROI equals (5M - 2M) / 2M, which is 150%.

  2. Which metric best demonstrates the business value of a new data product rather than a vanity statistic?

    Answer: Measurable business outcomes attributable to decisions made with the product

    Value is shown by outcomes such as revenue gained or costs avoided, not raw activity counts.

  3. In a CMMI-style data management maturity model, at which level are processes measured and controlled using quantitative data?

    Answer: Level 4 – Quantitatively Managed

    Level 4 introduces statistical and quantitative control of processes, while Level 5 focuses on continuous improvement.

  4. In the Plan-Do-Check-Act (PDCA) cycle applied to data quality improvement, what happens during the "Check" phase?

    Answer: Results are compared against the planned objectives

    The Check phase evaluates whether the implemented change achieved the expected results.

  5. Which is the best-written key result for an OKR whose objective is "Make trusted data available faster"?

    Answer: Reduce average time-to-access for new datasets from 15 days to 5 days by Q3

    Strong key results are specific, measurable, and time-bound outcomes.

  6. Which DORA metric, increasingly applied to DataOps, measures the percentage of production deployments that cause a failure?

    Answer: Change failure rate

    Change failure rate tracks the share of deployments that lead to incidents or rollbacks.

  7. The CFO asks whether rising cloud data spend reflects waste or healthy growth in usage. Which metric best answers this?

    Answer: Unit cost, such as cost per query or per active data product user

    Unit economics normalize spend against usage, showing whether efficiency is improving as consumption grows.

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