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55. Frage
Choose the CORRECT statement to use AI for product ideation.
Antwort: C
Begründung:
The correct answer is E. a, b and c only because all three statements describe valid ways AI supports product ideation. Product ideation is the process of discovering, developing, and evaluating new product ideas, features, improvements, or market opportunities. AI can support this process by analyzing large amounts of product, competitor, customer, market, and behavioral data.
Statement A is correct because AI can analyze competitor offerings, product descriptions, customer reviews, feature lists, pricing patterns, and market trends to identify what competitors are already providing. Statement B is also correct because AI can assist teams in generating new product ideas by finding unmet customer needs, emerging trends, feature gaps, and innovation opportunities. Statement C is correct because AI can quickly generate many possible ideas, compare alternatives, and help teams make better decisions using data- driven insights.
Since AI can support competitor analysis, idea generation, and rapid evaluation of product possibilities, the best answer is E. a, b and c only .
56. Frage
A model is trained using historical customer records where each record already contains the correct outcome, such as "churn" or "not churn." The model then predicts whether future customers are likely to churn. This is an example of ______.
Antwort: B
Begründung:
Supervised learning is used when a machine learning model is trained on labeled data. In this case, the historical customer records already include the correct outcome labels, such as "churn" or "not churn." The model learns the relationship between customer attributes and the known outcome, then applies that learned relationship to predict outcomes for new customers. This is a classic classification problem. Unsupervised learning is incorrect because it works with unlabeled data and is commonly used for clustering or discovering hidden patterns. Reinforcement learning is incorrect because there is no reward-based decision-making environment described. Generative learning is not the best answer because the task is prediction, not creating new content. Therefore, the correct answer is A. supervised learning .
57. Frage
Choose the BEST key components of workflow automation.
Antwort: C
Begründung:
Workflow automation in an AI or machine learning environment involves designing, running, tracking, and maintaining automated processes across the model lifecycle. Pipeline design and management is a key component because AI workflows often require structured pipelines for data ingestion, preprocessing, model training, validation, deployment, and updates. Pipeline execution and monitoring is also essential because automated workflows must be executed reliably, and teams need visibility into job status, failures, performance issues, and operational bottlenecks.
Model monitoring configuration is also a necessary component in AI workflow automation because deployed models must be observed for performance degradation, data drift, prediction quality, and operational reliability. Without monitoring, an automated AI workflow may continue producing poor or outdated results without detection. Since all three options support the implementation, operation, and governance of automated AI pipelines, the best and most complete answer is E. a, b, and c only .
58. Frage
Which of the following is NOT a common supervised learning model/algorithm?
Antwort: E
Begründung:
The correct answer is E. None of the above because K-nearest neighbors, random forest, and decision trees are all common supervised learning models or algorithms. Supervised learning uses labeled data to train a model so it can predict an output label or target value for new data.
K-nearest neighbors is a supervised learning algorithm commonly used for classification and regression. It predicts outcomes by comparing a new data point with the most similar labeled examples in the training data.
Random forest is also a supervised learning algorithm. It builds multiple decision trees and combines their results to improve prediction accuracy and reduce overfitting. Decision trees are supervised models that split data based on feature values to make classification or regression predictions.
Since options A, B, and C are all valid supervised learning algorithms, none of them is the correct example of a model that is NOT commonly supervised. Therefore, the correct answer is E. None of the above .
59. Frage
Which of the following is a step for the Value Engineering Framework?
Antwort: C
Begründung:
The correct answer is E. All of the above because the Value Engineering Framework focuses on identifying, delivering, and expanding measurable business value from data and AI initiatives. "Define value creation" is a key step because organizations must first clarify the business problem, expected outcomes, success metrics, stakeholders, and value drivers before investing in an AI solution.
"Realize value creation" is also correct because value must be converted from a planned objective into actual operational or financial impact. This may involve deploying the solution, measuring results, improving processes, reducing cost, increasing revenue, improving risk management, or enhancing customer outcomes.
"Scale value creation" is correct because successful AI initiatives should not remain limited to isolated pilots.
Organizations need to scale proven use cases across teams, business units, workflows, and enterprise platforms to maximize return on investment and long-term impact. Since all three options represent steps in value engineering, the best answer is E. All of the above .
60. Frage
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