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 #61
Which of the following describes a typical use case of video tracking?
- A . Augmented dreaming
- B . Medical diagnosis
- C . Traffic monitoring
- D . Video composition
Correct Answer: C
C
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.
C
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.
Question #62
Which of the following pieces of AI technology provides the ability to create fake videos?
- A . Generative adversarial networks (GAN)
- B . Long short-term memory (LSTM) networks
- C . Recurrent neural networks (RNN)
- D . Support-vector machines (SVM)
Correct Answer: A
A
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.
A
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.
Question #63
Which of the following is the correct definition of the quality criteria that describes completeness?
- A . The degree to which all required measures are known.
- B . The degree to which a set of measures are equivalent across systems.
- 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 the measures conform to defined business rules or constraints.
Correct Answer: A
A
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.
A
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.
Question #64
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 . Destroy
- C . Detain
- D . Duplicate
Correct Answer: B
B
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
B
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
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 . Sampling bias
- C . Stereotype bias
- D . Systematic value distortion
Correct Answer: B
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.
Reference: [Sampling bias – Wikipedia], [What is Sampling Bias? Definition, Types and Examples]
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.
Reference: [Sampling bias – Wikipedia], [What is Sampling Bias? Definition, Types and Examples]
Question #66
When should the model be retrained in the ML pipeline?
- A . A new monitoring component is added.
- B . Concept drift is detected in the pipeline.
- C . More data become available for the training phase.
- D . Some outliers are detected in live data.
Correct Answer: B
B
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.
Reference: Use pipeline parameters to retrain models in the designer – Azure Machine Learning | Microsoft Learn, Retraining Model During Deployment: Continuous Training and Continuous Testing
B
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.
Reference: Use pipeline parameters to retrain models in the designer – Azure Machine Learning | Microsoft Learn, Retraining Model During Deployment: Continuous Training and Continuous Testing
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 . 82
- B . 89
- C . 91
- D . 94
Correct Answer: C
C
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.
C
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.
Question #68
Which of the following statements are true regarding highly interpretable models? (Select two.)
- A . They are usually binary classifiers.
- B . They are usually easier to explain to business stakeholders.
- C . They are usually referred to as "black box" models.
- D . They are usually very good at solving non-linear problems.
- E . They usually compromise on model accuracy for the sake of interpretability.
Correct Answer: BE
BE
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.
BE
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.
Question #69
Which of the following is the primary purpose of hyperparameter optimization?
- A . Controls the learning process of a given algorithm
- B . Makes models easier to explain to business stakeholders
- C . Improves model interpretability
- D . Increases recall over precision
Correct Answer: A
A
Explanation:
Hyperparameter optimization is the process of finding the optimal values for hyperparameters that control the learning process of a given algorithm. Hyperparameters are parameters that are not learned by the algorithm but are set by the user before training. Hyperparameters can affect the performance and behavior of the algorithm, such as its speed, accuracy, complexity, or generalization. Hyperparameter optimization can help improve the efficiency and effectiveness of the algorithm by tuning its hyperparameters to achieve the best results.
A
Explanation:
Hyperparameter optimization is the process of finding the optimal values for hyperparameters that control the learning process of a given algorithm. Hyperparameters are parameters that are not learned by the algorithm but are set by the user before training. Hyperparameters can affect the performance and behavior of the algorithm, such as its speed, accuracy, complexity, or generalization. Hyperparameter optimization can help improve the efficiency and effectiveness of the algorithm by tuning its hyperparameters to achieve the best results.
Question #70
An HR solutions firm is developing software for staffing agencies that uses machine learning.
The team uses training data to teach the algorithm and discovers that it generates lower employability scores for women. Also, it predicts that women, especially with children, are less likely to get a high-paying job.
Which type of bias has been discovered?
- A . Automation
- B . Emergent
- C . Preexisting
- D . Technical
Correct Answer: C
C
Explanation:
Preexisting bias is a type of bias that originates from historical or social contexts, such as stereotypes, prejudices, or discriminations. Preexisting bias can affect the data or the algorithm used for machine learning, as well as the outcomes or decisions made by machine learning. Preexisting bias can cause unfair or harmful impacts on certain groups or individuals based on their attributes, such as gender, race, age, or disability3. In this case, the software that uses machine learning generates lower employability scores for women and predicts that women, especially with children, are less likely to get a high-paying job. This indicates that the software has preexisting bias against women, which may reflect the historical or social inequalities or expectations in the labor market.
C
Explanation:
Preexisting bias is a type of bias that originates from historical or social contexts, such as stereotypes, prejudices, or discriminations. Preexisting bias can affect the data or the algorithm used for machine learning, as well as the outcomes or decisions made by machine learning. Preexisting bias can cause unfair or harmful impacts on certain groups or individuals based on their attributes, such as gender, race, age, or disability3. In this case, the software that uses machine learning generates lower employability scores for women and predicts that women, especially with children, are less likely to get a high-paying job. This indicates that the software has preexisting bias against women, which may reflect the historical or social inequalities or expectations in the labor market.