Free AIP-210 Practice Test Questions and Answers (2026) | Cert Empire Practice Questions

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.

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.

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.

Reference: 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

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.

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. Reference: [Distributed

artificial intelligence - Wikipedia], [Distributed Artificial Intelligence: An Overview]

Harms modeling is a technique that helps product managers design AI solutions responsibly by

evaluating both positive and negative outcomes. Harms modeling involves creating a shared

taxonomy of potential negative impacts and conducting an assessment along vectors such as

severity, impact, frequency, and likelihood. Harms modeling can help identify and mitigate any risks

or harms that may arise from using AI solutions. Reference: [Harms Modeling for Responsible AI | by

Google Developers | Google Developers], [Harms Modeling for Responsible AI - YouTube]

Ridge regression is a type of linear regression that adds a regularization term to the loss function to

reduce overfitting and improve generalization. Ridge regression is fast and requires low memory and

low processing power, as it only involves solving a system of linear equations. Ridge regression can

also handle multicollinearity (high correlation among predictors) by shrinking the coefficients of

correlated predictors.

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

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. Reference: 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

A false negative is an error where a positive case (belonging to the target class) is incorrectly

predicted as negative (not belonging to the target class). A false negative rate is the ratio of false

negatives to all actual positive cases. A low false negative rate means that most of the positive cases

are correctly identified by the classifier.

For a diagnostic tool that can diagnose a particular disease so that individuals with the disease can

receive treatment, it is of primary importance that it has a low false negative rate. This is because

false negatives can have serious consequences for patients who have the disease but do not receive

treatment, such as increased risk of mortality or complications. A low false negative rate can ensure

that most patients who have the disease are diagnosed correctly and receive timely treatment.

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. Reference: [Federated Learning - Wikipedia], [Federated Learning

for Mobile Keyboard Prediction - Google AI Blog]

Lasso regression is a type of linear regression that adds a regularization term to the loss function to

reduce overfitting and improve generalization. Lasso regression uses an L1 norm as the regularization

term, which is the sum of the absolute values of the coefficients. Lasso regression can shrink some of

the coefficients to zero, which effectively eliminates some of the features from the model. Lasso

regression is preferable over ridge regression when there are many features with no association with

the dependent variable, as it can perform feature selection and reduce the complexity and noise of

the model.

Reinforcement learning is a type of machine learning that involves learning from trial and error

based on rewards and penalties. Reinforcement learning can be used to develop models for dynamic

pathing, which is the problem of finding an optimal path from one point to another in an uncertain

and changing environment. Reinforcement learning can enable the model to adapt to new situations

and learn from its own actions and feedback. For example, a self-driving car company can use

reinforcement learning to train its model to navigate complex traffic scenarios and avoid collisions .

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.

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]