CertNexus AIP-210 Practice Exams
Last updated on Oct 22,2026- Exam Code: AIP-210
- Exam Name: CertNexus Certified Artificial Intelligence Practitioner (CAIP)
- Certification Provider: CertNexus
- Latest update: Oct 22,2026
Question #1
Which of the following can take a question in natural language and return a precise answer to the question?
- A . Databricks
- B . IBM Watson
- C . Pandas
- D . Spark ML
Correct Answer: B
B
Explanation:
IBM Watson is an AI technology that can take a question in natural language and return a precise answer to the question. IBM Watson is a cognitive computing system that can understand natural language, generate hypotheses, and provide evidence-based answers. IBM Watson can be applied to various domains and industries, such as healthcare, education, finance, or law.
B
Explanation:
IBM Watson is an AI technology that can take a question in natural language and return a precise answer to the question. IBM Watson is a cognitive computing system that can understand natural language, generate hypotheses, and provide evidence-based answers. IBM Watson can be applied to various domains and industries, such as healthcare, education, finance, or law.
Question #2
An AI practitioner incorporates risk considerations into a deployment plan and decides to log and store historical predictions for potential, future access requests.
Which ethical principle is this an example of?
- A . Fairness
- B . Privacy
- C . Safety
- D . Transparency
Correct Answer: D
D
Explanation:
Transparency is an ethical principle that describes the degree to which an AI system can provide clear and understandable information about its inputs, outputs, processes, and decisions. Transparency can help increase trust and confidence among users and stakeholders, as well as enable accountability and responsibility for the system’s actions and outcomes. Logging and storing historical predictions for potential, future access requests is an example of transparency, as it can help provide evidence and explanation for the system’s recommendations, as well as facilitate auditing and feedback.
D
Explanation:
Transparency is an ethical principle that describes the degree to which an AI system can provide clear and understandable information about its inputs, outputs, processes, and decisions. Transparency can help increase trust and confidence among users and stakeholders, as well as enable accountability and responsibility for the system’s actions and outcomes. Logging and storing historical predictions for potential, future access requests is an example of transparency, as it can help provide evidence and explanation for the system’s recommendations, as well as facilitate auditing and feedback.
Question #3
In a self-driving car company, ML engineers want to develop a model for dynamic pathing.
Which of following approaches would be optimal for this task?
- A . Dijkstra Algorithm
- B . Reinforcement learning
- C . Supervised Learning.
- D . Unsupervised Learning
Correct Answer: B
B
Explanation:
Reinforcement learning is a type of machine learning that involves learning from trial and error based on rewards and penalties. Reinforcement learning can be used to develop models for dynamic pathing, which is the problem of finding an optimal path from one point to another in an uncertain and changing environment. Reinforcement learning can enable the model to adapt to new situations and learn from its own actions and feedback. For example, a self-driving car company can use reinforcement learning to train its model to navigate complex traffic scenarios and avoid collisions.
B
Explanation:
Reinforcement learning is a type of machine learning that involves learning from trial and error based on rewards and penalties. Reinforcement learning can be used to develop models for dynamic pathing, which is the problem of finding an optimal path from one point to another in an uncertain and changing environment. Reinforcement learning can enable the model to adapt to new situations and learn from its own actions and feedback. For example, a self-driving car company can use reinforcement learning to train its model to navigate complex traffic scenarios and avoid collisions.
Question #4
Which of the following scenarios is an example of entanglement in ML pipelines?
- A . Add a new method for drift detection in the model evaluation step.
- B . Add a new pipeline for retraining the model in the model training step.
- C . Change in normalization function in the feature engineering step.
- D . Change the way output is visualized in the monitoring step.
Correct Answer: C
C
Explanation:
Entanglement in ML pipelines occurs when a change in one step affects other steps that depend on it. Changing the normalization function in the feature engineering step would affect the model training and evaluation steps, as they rely on the features generated by the feature engineering step. Therefore, this scenario is an example of entanglement in ML pipelines. The other scenarios are not examples of entanglement, as they do not affect other steps in the pipeline.
C
Explanation:
Entanglement in ML pipelines occurs when a change in one step affects other steps that depend on it. Changing the normalization function in the feature engineering step would affect the model training and evaluation steps, as they rely on the features generated by the feature engineering step. Therefore, this scenario is an example of entanglement in ML pipelines. The other scenarios are not examples of entanglement, as they do not affect other steps in the pipeline.
Question #5
Which of the following algorithms is an example of unsupervised learning?
- A . Neural networks
- B . Principal components analysis
- C . Random forest
- D . Ridge regression
Correct Answer: B
B
Explanation:
Unsupervised learning is a type of machine learning that involves finding patterns or structures in unlabeled data without any predefined outcome or feedback. Unsupervised learning can be used for various tasks, such as clustering, dimensionality reduction, anomaly detection, or association rule mining.
Some of the common algorithms for unsupervised learning are:
Principal components analysis: Principal components analysis (PCA) is a method that reduces the dimensionality of data by transforming it into a new set of orthogonal variables (principal components) that capture the maximum amount of variance in the data. PCA can help simplify and visualize high-dimensional data, as well as remove noise or redundancy from the data.
K-means clustering: K-means clustering is a method that partitions data into k groups (clusters) based on their similarity or distance. K-means clustering can help discover natural or hidden groups in the data, as well as identify outliers or anomalies in the data.
Apriori algorithm: Apriori algorithm is a method that finds frequent itemsets (sets of items that occur together frequently) and association rules (rules that describe how items are related or correlated) in transactional data. Apriori algorithm can help discover patterns or insights in the data, such as customer behavior, preferences, or recommendations.
B
Explanation:
Unsupervised learning is a type of machine learning that involves finding patterns or structures in unlabeled data without any predefined outcome or feedback. Unsupervised learning can be used for various tasks, such as clustering, dimensionality reduction, anomaly detection, or association rule mining.
Some of the common algorithms for unsupervised learning are:
Principal components analysis: Principal components analysis (PCA) is a method that reduces the dimensionality of data by transforming it into a new set of orthogonal variables (principal components) that capture the maximum amount of variance in the data. PCA can help simplify and visualize high-dimensional data, as well as remove noise or redundancy from the data.
K-means clustering: K-means clustering is a method that partitions data into k groups (clusters) based on their similarity or distance. K-means clustering can help discover natural or hidden groups in the data, as well as identify outliers or anomalies in the data.
Apriori algorithm: Apriori algorithm is a method that finds frequent itemsets (sets of items that occur together frequently) and association rules (rules that describe how items are related or correlated) in transactional data. Apriori algorithm can help discover patterns or insights in the data, such as customer behavior, preferences, or recommendations.
Question #6
What is Word2vec?
- A . A bag of words.
- B . A matrix of how frequently words appear in a group of documents.
- C . A word embedding method that builds a one-hot encoded matrix from samples and the terms that appear in them.
- D . A word embedding method that finds characteristics of words in a very large number of documents.
Correct Answer: D
D
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.
D
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.
Question #7
When working with textual data and trying to classify text into different languages, which approach to representing features makes the most sense?
- A . Bag of words model with TF-IDF
- B . Bag of bigrams (2 letter pairs)
- C . Word2Vec algorithm
- D . Clustering similar words and representing words by group membership
Correct Answer: B
B
Explanation:
A bag of bigrams (2 letter pairs) is an approach to representing features for textual data that involves counting the frequency of each pair of adjacent letters in a text. For example, the word “hello” would be represented as {“he”: 1, “el”: 1, “ll”: 1, “lo”: 1}. A bag of bigrams can capture some information about the spelling and structure of words, which can be useful for identifying the language of a text. For example, some languages have more common bigrams than others, such as “th” in English or “ch” in German.
B
Explanation:
A bag of bigrams (2 letter pairs) is an approach to representing features for textual data that involves counting the frequency of each pair of adjacent letters in a text. For example, the word “hello” would be represented as {“he”: 1, “el”: 1, “ll”: 1, “lo”: 1}. A bag of bigrams can capture some information about the spelling and structure of words, which can be useful for identifying the language of a text. For example, some languages have more common bigrams than others, such as “th” in English or “ch” in German.
Question #8
A change in the relationship between the target variable and input features is
- A . concept drift.
- B . covariate shift.
- C . data drift.
- D . model decay.
Correct Answer: A
A
Explanation:
Concept drift, also known as model drift, occurs when the task that the model was designed to perform changes over time. For example, imagine that a machine learning model was trained to detect spam emails based on the content of the email. If the types of spam emails that people receive change significantly, the model may no longer be able to accurately detect spam.
Reference: Understanding Data Drift and Model Drift: Drift Detection in Python | DataCamp, Machine Learning Monitoring, Part 5: Why You Should Care About Data and Concept Drift
A
Explanation:
Concept drift, also known as model drift, occurs when the task that the model was designed to perform changes over time. For example, imagine that a machine learning model was trained to detect spam emails based on the content of the email. If the types of spam emails that people receive change significantly, the model may no longer be able to accurately detect spam.
Reference: Understanding Data Drift and Model Drift: Drift Detection in Python | DataCamp, Machine Learning Monitoring, Part 5: Why You Should Care About Data and Concept Drift
Question #9
Which of the following equations best represent an LI norm?
- A . |x| + |y|
- B . |x|+|y|^2
- C . |x|-|y|
- D . |x|^2+|y|^2
Correct Answer: A
A
Explanation:
An L1 norm is a measure of distance or magnitude that is defined as the sum of the absolute values of the components of a vector. For example, if x and y are two components of a vector, then the L1 norm of that vector is |x| + |y|. The L1 norm is also known as the Manhattan distance or the taxicab distance, as it represents the shortest path between two points in a grid-like city.
A
Explanation:
An L1 norm is a measure of distance or magnitude that is defined as the sum of the absolute values of the components of a vector. For example, if x and y are two components of a vector, then the L1 norm of that vector is |x| + |y|. The L1 norm is also known as the Manhattan distance or the taxicab distance, as it represents the shortest path between two points in a grid-like city.
Question #10
You are implementing a support-vector machine on your data, and a colleague suggests you use a polynomial kernel. In what situation might this help improve the prediction of your model?
- A . When it is necessary to save computational time.
- B . When the categories of the dependent variable are not linearly separable.
- C . When the distribution of the dependent variable is Gaussian.
- D . When there is high correlation among the features.
Correct Answer: B
B
Explanation:
A support-vector machine (SVM) is a supervised learning algorithm that can be used for classification or regression problems. An SVM tries to find an optimal hyperplane that separates the data into different categories or classes. However, sometimes the data is not linearly separable, meaning there is no straight line or plane that can separate them. In such cases, a polynomial kernel can help improve the prediction of the SVM by transforming the data into a higher-dimensional space where it becomes linearly separable. A polynomial kernel is a function that computes the similarity between two data points using a polynomial function of their features.
B
Explanation:
A support-vector machine (SVM) is a supervised learning algorithm that can be used for classification or regression problems. An SVM tries to find an optimal hyperplane that separates the data into different categories or classes. However, sometimes the data is not linearly separable, meaning there is no straight line or plane that can separate them. In such cases, a polynomial kernel can help improve the prediction of the SVM by transforming the data into a higher-dimensional space where it becomes linearly separable. A polynomial kernel is a function that computes the similarity between two data points using a polynomial function of their features.