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
Given a feature set with rows that contain missing continuous values, and assuming the data is normally distributed, what is the best way to fill in these missing features?
- A . Delete entire rows that contain any missing features.
- B . Fill in missing features with random values for that feature in the training set.
- C . Fill in missing features with the average of observed values for that feature in the entire dataset.
- D . Delete entire columns that contain any missing features.
C
Explanation:
Missing values are a common problem in data analysis and machine learning, as they can affect the quality and reliability of the data and the model. There are various methods to deal with missing values, such as deleting, imputing, or ignoring them. One of the most common methods is imputing, which means replacing the missing values with some estimated values based on some criteria. For continuous variables, one of the simplest and most widely used imputation methods is to fill in the missing values with the mean (average) of the observed values for that variable in the entire dataset. This method can preserve the overall distribution and variance of the data, as well as avoid introducing bias or noise.
Which of the following describes a benefit of machine learning for solving business problems?
- A . Increasing the quantity of original data
- B . Increasing the speed of analysis
- C . Improving the constraint of the problem
- D . Improving the quality of original data
B
Explanation:
Increasing the speed of analysis is a benefit of machine learning for solving business problems. Machine learning is a branch of artificial intelligence that involves creating systems that can learn from data and make predictions or decisions. Machine learning can help increase the speed of analysis by automating and optimizing various tasks, such as data processing, feature extraction, model training, model evaluation, or model deployment. Machine learning can also help handle large and complex data sets that may be difficult or impractical to analyze manually or with traditional methods.
Which of the following models are text vectorization methods? (Select two.)
- A . Lemmatization
- B . PCA
- C . Skip-gram
- D . TF-IDF
- E . Tokenization
- F . t-SNE
CD
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.
Reference: Text Vectorization and Word Embedding | Guide to Master NLP (Part 5), What Is Text Vectorization? Everything You Need to Know – deepset
Which of the following sentences is true about model evaluation and model validation in ML pipelines?
- A . Model evaluation and validation are the same.
- B . Model evaluation is defined as an external component.
- C . Model validation is defined as a set of tasks to confirm the model performs as expected.
- D . Model validation occurs before model evaluation.
C
Explanation:
Model validation is the process of checking whether the model meets the specified requirements and quality standards. It involves testing the model on a validation dataset, which is different from the training and testing datasets, and evaluating the model performance using appropriate metrics.
Reference: Overview of ML Pipelines | Machine Learning, MLOps: Continuous delivery and automation pipelines in machine learning
Which of the following is a type 1 error in statistical hypothesis testing?
- A . The null hypothesis is false, but fails to be rejected.
- B . The null hypothesis is false and is rejected.
- C . The null hypothesis is true and fails to be rejected.
- D . The null hypothesis is true, but is rejected.
D
Explanation:
A type 1 error in statistical hypothesis testing is when the null hypothesis is true, but is rejected. This means that the test falsely concludes that there is a significant difference or effect when there is none. The probability of making a type 1 error is denoted by alpha, which is also known as the significance level of the test. A type 1 error can be reduced by choosing a smaller alpha value, but this may increase the chance of making a type 2 error, which is when the null hypothesis is false but fails to be rejected.
Reference: [Type I and type II errors – Wikipedia], [Type I Error and Type II Error – Statistics How To]
In addition to understanding model performance, what does continuous monitoring of bias and variance help ML engineers to do?
- A . Detect hidden attacks
- B . Prevent hidden attacks
- C . Recover from hidden attacks
- D . Respond to hidden attacks
B
Explanation:
Hidden attacks are malicious activities that aim to compromise or manipulate an ML system without being detected or noticed. Hidden attacks can target different stages of an ML workflow, such as data collection, model training, model deployment, or model monitoring. Some examples of hidden attacks are data poisoning, backdoor attacks, model stealing, or adversarial examples. Continuous monitoring of bias and variance can help ML engineers to prevent hidden attacks, as it can help them detect any anomalies or deviations in the data or the model’s performance that may indicate a potential attack.
Which of the following items should be included in a handover to the end user to enable them to use and run a trained model on their own system? (Select three.)
- A . Information on the folder structure in your local machine
- B . Intermediate data files
- C . Link to a GitHub repository of the codebase
- D . README document
- E . Sample input and output data files
CDE
Explanation:
A handover is the process of transferring the ownership and responsibility of an ML system from one party to another, such as from the developers to the end users. A handover should include all the necessary information and resources that enable the end users to use and run a trained model on their own system.
Some of the items that should be included in a handover are:
Link to a GitHub repository of the codebase: A GitHub repository is an online platform that hosts the source code and version control of an ML system. A link to a GitHub repository can provide the end users with access to the latest and most updated version of the codebase, as well as the history and documentation of the changes made to the code.
README document: A README document is a text file that provides an overview and instructions for an ML system. A README document can include information such as the purpose, features, requirements, installation, usage, testing, troubleshooting, and license of the system.
Sample input and output data files: Sample input and output data files are data files that contain examples of valid inputs and expected outputs for an ML system. Sample input and output data files can help the end users understand how to use and run the system, as well as verify its functionality and performance.
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 . Decision tree
- B . Logistic regression
- C . Random forest
- D . XGBoost
C
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.
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 . k-nearest neighbors
- C . Logistic regression
- D . Naive-Bayes
A
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.
Reference: [K-means clustering – Wikipedia], [How to Run K-Means Clustering in Python]
An organization sells house security cameras and has asked their data scientists to implement a model to detect human feces, as distinguished from animals, so they can alert the customers only when a human gets close to their house.
Which of the following algorithms is an appropriate option with a correct reason?
- A . A decision tree algorithm, because the problem is a classification problem with a small number of features.
- B . k-means, because this is a clustering problem with a small number of features.
- C . Logistic regression, because this is a classification problem and our data is linearly separable.
- D . Neural network model, because this is a classification problem with a large number of features.
D
Explanation:
Neural network models are suitable for classification problems with a large number of features, because they can learn complex and non-linear patterns from high-dimensional data. They can also handle image data, which is likely to be the input for the human face detection problem. Neural networks can also be trained using transfer learning, which can leverage pre-trained models on similar tasks and improve the accuracy and efficiency of the model.
Reference: [Neural network – Wikipedia], [Transfer Learning – Machine Learning’s Next Frontier]