Free CertNexus AIP-210 Exam 2026 Practice Materials Collection
AIP-210 Exam Info and Free Practice Test All-in-One Exam Guide Mar-2026
NEW QUESTION # 53
A big data architect needs to be cautious about personally identifiable information (PII) that may be captured with their new IoT system. What is the final stage of the Data Management Life Cycle, which the architect must complete in order to implement data privacy and security appropriately?
- A. De-Duplicate
- B. Duplicate
- C. Destroy
- D. Detain
Answer: C
Explanation:
Explanation
The final stage of the data management life cycle is data destruction, which is the process of securely deleting or erasing data that is no longer needed or relevant for the organization. Data destruction ensures that data is disposed of in compliance with any legal or regulatory requirements, as well as any internal policies or standards. Data destruction also protects the organization from potential data breaches, leaks, or thefts that could compromise its privacy and security. Data destruction can be performed using various methods, such as overwriting, degaussing, shredding, or incinerating
NEW QUESTION # 54
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. Safety
- C. Transparency
- D. Privacy
Answer: C
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 # 55
Which of the following models are text vectorization methods? (Select two.)
- A. PCA
- B. t-SNE
- C. Tokenization
- D. TF-IDF
- E. Lemmatization
- F. Skip-gram
Answer: D,F
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 # 56
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. Spark ML
- D. Pandas
Answer: B
Explanation:
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.
NEW QUESTION # 57
Which of the following is the definition of accuracy?
- A. True Positives / (True Positives + False Positives)
- B. (True Positives + False Positives) / Total Predictions
- C. (True Positives + True Negatives) / Total Predictions
- D. True Positives / (True Positives + False Negatives)
Answer: C
Explanation:
Explanation
Accuracy is a measure of how well a classifier can correctly predict the class of an instance. Accuracy is calculated by dividing the number of correct predictions (true positives and true negatives) by the total number of predictions. True positives are instances that are correctly predicted as positive (belonging to the target class). True negatives are instances that are correctly predicted as negative (not belonging to the target class).
NEW QUESTION # 58
A change in the relationship between the target variable and input features is
- A. data drift.
- B. concept drift.
- C. covariate shift.
- D. model decay.
Answer: B
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. References: 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
NEW QUESTION # 59
Which of the following can benefit from deploying a deep learning model as an embedded model on edge devices?
- A. Reduction in latency
- B. Increase in data bandwidth consumption
- C. A more complex model
- D. Guaranteed availability of enough space
Answer: A
Explanation:
Latency is the time delay between a request and a response. Latency can affect the performance and user experience of an application, especially when real-time or near-real-time responses are required. Deploying a deep learning model as an embedded model on edge devices can reduce latency, as the model can run locally on the device without relying on network connectivity or cloud servers. Edge devices are devices that are located at the edge of a network, such as smartphones, tablets, laptops, sensors, cameras, or drones.
NEW QUESTION # 60
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. Respond to hidden attacks
- D. Recover from hidden attacks
Answer: B
Explanation:
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.
NEW QUESTION # 61
Which two techniques are used to build personas in the ML development lifecycle? (Select two.)
- A. Population estimates
- B. Population triage
- C. Population resampling
- D. Population regression
- E. Population variance
Answer: A,B
Explanation:
Personas are fictional characters that represent the potential users or customers of an ML system. Personas can help understand the needs, goals, preferences, and behaviors of the target audience, as well as design and evaluate the system from their perspective. Some of the techniques that are used to build personas in the ML development lifecycle are:
* Population estimates: Population estimates are statistical methods that estimate the size, characteristics, and distribution of a population based on a sample or a census. Population estimates can help identify and quantify the potential market segments and user groups for an ML system, as well as their demographics, locations, and behaviors.
* Population triage: Population triage is a process of prioritizing and selecting the most relevant and representative personas for an ML system based on some criteria or metrics. Population triage can help focus on the key user needs and scenarios, as well as avoid creating too many or too few personas.
NEW QUESTION # 62
What is Word2vec?
- A. A bag of words.
- B. A word embedding method that builds a one-hot encoded matrix from samples and the terms that appear in them.
- C. A word embedding method that finds characteristics of words in a very large number of documents.
- D. A matrix of how frequently words appear in a group of documents.
Answer: C
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 # 63
Which of the following methods can be used to rebalance a dataset using the rebalance design pattern?
- A. Bagging
- B. Weighted class
- C. Boosting
- D. Stacking
Answer: B
Explanation:
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. References: 4. Data Validation - Building Machine Learning Pipelines, A guide to React design patterns - LogRocket Blog
NEW QUESTION # 64
Which of the following is TRUE about SVM models?
- A. They can be used only for regression.
- B. They use the sigmoid function to classify the data points.
- C. They can take the feature space into higher dimensions to solve the problem.
- D. They can be used only for classification.
Answer: C
Explanation:
SVM models can use kernel functions to map the input data into higher-dimensional feature spaces, where linear separation is possible. This allows SVM models to handle non-linear problems effectively.
References: CertNexus Certified Artificial Intelligence Practitioner, Support vector machine - Wikipedia
NEW QUESTION # 65
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. Stereotype bias
- C. Systematic value distortion
- D. Sampling bias
Answer: D
Explanation:
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 # 66
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. K-nearest neighbors
- B. Logistic regression
- C. Ridge regression
- D. Lasso regression
- E. K-means
Answer: C,D
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 # 67
A dataset can contain a range of values that depict a certain characteristic, such as grades on tests in a class during the semester. A specific student has so far received the following grades: 76,81, 78, 87, 75, and 72.
There is one final test in the semester. What minimum grade would the student need to achieve on the last test to get an 80% average?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: B
Explanation:
Explanation
To calculate the minimum grade needed to achieve an 80% average, we can use the following formula:
minimum grade = (target average * number of tests - sum of grades) / (number of tests - 1) Plugging in the given values, we get:
minimum grade = (80 * 7 - (76 + 81 + 78 + 87 + 75 + 72)) / (7 - 6)
minimum grade = (560 - 469) / 1
minimum grade = 91
Therefore, the student needs to score at least 91 on the last test to get an 80% average.
NEW QUESTION # 68
Which of the following is TRUE about SVM models?
- A. They can be used only for regression.
- B. They use the sigmoid function to classify the data points.
- C. They can take the feature space into higher dimensions to solve the problem.
- D. They can be used only for classification.
Answer: C
Explanation:
Explanation
SVM models can use kernel functions to map the input data into higher-dimensional feature spaces, where linear separation is possible. This allows SVM models to handle non-linear problems effectively.
References: CertNexus Certified Artificial Intelligence Practitioner, Support vector machine - Wikipedia
NEW QUESTION # 69
Which two of the following decrease technical debt in ML systems? (Select two.)
- A. Boundary erosion
- B. Model complexity
- C. Documentation readability
- D. Design anti-patterns
- E. Refactoring
Answer: C,E
Explanation:
Explanation
Technical debt is a metaphor that describes the implied cost of additional work or rework caused by choosing an easy or quick solution over a better but more complex solution. Technical debt can accumulate in ML systems due to various factors, such as changing requirements, outdated code, poor documentation, or lack of testing. Some of the ways to decrease technical debt in ML systems are:
Documentation readability: Documentation readability refers to how easy it is to understand and use the documentation of an ML system. Documentation readability can help reduce technical debt by providing clear and consistent information about the system's design, functionality, performance, and maintenance. Documentation readability can also facilitate communication and collaboration among different stakeholders, such as developers, testers, users, and managers.
Refactoring: Refactoring is the process of improving the structure and quality of code without changing its functionality. Refactoring can help reduce technical debt by eliminating code smells, such as duplication, complexity, or inconsistency. Refactoring can also enhance the readability, maintainability, and extensibility of code.
NEW QUESTION # 70
Which of the following is a type 1 error in statistical hypothesis testing?
- A. The null hypothesis is false and is rejected.
- B. The null hypothesis is false, but fails to be rejected.
- C. The null hypothesis is true and fails to be rejected.
- D. The null hypothesis is true, but is rejected.
Answer: D
Explanation:
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. References: [Type I and type II errors - Wikipedia], [Type I Error and Type II Error - Statistics How To]
NEW QUESTION # 71
What is the primary benefit of the Federated Learning approach to machine learning?
- A. It does not require a labeled dataset to solve supervised learning problems.
- B. It protects the privacy of the user's data while providing well-trained models.
- C. It uses large, centralized data stores to train complex machine learning models.
- D. It requires less computation to train the same model using a traditional approach.
Answer: B
Explanation:
Federated learning is a distributed approach to machine learning that allows multiple parties to collaboratively train a model without sharing their data with each other or a central server. This protects the privacy of the user's data while still enabling well-trained models that can benefit from diverse and large-scale datasets.
References: [Federated Learning - Wikipedia], [Federated Learning for Mobile Keyboard Prediction - Google AI Blog]
NEW QUESTION # 72
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.
Answer: C
Explanation:
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.
NEW QUESTION # 73
Which of the following is NOT an activation function?
- A. Additive
- B. Hyperbolic tangent
- C. ReLU
- D. Sigmoid
Answer: A
Explanation:
An activation function is a function that determines the output of a neuron in a neural network based on its input. An activation function can introduce non-linearity into a neural network, which allows it to model complex and non-linear relationships between inputs and outputs. Some of the common activation functions are:
* Sigmoid: A sigmoid function is a function that maps any real value to a value between 0 and 1. It has an S-shaped curve and is often used for binary classification or probability estimation.
* Hyperbolic tangent: A hyperbolic tangent function is a function that maps any real value to a value between -1 and 1. It has a similar shape to the sigmoid function but is symmetric around the origin. It is often used for regression or classification problems.
* ReLU: A ReLU (rectified linear unit) function is a function that maps any negative value to 0 and any positive value to itself. It has a piecewise linear shape and is often used for hidden layers in deep neural networks.
Additive is not an activation function, but rather a term that describes a property of some functions. Additive functions are functions that satisfy the condition f(x+y) = f(x) + f(y) for any x and y. Additive functions are linear functions, which means they have a constant slope and do not introduce non-linearity.
NEW QUESTION # 74
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