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CertNexus AIP-210 Exam Syllabus Topics:

Topic
Details

Topic 1

Train, validate, and test data subsets
Training and Tuning ML Systems and Models

Topic 2

Identify potential ethical concerns
Analyze machine learning system use cases

Topic 3

Transform numerical and categorical data
Address business risks, ethical concerns, and related concepts in operationalizing the model

Topic 4

Recognize relative impact of data quality and size to algorithms
Engineering Features for Machine Learning

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CertNexus Certified Artificial Intelligence Practitioner (CAIP) Sample Questions (Q56-Q61):

NEW QUESTION # 56
What is Word2vec?

A. A word embedding method that finds characteristics of words in a very large number of documents.
B. A bag of words.
C. A matrix of how frequently words appear in a group of documents.
D. A word embedding method that builds a one-hot encoded matrix from samples and the terms that appear in them.

Answer: A

Explanation:
Explanation
Word2vec is a word embedding method that finds characteristics of words in a very large number of documents. Word embedding is a technique that converts words into numerical vectors that represent their meaning, usage, or context. Word2vec learns a dense and continuous vector representation for each word based on its context in a large corpus of text. Word2vec can capture the semantic and syntactic similarity and relationships among words, such as synonyms, antonyms, analogies, or associations1.

NEW QUESTION # 57
A product manager is designing an Artificial Intelligence (AI) solution and wants to do so responsibly, evaluating both positive and negative outcomes.
The team creates a shared taxonomy of potential negative impacts and conducts an assessment along vectors such as severity, impact, frequency, and likelihood.
Which modeling technique does this team use?

A. Harms
B. Business
C. Process
D. Threat

Answer: A

Explanation:
Harms modeling is a technique that helps product managers design AI solutions responsibly by evaluating both positive and negative outcomes. Harms modeling involves creating a shared taxonomy of potential negative impacts and conducting an assessment along vectors such as severity, impact, frequency, and likelihood. Harms modeling can help identify and mitigate any risks or harms that may arise from using AI solutions. References: [Harms Modeling for Responsible AI | by Google Developers | Google Developers],
[Harms Modeling for Responsible AI - YouTube]

NEW QUESTION # 58
Which of the following models are text vectorization methods? (Select two.)

A. Skip-gram
B. t-SNE
C. TF-IDF
D. Lemmatization
E. PCA
F. Tokenization

Answer: A,C

Explanation:
Explanation
Skip-gram and TF-IDF are both text vectorization methods that convert text into numerical feature vectors.
Skip-gram is a prediction-based word embedding method that learns vector representations of words from their contexts in a large corpus of text. TF-IDF is a frequency-based word weighting method that assigns scores to words based on their importance in a document and in a corpus of documents. References: Text Vectorization and Word Embedding | Guide to Master NLP (Part 5), What Is Text Vectorization? Everything You Need to Know - deepset

NEW QUESTION # 59
You train a neural network model with two layers, each layer having four nodes, and realize that the model is underfit. Which of the actions below will NOT work to fix this underfitting?

A. Add features to training data
B. Increase the complexity of the model
C. Get more training data
D. Train the model for more epochs

Answer: C

Explanation:
Underfitting is a problem that occurs when a model learns too little from the training data and fails to capture the underlying complexity or structure of the data. Underfitting can result from using insufficient or irrelevant features, a low complexity of the model, or a lack of training data. Underfitting can reduce the accuracy and generalization of the model, as it may produce oversimplified or inaccurate predictions. Some of the ways to fix underfitting are:
* Add features to training data: Adding more features or variables to the training data can help increase the information and diversity of the data, which can help the model learn more complex patterns and relationships.
* Increase the complexity of the model: Increasing the complexity of the model can help increase its expressive power and flexibility, which can help it fit better to the data. For example, adding more layers or nodes to a neural network can increase its complexity.
* Train the model for more epochs: Training the model for more epochs can help increase its learning ability and convergence, which can help it optimize its parameters and reduce its error.
Getting more training data will not work to fix underfitting, as it will not change the complexity or structure of the data or the model. Getting more training data may help with overfitting, which is when a model learns too much from the training data and fails to generalize well to new or unseen data.

NEW QUESTION # 60
Which of the following unsupervised learning models can a bank use for fraud detection?

A. Hierarchical clustering
B. k-means
C. Anomaly detection
D. DB5CAN

Answer: C

Explanation:
Anomaly detection is an unsupervised learning technique that identifies outliers or abnormal patterns in data, which can be useful for fraud detection. Anomaly detection algorithms can learn the normal behavior of transactions and flag the ones that deviate significantly from the norm, indicating possible fraud.

NEW QUESTION # 61
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