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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q37-Q42):
NEW QUESTION # 37
Which of the following best describes the technical definition of Machine Learning?
- A. The application of pre-defined rules and algorithms to solve complex problems.
- B. A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.
- C. The use of computing technology to enable machines to gain cognitive intelligence.
- D. An approach to using increasing levels of intelligence to solve greater cognitive needs from unintelligent automation to autonomous business process.
Answer: B
Explanation:
Tom Mitchell's widely adopted formulation captures ML's essence: improvement on task T, measured by P, through experience E. This aligns with CPMAI's view that ML enables systems to learn from data and improve over time ("The ability of a machine to learn from data, improve with experience, and apply that learning to make predictions.") .
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NEW QUESTION # 38
You're working with an inexperienced team and this is all their first AI project. You're trying to work on a supervised learning binary classification problem to determine if emails are spam or not.
What is the best approach for this project?
- A. Pick an ensemble method since you're not sure which algorithm will perform best
- B. Pick a simple algorithm such as naive bayes
- C. Pick a simple algorithm such as Gaussian mixture
- D. Pick a neural network algorithm since you know this works well for supervised learning approaches
Answer: B
Explanation:
Naive Bayes classifiers are a family of "simple probabilistic classifiers based on Bayes' theorem with the
'naive' assumption of feature independence," making them fast to train and easy to interpret-ideal for teams new to AI tackling binary tasks like spam detection .
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NEW QUESTION # 39
In what way would you be using Generative AI if you used the results of the Generative AI solution to improve and accelerate your job?
- A. As an Augmented Intelligence system
- B. As a programmatic approach for automation
- C. Used for Hyperpersonalization
- D. As an autonomous system removing the human from the loop
Answer: A
Explanation:
The CPMAI Glossary defines Augmented Intelligence as "enhancing human abilities with AI," where AI outputs are leveraged by humans to improve decision-making or productivity. Using Generative AI to accelerate or improve your own work is precisely an Augmented Intelligence use case, distinct from full autonomy or simple automation .
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NEW QUESTION # 40
Your organization wants to use Generative AI. What are examples of when Generative AI can and should be used? (Select all that apply.)
- A. Human Augmentation
- B. Explainable Decision-support systems
- C. Data Augmentation for Training
- D. Content Generation
- E. Programmatic automated content generation
- F. Virtual Avatars and Characters
Answer: A,C,D,E,F
Explanation:
The CPMAI Glossary's entry for Generative AI highlights its use in creating new content (text, images, or code), enhancing training datasets via data augmentation, powering virtual avatars/characters, and serving as an Augmented Intelligence tool to boost human productivity . It also underpins programmatic content generation across multiple media types. Generative AI is not designed primarily for explainable decision- support interfaces.
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NEW QUESTION # 41
You've built your model and now need to see if it actually works as expected. In which phase of CPMAI is this done?
- A. Phase III
- B. Phase V
- C. Phase IV
- D. Phase VI
- E. Phase I
- F. Phase II
Answer: B
Explanation:
Phase V: Model Evaluation is dedicated to validating a trained model's performance against technical metrics and the business success criteria defined earlier. This phase encompasses tasks such as generating performance results, measuring KPIs, and deciding whether to retrain or proceed to operationalization.
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NEW QUESTION # 42
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