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Your team is working on a new project for finding the most optimal flow of warehouse robots on the warehouse floor. Which type of machine learning approach would be most appropriate to pick for this problem?
A. Reinforcement Learning
B. Supervised Learning
C. All would work equally well
D. Unsupervised Learning
Answer: A
Explanation:
Reinforcement Learning (RL) is specifically designed for sequential decision-making tasks where an agent interacts with an environment and learns optimal behaviors through trial-and-error and reward signals.
Optimizing robot paths on a warehouse floor-deciding which movements lead to higher throughput-is a classic RL use case.
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NEW QUESTION # 93
When looking to implement AI to help break the Digital Transformation logjam, it's important to:
A. Invest in technology solutions to solve the problem
B. Start with basic automation and introduce AI only as needed
C. Figure out which pattern(s) of AI are needed to incorporate intelligent systems into non-digital processes
D. Have the right culture for AI
Answer: D
Explanation:
CPMAI emphasizes that the largest barriers to AI adoption are organizational and cultural, not technical. In Phase I's Assess Situation task group, teams inventory not only tools and data but also resources, constraints, and the cultural readiness for AI-ensuring leadership and staff embrace data-driven decision-making and experimentation. Without the right AI culture in place, even the best technology investments fail to deliver value .
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NEW QUESTION # 94
Your team is trying to determine which pattern best fits their AI problem. To do this the project team is running through the seven patterns of AI to figure out what pattern best applies to their problem.
Which of the following is the best approach?
A. Determine what you're trying to accomplish and see which pattern(s) of AI fit best.
B. When in doubt, don't apply any pattern of AI.
C. When in doubt, go with the Patterns & Anomalies pattern as all AI projects are about pattern matching.
D. Apply every pattern to the project.
Answer: A
Explanation:
CPMAI's Task: AI Pattern Identification requires teams to map their specific business objectives to the most appropriate one or more of the Seven Patterns of AI. Starting from "what are we trying to accomplish?" and then selecting the pattern(s) that align with those goals is the prescribed approach.
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NEW QUESTION # 95
You are working with a dataset that has a high number of dimensions. You're running into issues because some dimensions don't have enough real examples to properly train the systems for predictable results. What' s your best course of action?
A. Keep going as planned and the problem will eventually correct itself
B. Try to get additional data - at least 5 training examples for each dimension in the representation
C. Try to get additional information from project lead to see how many examples per dimension are needed
D. Try to improve the quality of your data through more preparation
Answer: B
Explanation:
CPMAI's Phase II: Data Understanding includes verifying that you have sufficient data volume for each feature to support reliable model training. The learning curve concept underscores that model performance improves with additional training examples. When dimensions are under-represented, the team must source or generate more data-aiming for a minimum number of examples per feature-to avoid underfitting and ensure stable predictions.
NEW QUESTION # 96
You have been tasked with creating a model that will recommend products based on what other customers have similarly purchased. Which algorithm is the best choice given this situation?
A. K Nearest Neighbor
B. K-means
C. Hyperpersonalization
D. Neural Network
Answer: A
Explanation:
CPMAI's Generic Task Group: Select Modeling Technique in Phase IV: Model Development outlines common cognitive algorithms. For recommendation systems-which rely on finding similar user or item profiles-the K-Nearest Neighbor algorithm is the canonical choice, using customer purchase vectors to locate "nearest neighbors." In contrast, K-means is purely unsupervised clustering, Neural Networks are more complex and not necessary for basic collaborative filtering, and Hyperpersonalization is an AI pattern, not an algorithm.
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NEW QUESTION # 97
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