Get 100% Passing Success With True AIP-210 Exam! [Dec-2025]
CertNexus AIP-210 PDF Questions - Exceptional Practice To CertNexus Certified Artificial Intelligence Practitioner (CAIP)
CertNexus AIP-210 Exam Syllabus Topics:
| Topic | Details |
|---|---|
| Topic 1 |
|
| Topic 2 |
|
| Topic 3 |
|
| Topic 4 |
|
NEW QUESTION # 32
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: D
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 # 33
When working with textual data and trying to classify text into different languages, which approach to representing features makes the most sense?
- A. Clustering similar words and representing words by group membership
- B. Bag of words model with TF-IDF
- C. Bag of bigrams (2 letter pairs)
- D. Word2Vec algorithm
Answer: C
Explanation:
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 # 34
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 training data used was inaccurate.
- B. The team set flawed expectations when training the model.
- C. The application was migrated from on-premise to a public cloud.
- D. The AI model was trained in winter and applied in summer.
Answer: D
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 # 35
Which database is designed to better anticipate and avoid risks of AI systems causing safety, fairness, or other ethical problems?
- A. Asset
- B. Configuration Management
- C. Code Repository
- D. Incident
Answer: D
Explanation:
An incident database is a database that is designed to better anticipate and avoid risks of AI systems causing safety, fairness, or other ethical problems. An incident database collects and stores information about incidents or events where AI systems have caused or contributed to negative outcomes or harms, such as accidents, errors, biases, discriminations, or violations. An incident database can help identify patterns, trends, causes, impacts, and solutions for AI-related incidents, as well as provide guidance and best practices for preventing or mitigating future incidents.
NEW QUESTION # 36
A healthcare company experiences a cyberattack, where the hackers were able to reverse-engineer a dataset to break confidentiality.
Which of the following is TRUE regarding the dataset parameters?
- A. The model is underfitted and trained on a high quantity of patient records.
- B. The model is overfitted and trained on a low quantity of patient records.
- C. The model is overfitted and trained on a high quantity of patient records.
- D. The model is underfitted and trained on a low quantity of patient records.
Answer: B
Explanation:
Explanation
Overfitting is a problem that occurs when a model learns too much from the training data and fails to generalize well to new or unseen data. Overfitting can result from using a low quantity of training data, a high complexity of the model, or a lack of regularization. Overfitting can also increase the risk of reverse-engineering a dataset from a model's outputs, as the model may reveal too much information about the specific features or patterns of the training data. This can break the confidentiality of the data and expose sensitive information about the individuals in the dataset .
NEW QUESTION # 37
Which of the following pieces of AI technology provides the ability to create fake videos?
- A. Recurrent neural networks (RNN)
- B. Generative adversarial networks (GAN)
- C. Long short-term memory (LSTM) networks
- D. Support-vector machines (SVM)
Answer: B
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 # 38
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. XGBoost
- B. Logistic regression
- C. Random forest
- D. Decision tree
Answer: C
NEW QUESTION # 39
Which of the following describes a typical use case of video tracking?
- A. Traffic monitoring
- B. Video composition
- C. Augmented dreaming
- D. Medical diagnosis
Answer: A
Explanation:
Video tracking is a technique that involves detecting and following moving objects in a video sequence.
Video tracking can be used for various applications, such as surveillance, security, sports analysis, and human- computer interaction. One typical use case of video tracking is traffic monitoring, where video tracking can help measure traffic flow, detect congestion, identify violations, and optimize traffic signals.
NEW QUESTION # 40
Which of the following equations best represent an LI norm?
- A. |x|^2+|y|^2
- B. |x|-|y|
- C. |x|+|y|^2
- D. |x| + |y|
Answer: D
Explanation:
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.
NEW QUESTION # 41
Which of the following statements are true regarding highly interpretable models? (Select two.)
- A. They are usually binary classifiers.
- B. They are usually referred to as "black box" models.
- C. They are usually very good at solving non-linear problems.
- D. They usually compromise on model accuracy for the sake of interpretability.
- E. They are usually easier to explain to business stakeholders.
Answer: D,E
Explanation:
Explanation
Highly interpretable models are models that can provide clear and intuitive explanations for their predictions, such as decision trees, linear regression, or logistic regression. Some of the statements that are true regarding highly interpretable models are:
They are usually easier to explain to business stakeholders: Highly interpretable models can help communicate the logic and reasoning behind their predictions, which can increase trust and confidence among business stakeholders. For example, a decision tree can show how each feature contributes to a decision outcome, or a linear regression can show how each coefficient affects the dependent variable.
They usually compromise on model accuracy for the sake of interpretability: Highly interpretable models may not be able to capture complex or non-linear patterns in the data, which can reduce their accuracy and generalization. For example, a decision tree may overfit or underfit the data if it is too deep or too shallow, or a linear regression may not be able to model curved relationships between variables.
NEW QUESTION # 42
What is the primary benefit of the Federated Learning approach to machine learning?
- A. It protects the privacy of the user's data while providing well-trained models.
- B. It does not require a labeled dataset to solve supervised learning problems.
- 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: A
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 # 43
Which of the following pieces of AI technology provides the ability to create fake videos?
- A. Recurrent neural networks (RNN)
- B. Generative adversarial networks (GAN)
- C. Long short-term memory (LSTM) networks
- D. Support-vector machines (SVM)
Answer: B
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 # 44
Which two encodes can be used to transform categories data into numerical features? (Select two.)
- A. Count Encoder
- B. Median Encoder
- C. One-Hot Encoder
- D. Log Encoder
- E. Mean Encoder
Answer: C,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 # 45
A change in the relationship between the target variable and input features is
- A. data drift.
- B. model decay.
- C. covariate shift.
- D. concept drift.
Answer: D
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 # 46
We are using the k-nearest neighbors algorithm to classify the new data points. The features are on different scales.
Which method can help us to solve this problem?
- A. Log transformation
- B. Normalization
- C. Standardization
- D. Square-root transformation
Answer: B
Explanation:
Normalization is a method that can help us to solve the problem of features being on different scales when using the k-nearest neighbors algorithm. Normalization is a technique that rescales the values of features to a common range, such as [0, 1] or [-1, 1]. Normalization can help reduce the influence or dominance of some features over others, as well as improve the accuracy and performance of the algorithm2.
NEW QUESTION # 47
Word Embedding describes a task in natural language processing (NLP) where:
- A. Words are featurized by taking a histogram of letter counts.
- B. Words are grouped together into clusters and then represented by word cluster membership.
- C. Words are featurized by taking a matrix of bigram counts.
- D. Words are converted into numerical vectors.
Answer: D
Explanation:
Explanation
Word embedding is a task in natural language processing (NLP) where words are converted into numerical vectors that represent their meaning, usage, or context. Word embedding can help reduce the dimensionality and sparsity of text data, as well as enable various operations and comparisons among words based on their vector representations. Some of the common methods for word embedding are:
One-hot encoding: One-hot encoding is a method that assigns a unique binary vector to each word in a vocabulary. The vector has only one element with a value of 1 (the hot bit) and the rest with a value of
0. One-hot encoding can create distinct and orthogonal vectors for each word, but it does not capture any semantic or syntactic information about words.
Word2vec: Word2vec is a method that 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 associations.
GloVe: GloVe (Global Vectors for Word Representation) is a method that combines the advantages of count-based methods (such as TF-IDF) and predictive methods (such as Word2vec) to create word vectors. GloVe can leverage both global and local information from a large corpus of text to capture the co-occurrence patterns and probabilities of words.
NEW QUESTION # 48
A data scientist is tasked to extract business intelligence from primary data captured from the public. Which of the following is the most important aspect that the scientist cannot forget to include?
- A. Data privacy
- B. Cybersecurity
- C. Cyberprotection
- D. Data security
Answer: A
Explanation:
Explanation
Data privacy is the right of individuals to control how their personal data is collected, used, shared, and protected. It also involves complying with relevant laws and regulations that govern the handling of personal data. Data privacy is especially important when extracting business intelligence from primary data captured from the public, as it may contain sensitive or confidential information that could harm the individuals if misused or breached .
NEW QUESTION # 49
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. Transparency
- D. Safety
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 # 50
When should the model be retrained in the ML pipeline?
- A. Concept drift is detected in the pipeline.
- B. More data become available for the training phase.
- C. Some outliers are detected in live data.
- D. A new monitoring component is added.
Answer: A
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 # 51
Which of the following describes a benefit of machine learning for solving business problems?
- A. Increasing the speed of analysis
- B. Improving the constraint of the problem
- C. Improving the quality of original data
- D. Increasing the quantity of original data
Answer: A
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.
NEW QUESTION # 52
When working with textual data and trying to classify text into different languages, which approach to representing features makes the most sense?
- A. Clustering similar words and representing words by group membership
- B. Bag of words model with TF-IDF
- C. Bag of bigrams (2 letter pairs)
- D. Word2Vec algorithm
Answer: C
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 # 53
Which two encoders can be used to transform categorical data into numerical features? (Select two.)
- A. Mean Encoder
- B. Median Encoder
- C. Count Encoder
- D. One-Hot Encoder
- E. Log Encoder
Answer: C,D
NEW QUESTION # 54
Which of the following is the correct definition of the quality criteria that describes completeness?
- A. The degree to which the measures conform to defined business rules or constraints.
- B. The degree to which all required measures are known.
- C. The degree to which a set of measures are specified using the same units of measure in all systems.
- D. The degree to which a set of measures are equivalent across systems.
Answer: B
Explanation:
Explanation
Completeness is a quality criterion that describes the degree to which all required measures are known.
Completeness can help assess the coverage and availability of data for a given purpose or analysis.
Completeness can be measured by comparing the actual number of measures with the expected number of measures, or by identifying and counting any missing, null, or unknown values in the data.
NEW QUESTION # 55
Which two techniques are used to build personas in the ML development lifecycle? (Select two.)
- A. Population variance
- B. Population regression
- C. Population triage
- D. Population estimates
- E. Population resampling
Answer: C,D
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 # 56
......
AIP-210 dumps - BootcampPDF - 100% Passing Guarantee: https://passleader.bootcamppdf.com/AIP-210-exam-actual-tests.html