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 #21
Which of the following methods can be used to rebalance a dataset using the rebalance design pattern?
- A . Bagging
- B . Boosting
- C . Stacking
- D . Weighted class
Correct Answer: D
D
Explanation:
Weighted class is a technique to rebalance a dataset by assigning different weights to each class, according to their frequency in the dataset. The weights are inversely proportional to the class frequency, meaning that rare classes have higher weights and common classes have lower weights. This helps to reduce the bias towards the majority class and improve the model performance on the minority class.
Reference: 4. Data Validation – Building Machine Learning Pipelines, A guide to React design patterns – LogRocket Blog
D
Explanation:
Weighted class is a technique to rebalance a dataset by assigning different weights to each class, according to their frequency in the dataset. The weights are inversely proportional to the class frequency, meaning that rare classes have higher weights and common classes have lower weights. This helps to reduce the bias towards the majority class and improve the model performance on the minority class.
Reference: 4. Data Validation – Building Machine Learning Pipelines, A guide to React design patterns – LogRocket Blog
Question #22
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 . Get more training data
- C . Increase the complexity of the model
- D . Train the model for more epochs
Correct Answer: B
B
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.
B
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.
Question #23
Which of the following sentences is TRUE about the definition of cloud models for machine learning pipelines?
- A . Data as a Service (DaaS) can host the databases providing backups, clustering, and high availability.
- B . Infrastructure as a Service (IaaS) can provide CPU, memory, disk, network and GPU.
- C . Platform as a Service (PaaS) can provide some services within an application such as payment applications to create efficient results.
- D . Software as a Service (SaaS) can provide AI practitioner data science services such as Jupyter notebooks.
Correct Answer: D
D
Explanation:
Cloud models are service models that provide different levels of abstraction and control over computing resources in a cloud environment. Some of the common cloud models for machine learning pipelines are:
Software as a Service (SaaS): SaaS provides ready-to-use applications that run on the cloud provider’s infrastructure and are accessible through a web browser or an API. SaaS can provide AI practitioner data science services such as Jupyter notebooks, which are web-based interactive environments that allow users to create and share documents that contain code, text, visualizations, and more.
Platform as a Service (PaaS): PaaS provides a platform that allows users to develop, run, and manage applications without worrying about the underlying infrastructure. PaaS can provide some services within an application such as payment applications to create efficient results.
Infrastructure as a Service (IaaS): IaaS provides access to fundamental computing resources such as servers, storage, networks, and operating systems. IaaS can provide CPU, memory, disk, network and GPU resources that can be used to run machine learning models and applications.
Data as a Service (DaaS): DaaS provides access to data sources that can be consumed by applications or users on demand. DaaS can host the databases providing backups, clustering, and high availability.
D
Explanation:
Cloud models are service models that provide different levels of abstraction and control over computing resources in a cloud environment. Some of the common cloud models for machine learning pipelines are:
Software as a Service (SaaS): SaaS provides ready-to-use applications that run on the cloud provider’s infrastructure and are accessible through a web browser or an API. SaaS can provide AI practitioner data science services such as Jupyter notebooks, which are web-based interactive environments that allow users to create and share documents that contain code, text, visualizations, and more.
Platform as a Service (PaaS): PaaS provides a platform that allows users to develop, run, and manage applications without worrying about the underlying infrastructure. PaaS can provide some services within an application such as payment applications to create efficient results.
Infrastructure as a Service (IaaS): IaaS provides access to fundamental computing resources such as servers, storage, networks, and operating systems. IaaS can provide CPU, memory, disk, network and GPU resources that can be used to run machine learning models and applications.
Data as a Service (DaaS): DaaS provides access to data sources that can be consumed by applications or users on demand. DaaS can host the databases providing backups, clustering, and high availability.
Question #24
Your dependent variable Y is a count, ranging from 0 to infinity. Because Y is approximately log-normally distributed, you decide to log-transform the data prior to performing a linear regression.
What should you do before log-transforming Y?
- A . Add 1 to all of the Y values.
- B . Divide all the Y values by the standard deviation of Y.
- C . Explore the data for outliers.
- D . Subtract the mean of Y from all the Y values.
Correct Answer: A
A
Explanation:
Before log-transforming Y, we should add 1 to all of the Y values. This is because log transformation is undefined for zero or negative values, and some of the Y values may be zero. Adding 1 to all of the Y values can avoid this problem and ensure that the log transformation is valid and meaningful. Adding 1 to all of the Y values is also known as a log-plus-one transformation.
A
Explanation:
Before log-transforming Y, we should add 1 to all of the Y values. This is because log transformation is undefined for zero or negative values, and some of the Y values may be zero. Adding 1 to all of the Y values can avoid this problem and ensure that the log transformation is valid and meaningful. Adding 1 to all of the Y values is also known as a log-plus-one transformation.
Question #25
Which two encodes can be used to transform categories data into numerical features? (Select two.)
- A . Count Encoder
- B . Log Encoder
- C . Mean Encoder
- D . Median Encoder
- E . One-Hot Encoder
Correct Answer: CE
CE
Explanation:
Encoding is a technique that transforms categorical data into numerical features that can be used by machine learning models. Categorical data are data that have a finite number of possible values or categories, such as gender, color, or country. Encoding can help convert categorical data into a format that is suitable and understandable for machine learning models.
Some of the encoding methods that can be used to transform categorical data into numerical features are:
Mean Encoder: Mean encoder is a method that replaces each category with the mean value of the target variable for that category. Mean encoder can capture the relationship between the category and the target variable, but it may cause overfitting or multicollinearity problems.
One-Hot Encoder: One-hot encoder is a method that creates a binary vector for each category, where only one element has a value of 1 (the hot bit) and the rest have a value of 0. One-hot encoder can create distinct and orthogonal vectors for each category, but it may increase the dimensionality and sparsity of the data.
CE
Explanation:
Encoding is a technique that transforms categorical data into numerical features that can be used by machine learning models. Categorical data are data that have a finite number of possible values or categories, such as gender, color, or country. Encoding can help convert categorical data into a format that is suitable and understandable for machine learning models.
Some of the encoding methods that can be used to transform categorical data into numerical features are:
Mean Encoder: Mean encoder is a method that replaces each category with the mean value of the target variable for that category. Mean encoder can capture the relationship between the category and the target variable, but it may cause overfitting or multicollinearity problems.
One-Hot Encoder: One-hot encoder is a method that creates a binary vector for each category, where only one element has a value of 1 (the hot bit) and the rest have a value of 0. One-hot encoder can create distinct and orthogonal vectors for each category, but it may increase the dimensionality and sparsity of the data.
Question #26
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 . Business
- B . Harms
- C . Process
- D . Threat
Correct Answer: B
B
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.
Reference: [Harms Modeling for Responsible AI | by Google Developers | Google Developers], [Harms Modeling for Responsible AI – YouTube]
B
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.
Reference: [Harms Modeling for Responsible AI | by Google Developers | Google Developers], [Harms Modeling for Responsible AI – YouTube]
Question #27
In general, models that perform their tasks:
- A . Less accurately are less robust against adversarial attacks.
- B . Less accurately are neither more nor less robust against adversarial attacks.
- C . More accurately are less robust against adversarial attacks.
- D . More accurately are neither more nor less robust against adversarial attacks.
Correct Answer: C
C
Explanation:
Adversarial attacks are malicious attempts to fool or manipulate machine learning models by adding small perturbations to the input data that are imperceptible to humans but can cause significant changes in the model output. In general, models that perform their tasks more accurately are less robust against adversarial attacks, because they tend to have higher confidence in their predictions and are more sensitive to small changes in the input data.
Reference: [Adversarial machine learning – Wikipedia], [Why Are Machine Learning Models Susceptible to Adversarial Attacks? | by Anirudh Jain | Towards Data Science]
C
Explanation:
Adversarial attacks are malicious attempts to fool or manipulate machine learning models by adding small perturbations to the input data that are imperceptible to humans but can cause significant changes in the model output. In general, models that perform their tasks more accurately are less robust against adversarial attacks, because they tend to have higher confidence in their predictions and are more sensitive to small changes in the input data.
Reference: [Adversarial machine learning – Wikipedia], [Why Are Machine Learning Models Susceptible to Adversarial Attacks? | by Anirudh Jain | Towards Data Science]
Question #28
You create a prediction model with 96% accuracy. While the model’s true positive rate (TPR) is
performing well at 99%, the true negative rate (TNR) is only 50%. Your supervisor tells you that the
TNR needs to be higher, even if it decreases the TPR. Upon further inspection, you notice that the
vast majority of your data is truly positive.
What method could help address your issue?
- A . Normalization
- B . Oversampling
- C . Principal components analysis
- D . Quality filtering
Correct Answer: B
B
Explanation:
Oversampling is a method that can help address the issue of imbalanced data, which is when one class is much more frequent than the other in the dataset. This can cause the model to be biased towards the majority class and have a low true negative rate. Oversampling involves creating synthetic samples of the minority class or replicating existing samples to balance the class distribution. This can help the model learn more from the minority class and improve the true negative rate.
Reference: [Handling imbalanced datasets in machine learning], [Oversampling and undersampling in data analysis – Wikipedia]
B
Explanation:
Oversampling is a method that can help address the issue of imbalanced data, which is when one class is much more frequent than the other in the dataset. This can cause the model to be biased towards the majority class and have a low true negative rate. Oversampling involves creating synthetic samples of the minority class or replicating existing samples to balance the class distribution. This can help the model learn more from the minority class and improve the true negative rate.
Reference: [Handling imbalanced datasets in machine learning], [Oversampling and undersampling in data analysis – Wikipedia]
Question #29
Which of the following text vectorization methods is appropriate and correctly defined for an English-to-Spanish translation machine?
- A . Using TF-IDF because in translation machines, we do not care about the order of the words.
- B . Using TF-IDF because in translation machines, we need to consider the order of the words.
- C . Using Word2vec because in translation machines, we do not care about the order of the words.
- D . Using Word2vec because in translation machines, we need to consider the order of the words.
Correct Answer: D
D
Explanation:
Text vectorization is a technique that converts text into numerical vectors that can be used by machine learning models. Text vectorization can use different methods to represent text features, such as word frequency, word order, word meaning, or word context.
Some of the common text vectorization methods are:
TF-IDF: TF-IDF (term frequency-inverse document frequency) is a method that assigns a weight to each word based on its frequency in a document and its rarity across a collection of documents. TF-IDF can capture the importance and relevance of words for a given topic or domain, but it does not consider the order or meaning of words.
Word2vec: Word2vec is a method that learns a 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, as well as preserve the order of words.
For an English-to-Spanish translation machine, using Word2vec would be appropriate and correctly defined, because in translation machines, we need to consider the order of the words, as well as their meaning and context.
D
Explanation:
Text vectorization is a technique that converts text into numerical vectors that can be used by machine learning models. Text vectorization can use different methods to represent text features, such as word frequency, word order, word meaning, or word context.
Some of the common text vectorization methods are:
TF-IDF: TF-IDF (term frequency-inverse document frequency) is a method that assigns a weight to each word based on its frequency in a document and its rarity across a collection of documents. TF-IDF can capture the importance and relevance of words for a given topic or domain, but it does not consider the order or meaning of words.
Word2vec: Word2vec is a method that learns a 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, as well as preserve the order of words.
For an English-to-Spanish translation machine, using Word2vec would be appropriate and correctly defined, because in translation machines, we need to consider the order of the words, as well as their meaning and context.
Question #30
An AI system recommends New Year’s resolutions. It has an ML pipeline without monitoring components.
What retraining strategy would be BEST for this pipeline?
- A . Periodically before New Year’s Day and after New Year’s Day
- B . Periodically every year
- C . When concept drift is detected
- D . When data drift is detected
Correct Answer: B
B
Explanation:
Retraining is the process of updating an existing ML model with new or updated data to maintain or improve its performance and relevance. Retraining can help address various issues or challenges in ML systems, such as data drift, concept drift, model degradation, or changing requirements. Retraining can be done using different strategies, such as periodically, continuously, or on-demand.
For an AI system that recommends New Year’s resolutions, retraining periodically every year would be the best strategy for this pipeline. This is because New Year’s resolutions are seasonal and time-sensitive, meaning that they may vary depending on the year or the current situation. Retraining periodically every year can help ensure that the system’s recommendations are up-to-date and relevant for each new year.
B
Explanation:
Retraining is the process of updating an existing ML model with new or updated data to maintain or improve its performance and relevance. Retraining can help address various issues or challenges in ML systems, such as data drift, concept drift, model degradation, or changing requirements. Retraining can be done using different strategies, such as periodically, continuously, or on-demand.
For an AI system that recommends New Year’s resolutions, retraining periodically every year would be the best strategy for this pipeline. This is because New Year’s resolutions are seasonal and time-sensitive, meaning that they may vary depending on the year or the current situation. Retraining periodically every year can help ensure that the system’s recommendations are up-to-date and relevant for each new year.