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CertNexus AIP-210 Prüfungsplan:

Thema	Einzelheiten
Thema 1	Identify potential ethical concerns Analyze machine learning system use cases
Thema 2	Train, validate, and test data subsets Training and Tuning ML Systems and Models
Thema 3	Address business risks, ethical concerns, and related concepts in training and tuning Work with textual, numerical, audio, or video data formats
Thema 4	Recognize relative impact of data quality and size to algorithms Engineering Features for Machine Learning
Thema 5	Understanding the Artificial Intelligence Problem Analyze the use cases of ML algorithms to rank them by their success probability

CertNexus Certified Artificial Intelligence Practitioner (CAIP) AIP-210 Prüfungsfragen mit Lösungen (Q93-Q98):

93. Frage
A market research team has ratings from patients who have a chronic disease, on several functional, physical, emotional, and professional needs that stay unmet with the current therapy. The dataset also captures ratings on how the disease affects their day-to-day activities.
A pharmaceutical company is introducing a new therapy to cure the disease and would like to design their marketing campaign such that different groups of patients are targeted with different ads. These groups should ideally consist of patients with similar unmet needs.
Which of the following algorithms should the market research team use to obtain these groups of patients?

A. k-means clustering
B. Naive-Bayes
C. Logistic regression
D. k-nearest neighbors

Antwort: A

Begründung:
Explanation
k-means clustering is an algorithm that should be used by the market research team to obtain groups of patients with similar unmet needs. k-means clustering is an unsupervised learning technique that partitions the data into k clusters based on the similarity of the features. The algorithm iteratively assigns each data point to the cluster with the nearest centroid and updates the centroid until convergence. k-means clustering can help identify patterns and segments in the data that may not be obvious or intuitive. References: [K-means clustering - Wikipedia], [How to Run K-Means Clustering in Python]

94. Frage
You have a dataset with many features that you are using to classify a dependent variable. Because the sample size is small, you are worried about overfitting. Which algorithm is ideal to prevent overfitting?

A. Random forest
B. Logistic regression
C. Decision tree
D. XGBoost

Antwort: A

Begründung:
Explanation
Random forest is an algorithm that is ideal to prevent overfitting when using a dataset with many features and a small sample size. Random forest is an ensemble learning method that combines multiple decision trees to create a more robust and accurate model. Random forest can prevent overfitting by introducing randomness and diversity into the model, such as by using bootstrap sampling (sampling with replacement) to create different subsets of data for each tree, or by using feature selection (choosing a random subset of features) to split each node in a tree.

95. Frage
A classifier has been implemented to predict whether or not someone has a specific type of disease.
Considering that only 1% of the population in the dataset has this disease, which measures will work the BEST to evaluate this model?

A. Precision and accuracy
B. Mean squared error
C. Recall and explained variance
D. Precision and recall

Antwort: D

Begründung:
Explanation
Precision and recall are two measures that can evaluate the performance of a classifier, especially when the data is imbalanced. Precision is the ratio of true positives (correctly predicted positive cases) to all predicted positive cases. Recall is the ratio of true positives to all actual positive cases. Precision and recall can help assess how well the classifier can identify the positive cases (the disease) and avoid false negatives (missed diagnosis) or false positives (unnecessary treatment).

96. Frage
We are using the k-nearest neighbors algorithm to classify the new data points. The features are on different scales.
Which method can help us to solve this problem?

A. Normalization
B. Square-root transformation
C. Log transformation
D. Standardization

Antwort: A

Begründung:
Normalization is a method that can help us to solve the problem of features being on different scales when using the k-nearest neighbors algorithm. Normalization is a technique that rescales the values of features to a common range, such as [0, 1] or [-1, 1]. Normalization can help reduce the influence or dominance of some features over others, as well as improve the accuracy and performance of the algorithm2.

97. Frage
Which of the following pieces of AI technology provides the ability to create fake videos?

A. Recurrent neural networks (RNN)
B. Long short-term memory (LSTM) networks
C. Generative adversarial networks (GAN)
D. Support-vector machines (SVM)

Antwort: C

Begründung:
Generative adversarial networks (GAN) are a type of AI technology that can create fake videos, images, audio, or text that are realistic and indistinguishable from real ones. GAN consist of two neural networks: a generator and a discriminator. The generator tries to produce fake samples from random noise, while the discriminator tries to distinguish between real and fake samples. The two networks compete against each other in a game-like scenario, where the generator tries to fool the discriminator and the discriminator tries to catch the generator. Through this process, both networks improve their abilities until they reach an equilibrium where the generator can produce convincing fakes.

98. Frage
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