Free AIB-C01 – AWS Certified AI Business Strategist Practice Test Question

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  Training the model by exposing it to three years of historical sales records

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  Using the already trained model to generate forecasts from new, current data

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  Cleaning and standardizing the historical transaction records before analysis

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  Choosing which algorithm the data science team will use to build the system

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  Let each region keep its own terminology to preserve local flexibility

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  Ban the use of technical terms in executive meetings altogether

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  Require every vendor to use the retailer's internally invented definitions

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  Adopt a shared AI vocabulary based on global frameworks such as ISO/IEC 23053, which establishes a common framework for AI concepts and terminology

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  Approved, because a business team is already using it

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  Under evaluation, because it is in a limited pilot and has not completed review for general use

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  Blocked, because any tool that handles creative work is prohibited

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  Unclassified, because pilot tools do not need governance oversight

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  Use AI to score and prioritize leads so sales representatives focus first on the accounts with the highest purchase propensity, and measure success by conversion rate

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  Use generative AI to publish a much larger volume of social media posts each week

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  Purchase AI tool licenses for every marketing employee so the whole team has access

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  Automate expense report approvals in the finance department to reduce administrative work

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  Cut over all traffic in a single weekend with no fallback, to keep the project short

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  Shut down the legacy assistant first so users are forced onto the new platform quickly

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  Run the new assistant in parallel, shift traffic in phases with validation at each step, and keep a tested rollback path

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  Migrate during the busiest week on purpose so the new platform is proven under maximum load immediately

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  Annual revenue growth, annual profit margin, and year-end market share

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  Representative adoption rate, percentage of opportunities where AI insights were used, and freshness of the underlying sales data

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  Number of press releases issued about the AI program

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  Total number of AI models the data science team has deployed this year

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  The advantage is permanent because competitors are legally barred from adopting similar forecasting

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  The advantage comes mainly from keeping the AI program secret so rivals cannot learn from it

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  A single forecasting model launch creates a moat that needs no further investment

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  Forecasting accuracy feeds inventory, pricing, and staffing decisions across the whole operation, so improvements compound into cost and service advantages that rivals must match across the entire chain, not just in one tool

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  Remove recruiters from the process for low-ranked candidates to keep screening fully automated

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  Instruct recruiters to accept the tool's rankings unless a candidate complains

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  Hide the confidence scores so recruiters are not influenced by them

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  Route low-confidence or borderline cases to human recruiters for review, with clear criteria for when the tool's output must not be acted on alone

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  Classify systems by the size of the team that built them

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  Classify systems by model type, with the newest models receiving the most oversight

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  Classify systems by the potential impact of their decisions on customers, finances, and legal obligations, applying the strongest controls to the highest-impact uses

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  Apply identical controls to all systems to keep the framework simple

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  Ask the assistant to be more careful by adding 'do not invent facts' to its instructions

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  Give the assistant access to more document types so it has broader context

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  Accept occasional invented figures as the cost of the productivity gains

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  Require the assistant to cite the source passage for every figure it reports, and mandate human verification of numbers before they are used in client-facing material

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  Data silos and inconsistent data standards are undermining data readiness for AI at scale

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  The forecasting model is too sophisticated for retail use cases

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  Regional managers lack sufficient AI literacy to interpret forecasts

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  The company does not have enough cloud storage capacity for national data

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  Monitor individual usage statistics and discipline adjusters who fall behind

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  Replace the tool with a simpler one that asks less of adjusters

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  Create a safe environment for experimentation: recognize learning, tolerate early mistakes, and involve adjusters in improving the tool

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  Keep the tool unchanged but give it a new name and relaunch it