Practice Test Geeks(CAIC) Certified AI Consultant Practice Test
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Software Development Lifecycle Flashcards

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

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  1. Which SDLC methodology is MOST suitable for an AI R&D project where requirements are highly uncertain?

    Answer: Spiral with prototyping

    The Spiral model's iterative risk-driven approach accommodates the high uncertainty and frequent experimentation typical of AI R&D.

  2. A team wants to ensure their AI model can be rolled back quickly in production. The BEST DevOps practice to support this is:

    Answer: Blue-green deployment with model registry versioning

    Blue-green deployments allow instant traffic switching, and a model registry tracks versions so any prior version can be promoted without code changes.

  3. In AI system design, which principle does 'fail-safe defaults' BEST align with?

    Answer: When uncertain, fall back to a safe or conservative prediction

    Fail-safe defaults ensure that when a model is uncertain or encounters an out-of-distribution input, it defaults to the lowest-risk action.

  4. Which activity BEST represents the 'build' phase in an AI-specific CI pipeline?

    Answer: Training and packaging the model as a deployable artifact

    In an AI CI pipeline, the build phase produces a versioned, packaged model artifact (e.g., Docker image or model bundle) ready for testing.

  5. A product owner wants to prioritize AI backlog items. Which prioritization framework is MOST compatible with Agile AI development?

    Answer: RICE scoring (Reach, Impact, Confidence, Effort)

    RICE scoring quantifies business value and feasibility, making it well-suited for prioritizing AI features that vary widely in uncertainty and effort.

  6. What is the PRIMARY purpose of a staging environment in the AI SDLC?

    Answer: To mirror production conditions for final validation before release

    A staging environment replicates production infrastructure so that models and integrations can be validated under realistic conditions before go-live.

  7. In an AI project, 'data lineage' tracking is MOST useful for:

    Answer: Tracing how data was transformed from source to model input for debugging and compliance

    Data lineage records every transformation step, enabling teams to trace errors back to their source and satisfy regulatory traceability requirements.