AI Strategy & Implementation 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.
Read the first 7 AI Strategy & Implementation flashcards as text
Which AI governance framework element ensures that AI models perform consistently and fairly after deployment?
Answer: Ongoing monitoring and model performance auditing
Ongoing monitoring is essential to detect model drift, bias emergence, and performance degradation that can occur after initial deployment.
A retail company plans to use AI for demand forecasting. The MOST important data consideration is:
Answer: Ensuring historical sales data is complete, clean, and covers sufficient time periods
Demand forecasting requires comprehensive historical data to identify seasonal patterns and trends, making data completeness and coverage critical.
In the context of AI strategy, 'model drift' refers to:
Answer: A decline in model accuracy over time as real-world data patterns change
Model drift occurs when the statistical properties of the input data change over time, causing the model's predictions to become less accurate.
Which approach best describes 'AI democratization' within an enterprise?
Answer: Making AI tools and capabilities accessible to non-technical business users through low-code/no-code platforms
AI democratization enables broader organizational participation by giving non-technical users tools to leverage AI without deep programming knowledge.
When prioritizing AI use cases for implementation, organizations should give highest priority to cases that:
Answer: Have the highest ratio of business impact to implementation complexity and risk
Prioritization should favor use cases where business value is high and implementation risk is manageable, ensuring strong ROI and early wins.
A key difference between supervised and unsupervised learning in a business implementation context is:
Answer: Supervised learning requires labeled training data while unsupervised learning discovers patterns in unlabeled data
Supervised learning trains on labeled examples to predict outcomes, while unsupervised learning identifies hidden structures in data without predefined labels.
An AI consultant recommends starting with 'quick wins' in an implementation plan. The primary reason for this is to:
Answer: Build organizational confidence, demonstrate value, and secure continued investment in AI initiatives
Early successes build stakeholder trust and momentum, making it easier to secure resources and support for more ambitious AI projects.