[Feb 06, 2026] CertNexus AIP-210 Real Exam Questions and Answers FREE
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CertNexus AIP-210 Exam Syllabus Topics:
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NEW QUESTION # 52
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 recall
- B. Mean squared error
- C. Precision and accuracy
- D. Recall and explained variance
Answer: A
NEW QUESTION # 53
Which two of the following criteria are essential for machine learning models to achieve before deployment?
(Select two.)
- A. Explainability
- B. Scalability
- C. Data size
- D. Complexity
- E. Portability
Answer: A,B
Explanation:
Explanation
Scalability and explainability are two criteria that are essential for ML models to achieve before deployment.
Scalability is the ability of an ML model to handle increasing amounts of data or requests without compromising its performance or quality. Scalability can help ensure that the model can meet the demand and expectations of users or customers, as well as adapt to changing conditions or environments. Explainability is the ability of an ML model to provide clear and intuitive explanations for its predictions or decisions.
Explainability can help increase trust and confidence among users or stakeholders, as well as enable accountability and responsibility for the model's actions and outcomes.
NEW QUESTION # 54
Which two encodes can be used to transform categories data into numerical features? (Select two.)
- A. One-Hot Encoder
- B. Count Encoder
- C. Median Encoder
- D. Log Encoder
- E. Mean Encoder
Answer: A,E
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.
NEW QUESTION # 55
You have a dataset with thousands of features, all of which are categorical. Using these features as predictors, you are tasked with creating a prediction model to accurately predict the value of a continuous dependent variable. Which of the following would be appropriate algorithms to use? (Select two.)
- A. Logistic regression
- B. K-means
- C. Lasso regression
- D. K-nearest neighbors
- E. Ridge regression
Answer: C,E
Explanation:
Lasso regression and ridge regression are both types of linear regression models that can handle high- dimensional and categorical data. They use regularization techniques to reduce the complexity of the model and avoid overfitting. Lasso regression uses L1 regularization, which adds a penalty term proportional to the absolute value of the coefficients to the loss function. This can shrink some coefficients to zero and perform feature selection. Ridge regression uses L2 regularization, which adds a penalty term proportional to the square of the coefficients to the loss function. This can shrink all coefficients towards zero and reduce multicollinearity. References: [Lasso (statistics) - Wikipedia], [Ridge regression - Wikipedia]
NEW QUESTION # 56
When should you use semi-supervised learning? (Select two.)
- A. There is a large amount of unlabeled data to be used for predictions.
- B. Labeling data is challenging and expensive.
- C. A small set of labeled data is biased toward one class.
- D. There is a large amount of labeled data to be used for predictions.
- E. A small set of labeled data is available but not representative of the entire distribution.
Answer: A,B
Explanation:
Semi-supervised learning is a type of machine learning that uses both labeled and unlabeled data to train a model. Semi-supervised learning can be useful when:
* Labeling data is challenging and expensive: Labeling data requires human intervention and domain expertise, which can be costly and time-consuming. Semi-supervised learning can leverage the large amount of unlabeled data that is easier and cheaper to obtain and use it to improve the model's performance.
* There is a large amount of unlabeled data to be used for predictions: Unlabeled data can provide additional information and diversity to the model, which can help it learn more complex patterns and generalize better to new data. Semi-supervised learning can use various techniques, such as self- training, co-training, or generative models, to incorporate unlabeled data into the learning process.
NEW QUESTION # 57
When working with textual data and trying to classify text into different languages, which approach to representing features makes the most sense?
- A. Word2Vec algorithm
- B. Bag of words model with TF-IDF
- C. Clustering similar words and representing words by group membership
- D. Bag of bigrams (2 letter pairs)
Answer: D
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 .
NEW QUESTION # 58
Your dependent variable data is a proportion. The observed range of your data is 0.01 to 0.99. The instrument used to generate the dependent variable data is known to generate low quality data for values close to 0 and close to 1. A colleague suggests performing a logit-transformation on the data prior to performing a linear regression. Which of the following is a concern with this approach?
Definition of logit-transformation
If p is the proportion: logit(p)=log(p/(l-p))
- A. The model will be more likely to violate the assumption of normality.
- B. Values near 0.5 before logit-transformation will be near 0 after.
- C. After logit-transformation, the data may violate the assumption of independence.
- D. Noisy data could become more influential in your model.
Answer: D
Explanation:
Explanation
Logit-transformation is a common way to transform proportion data into a continuous variable that can be used for linear regression. However, one concern with this approach is that noisy data could become more influential in your model. This is because logit-transformation tends to amplify the values close to 0 and 1, which are also the values that are likely to be affected by measurement errors or outliers. This could distort the relationship between the dependent and independent variables and bias the regression coefficients. References:
[Logit Transformation | Real Statistics Using Excel], [Logit transformation for proportions - Cross Validated]
NEW QUESTION # 59
Which of the following tests should be performed at the production level before deploying a newly retrained model?
- A. Security test
- B. Unit test
- C. A/Btest
- D. Performance test
Answer: D
Explanation:
Explanation
Performance testing is a type of testing that should be performed at the production level before deploying a newly retrained model. Performance testing measures how well the model meets the non-functional requirements, such as speed, scalability, reliability, availability, and resource consumption. Performance testing can help identify any bottlenecks or issues that may affect the user experience or satisfaction with the model. References: [Performance Testing Tutorial: What is, Types, Metrics & Example], [Performance Testing for Machine Learning Systems | by David Talby | Towards Data Science]
NEW QUESTION # 60
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. Fill in missing features with random values for that feature in the training set.
- B. Delete entire columns that contain any missing features.
- C. Fill in missing features with the average of observed values for that feature in the entire dataset.
- D. Delete entire rows that contain any missing features.
Answer: C
Explanation:
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.
NEW QUESTION # 61
When should the model be retrained in the ML pipeline?
- A. A new monitoring component is added.
- B. More data become available for the training phase.
- C. Some outliers are detected in live data.
- D. Concept drift is detected in the pipeline.
Answer: D
Explanation:
Explanation
When concept drift is detected in the pipeline, it means that the model performance has degraded over time due to changes in the underlying data generating process. This requires retraining the model with new data that reflects the current situation and updating the model parameters accordingly. References: Use pipeline parameters to retrain models in the designer - Azure Machine Learning | Microsoft Learn, Retraining Model During Deployment: Continuous Training and Continuous Testing
NEW QUESTION # 62
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. Sample input and output data files
- B. Intermediate data files
- C. README document
- D. Information on the folder structure in your local machine
- E. Link to a GitHub repository of the codebase
Answer: A,C,E
Explanation:
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.
NEW QUESTION # 63
Below are three tables: Employees, Departments, and Directors.
Employee_Table
Department_Table
Director_Table
ID
Firstname
Lastname
Age
Salary
DeptJD
4566
Joey
Morin
62
$ 122,000
1
1230
Sam
Clarck
43
$ 95,670
2
9077
Lola
Russell
54
$ 165,700
3
1346
Lily
Cotton
46
$ 156,000
4
2088
Beckett
Good
52
$ 165,000
5
Which SQL query provides the Directors' Firstname, Lastname, the name of their departments, and the average employee's salary?
- A. SELECT m.Firstname, m.Lastname, d.Name, AVG(e.Salary) as Dept_avg_SalaryFROM Employee_Table as eRIGHT JOIN Department_Table as d on e.Dept = d.NameINNER JOIN Directorjable as m on d.ID = m.DeptJDGROUP BY e.Salary
- B. SELECT m.Firstname, m.Lastname, d.Name, AVG(e.Salary) as Dept_avg_SalaryFROM Employee_Table as eRIGHT JOIN Departmentjable as d on e.Dept = d.NameINNER JOIN Directorjable as m on d.ID = m.DeptJDGROUP BY d.Name
- C. SELECT m.Firstname, m.Lastname, d.Name, AVG(e.Saiary) as Dept_avg_SaiaryFROM Employee_Table as eLEFT JOIN Department_Table as d on e.Dept = d.NameLEFT JOIN Directorjable as m on d.ID = m.DeptJDGROUP BY m.Firstname, m.Lastname, d.Name
- D. SELECT m.Firstname, m.Lastname, d.Name, AVG(e.Salary) as Dept_avg_SalaryFROM Employee_Table as eRIGHT JOIN Department_Table as d on e.Dept = d.NameINNER JOIN Directorjable as m on d.ID = m.DeptIDGROUP BY m.Firstname, m.Lastname, d.Name
Answer: D
Explanation:
This SQL query provides the Directors' Firstname, Lastname, the name of their departments, and the average employee's salary by joining the three tables using the appropriate join types and conditions. The RIGHT JOIN between Employee_Table and Department_Table ensures that all departments are included in the result, even if they have no employees. The INNER JOIN between Department_Table and Directorjable ensures that only departments with directors are included in the result. The GROUP BY clause groups the result by the directors' names and departments' names, and calculates the average salary for each group using the AVG function. References: SQL Joins - W3Schools, SQL GROUP BY Statement - W3Schools
NEW QUESTION # 64
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. Transparency
- B. Privacy
- C. Fairness
- D. Safety
Answer: A
Explanation:
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.
NEW QUESTION # 65
What is the open framework designed to help detect, respond to, and remediate threats in ML systems?
- A. Threat Susceptibility Matrix
- B. MITRE ATTandCK Matrix
- C. Adversarial ML Threat Matrix
- D. OWASP Threat and Safeguard Matrix
Answer: C
Explanation:
The Adversarial ML Threat Matrix is an open framework designed to help detect, respond to, and remediate threats in ML systems. The Adversarial ML Threat Matrix is inspired by the MITRE ATTandCK Matrix1, which is a framework for describing cyberattacks across various stages of an attack lifecycle. The Adversarial ML Threat Matrix adapts this framework to address specific threats and vulnerabilities in ML systems, such as data poisoning, model stealing, model evasion, or model inversion2. The Adversarial ML Threat Matrix provides a structured way to organize and classify adversarial techniques, tactics, procedures, examples, and mitigations for ML systems2.
NEW QUESTION # 66
Which of the following regressions will help when there is the existence of near-linear relationships among the independent variables (collinearity)?
- A. Clustering
- B. Linear regression
- C. Polynomial regression
- D. Ridge regression
Answer: D
Explanation:
Explanation
Ridge regression is a type of regularization technique that can help reduce collinearity among independent variables. It does this by adding a penalty term to the ordinary least squares (OLS) objective function, which shrinks the coefficients of highly correlated variables towards zero. This reduces the variance of the coefficient estimates and improves the stability and accuracy of the regression model. References: Multicollinearity in Regression Analysis: Problems, Detection, and Solutions - Statistics By Jim, A Beginner's Guide to Collinearity: What it is and How it affects our regression model - StrataScratch
NEW QUESTION # 67
Which two of the following statements about the beta value in an A/B test are accurate? (Select two.)
- A. The statistical power of a test is the inverse of the Beta value, or 1 - Beta.
- B. The Beta value is the rate of type I errors for the test.
- C. The Beta value is the rate of type II errors for the test.
- D. The Beta in an Alpha/Beta test represents one of the two variants of the A/B test.
Answer: C
Explanation:
The Beta value in an A/B test is the probability of making a type II error, which is failing to reject the null hypothesis when it is false. The statistical power of a test is the probability of correctly rejecting the null hypothesis when it is false, which is equal to 1 - Beta. References: Formulas for Bayesian A/B Testing - Evan Miller, The Practical Guide To AB testing statistics | Convertize
NEW QUESTION # 68
Which of the following pieces of AI technology provides the ability to create fake videos?
- A. Support-vector machines (SVM)
- B. Generative adversarial networks (GAN)
- C. Recurrent neural networks (RNN)
- D. Long short-term memory (LSTM) networks
Answer: B
Explanation:
Explanation
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.
NEW QUESTION # 69
Which of the following occurs when a data segment is collected in such a way that some members of the intended statistical population are less likely to be included than others?
- A. Algorithmic bias
- B. Sampling bias
- C. Systematic value distortion
- D. Stereotype bias
Answer: B
Explanation:
Sampling bias occurs when a data segment is collected in such a way that some members of the intended statistical population are less likely to be included than others. This can result in a sample that is not representative of the population and may lead to inaccurate or misleading conclusions. Sampling bias can be caused by various factors, such as non-random sampling methods, non-response, self-selection, or convenience sampling. References: [Sampling bias - Wikipedia], [What is Sampling Bias? Definition, Types and Examples]
NEW QUESTION # 70
Which of the following is a common negative side effect of not using regularization?
- A. Slow convergence time
- B. Higher compute resources
- C. Low test accuracy
- D. Overfitting
Answer: D
Explanation:
Overfitting is a common negative side effect of not using regularization. Regularization is a technique that reduces the complexity of a model by adding a penalty term to the loss function, which prevents the model from learning too many parameters that may fit the noise in the training data. Overfitting occurs when the model performs well on the training data but poorly on the test data or new data, because it has memorized the training data and cannot generalize well. References: Regularization (mathematics) - Wikipedia, Overfitting in Machine Learning: What It Is and How to Prevent It
NEW QUESTION # 71
Personal data should not be disclosed, made available, or otherwise used for purposes other than specified with which of the following exceptions? (Select two.)
- A. If it was requested by the authority of law.
- B. If it is for a good cause.
- C. If it was collected accidentally.
- D. If the data is only collected once.
- E. If it was with consent of the person it is collected from.
Answer: A,E
Explanation:
Personal data is any information that relates to an identified or identifiable individual, such as name, address, email, phone number, or biometric data. Personal data should not be disclosed, made available, or otherwise used for purposes other than specified, except with:
* The consent of the person it is collected from: Consent is a clear and voluntary indication of agreement by the person to the processing of their personal data for a specific purpose. Consent can be given by a statement or a clear affirmative action, such as ticking a box or clicking a button.
* The authority of law: The authority of law is a legal basis or obligation that requires or permits the processing of personal data for a legitimate purpose. For example, the authority of law could be a court order, a subpoena, a warrant, or a statute.
NEW QUESTION # 72
A company is developing a merchandise sales application The product team uses training data to teach the AI model predicting sales, and discovers emergent bias. What caused the biased results?
- A. The team set flawed expectations when training the model.
- B. The AI model was trained in winter and applied in summer.
- C. The training data used was inaccurate.
- D. The application was migrated from on-premise to a public cloud.
Answer: B
Explanation:
Explanation
Emergent bias is a type of bias that arises when an AI model encounters new or different data or scenarios that were not present or accounted for during its training or development. Emergent bias can cause the model to make inaccurate or unfair predictions or decisions, as it may not be able to generalize well to new situations or adapt to changing conditions. One possible cause of emergent bias is seasonality, which means that some variables or patterns in the data may vary depending on the time of year. For example, if an AI model for merchandise sales prediction was trained in winter and applied in summer, it may produce biased results due to differences in customer behavior, demand, or preferences.
NEW QUESTION # 73
Which two encodes can be used to transform categories data into numerical features? (Select two.)
- A. One-Hot Encoder
- B. Count Encoder
- C. Median Encoder
- D. Log Encoder
- E. Mean Encoder
Answer: A,E
Explanation:
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.
NEW QUESTION # 74
Which of the following best describes distributed artificial intelligence?
- A. It does not require hyperparemeter tuning because the distributed nature accounts for the bias.
- B. It uses a centralized system to speak to decentralized nodes.
- C. It relies on a distributed system that performs robust computations across a network of unreliable nodes.
- D. It intelligently pre-distributes the weight of starting a neural network.
Answer: C
Explanation:
Explanation
Distributed artificial intelligence (DAI) is a subfield of artificial intelligence that studies how multiple intelligent agents can coordinate and cooperate to achieve a common goal or solve a complex problem. DAI relies on a distributed system that performs robust computations across a network of unreliable nodes, such as sensors, robots, or humans. DAI can handle large-scale, dynamic, and uncertain environments that are beyond the capabilities of a single agent. References: [Distributed artificial intelligence - Wikipedia], [Distributed Artificial Intelligence: An Overview]
NEW QUESTION # 75
Which type of regression represents the following formula: y = c + b*x, where y = estimated dependent variable score, c = constant, b = regression coefficient, and x = score on the independent variable?
- A. Lasso regression
- B. Ridge regression
- C. Linear regression
- D. Polynomial regression
Answer: C
NEW QUESTION # 76
Which of the following is a privacy-focused law that an AI practitioner should adhere to while designing and adapting an AI system that utilizes personal data?
- A. PCIDSS
- B. Sarbanes Oxley (SOX)
- C. ISO/IEC 27001
- D. General Data Protection Regulation (GDPR)
Answer: D
Explanation:
Explanation
The General Data Protection Regulation (GDPR) is a privacy-focused law that an AI practitioner should adhere to while designing and adapting an AI system that utilizes personal data. The GDPR applies to any organization that processes personal data of individuals in the European Union (EU), regardless of where the organization is located. The GDPR grants individuals rights over their personal data, such as the right to access, rectify, erase, restrict, or object to its processing. The GDPR also imposes obligations on organizations that process personal data, such as the duty to obtain consent, conduct data protection impact assessments, implement data protection by design and by default, and ensure accountability and transparency. The GDPR also addresses some specific issues related to AI, such as automated decision-making, profiling, and data portability.
NEW QUESTION # 77
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