Free CPMAI_v7 Exam Dumps

Question 16

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 a simple algorithm such as naive bayes
B. Pick a neural network algorithm since you know this works well for supervised learning approaches
C. Pick an ensemble method since you're not sure which algorithm will perform best
D. Pick a simple algorithm such as Gaussian mixture

Correct Answer:A
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 .

Question 17

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. Neural Network
D. Hyperpersonalization

Correct Answer:A
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.

Question 18

Your team is tasked with selecting an algorithm for a supervised learning classification project. Which algorithm might you choose?

A. Gaussian mixture
B. Q learning
C. K-nearest neighbor
D. K-means

Correct Answer:C
K-nearest neighbor (KNN) is a classic supervised classification algorithm that assigns a class label based on the majority label of the K closest training samples in feature space. In contrast, Gaussian Mixture Models and K-means are unsupervised clustering techniques, and Q-learning is a reinforcement-learning algorithm-not a supervised classifier .

Question 19

- [Data for AI]-
A team is getting ready to begin working on a ML project. They need to build a data preparation pipeline and someone on the team suggests they reuse the same pipeline they created for their last project.
What??s wrong with this suggestion?

A. Pipelines are model operationalization need specific.
B. Pipelines are pattern and model need specific.
C. Pipelines are pattern needs specific so as long as it??s the same pattern then you can reuse the pipeline.
D. There is no issu
E. Pipelines can be reused as needed between projects.

Correct Answer:B
In Phase III: Data Preparation, CPMAI specifies that data pipelines must be designed to address the specific modeling pattern and model requirements of the current project. Even if two projects use similar ingestion or cleaning steps, the pipeline must be tailored for the exact feature transformations, label mappings, and data schemas of the new model. Therefore, pipelines are pattern- and model-specific, and blindly reusing one from a prior project without adaptation will likely break downstream model training or inference requirements.