2026 Valid AIP-210 Exam Updates – 2026 Study Guide [Q52-Q72]

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2026 Valid AIP-210 Exam Updates – 2026 Study Guide

AIP-210 Certification – The Ultimate Guide [Updated 2026]

CertNexus AIP-210 Exam Syllabus Topics:

Topic	Details
Topic 1	Train, validate, and test data subsets Training and Tuning ML Systems and Models
Topic 2	Design machine and deep learning models Explain data collection transformation process in ML workflow
Topic 3	Address business risks, ethical concerns, and related concepts in training and tuning Work with textual, numerical, audio, or video data formats
Topic 4	Understanding the Artificial Intelligence Problem Analyze the use cases of ML algorithms to rank them by their success probability

NEW QUESTION 52

The graph is an elbow plot showing the inertia or within-cluster sum of squares on the y-axis and number of clusters (also called K) on the x-axis, denoting the change in inertia as the clusters change using k-means algorithm.
What would be an optimal value of K to ensure a good number of clusters?

NEW QUESTION 53
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?

SELECT m.Firstname, m.Lastname, d.Name, AVG(e.Saiary) as Dept_avg_Saiary FROM Employee_Table as e LEFT JOIN Department_Table as d on e.Dept = d.Name LEFT JOIN Directorjable as m on d.ID = m.DeptJD GROUP BY m.Firstname, m.Lastname, d.Name

SELECT m.Firstname, m.Lastname, d.Name, AVG(e.Salary) as Dept_avg_Salary FROM Employee_Table as e RIGHT JOIN Departmentjable as d on e.Dept = d.Name INNER JOIN Directorjable as m on d.ID = m.DeptJD GROUP BY d.Name

SELECT m.Firstname, m.Lastname, d.Name, AVG(e.Salary) as Dept_avg_Salary FROM Employee_Table as e RIGHT JOIN Department_Table as d on e.Dept = d.Name INNER JOIN Directorjable as m on d.ID = m.DeptJD GROUP BY e.Salary

NEW QUESTION 54
An organization sells house security cameras and has asked their data scientists to implement a model to detect human feces, as distinguished from animals, so they can alert th customers only when a human gets close to their house.
Which of the following algorithms is an appropriate option with a correct reason?

A decision tree algorithm, because the problem is a classification problem with a small number of features.

k-means, because this is a clustering problem with a small number of features.

Logistic regression, because this is a classification problem and our data is linearly separable.

Neural network model, because this is a classification problem with a large number of features.

NEW QUESTION 55
Which of the following tests should be performed at the production level before deploying a newly retrained model?

A/Btest

Performance test

Security test

Unit test

NEW QUESTION 56
Which two encoders can be used to transform categorical data into numerical features? (Select two.)

Count Encoder

Log Encoder

Mean Encoder

Median Encoder

One-Hot Encoder

NEW QUESTION 57
Which of the following pieces of AI technology provides the ability to create fake videos?

Generative adversarial networks (GAN)

Support-vector machines (SVM)

Recurrent neural networks (RNN)

Long short-term memory (LSTM) networks

NEW QUESTION 58
Which database is designed to better anticipate and avoid risks of AI systems causing safety, fairness, or other ethical problems?

Asset

Code Repository

Configuration Management

Incident

NEW QUESTION 59
Which of the following can take a question in natural language and return a precise answer to the question?

Databricks

IBM Watson

Pandas

Spark ML

NEW QUESTION 60
Which of the following tools would you use to create a natural language processing application?

AWS DeepRacer

Azure Search

DeepDream

NLTK

NEW QUESTION 61
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?

General Data Protection Regulation (GDPR)

ISO/IEC 27001

PCIDSS

Sarbanes Oxley (SOX)

NEW QUESTION 62
Which of the following describes a neural network without an activation function?

A form of a linear regression

A form of a quantile regression

An unsupervised learning technique

A radial basis function kernel

NEW QUESTION 63
When working with textual data and trying to classify text into different languages, which approach to representing features makes the most sense?

Bag of words model with TF-IDF

Bag of bigrams (2 letter pairs)

Word2Vec algorithm

Clustering similar words and representing words by group membership

NEW QUESTION 64
In which of the following scenarios is lasso regression preferable over ridge regression?

The number of features is much larger than the sample size.

There are many features with no association with the dependent variable.

There is high collinearity among some of the features associated with the dependent variable.

The sample size is much larger than the number of features.

NEW QUESTION 65
Which of the following is the definition of accuracy?

(True Positives + False Positives) / Total Predictions

(True Positives + True Negatives) / Total Predictions

True Positives / (True Positives + False Negatives)

True Positives / (True Positives + False Positives)

NEW QUESTION 66
Which of the following is a common negative side effect of not using regularization?

Overfitting

Slow convergence time

Higher compute resources

Low test accuracy

NEW QUESTION 67
Which of the following text vectorization methods is appropriate and correctly defined for an English-to- Spanish translation machine?

Using TF-IDF because in translation machines, we do not care about the order of the words.

Using TF-IDF because in translation machines, we need to consider the order of the words.

Using Word2vec because in translation machines, we do not care about the order of the words.

Using Word2vec because in translation machines, we need to consider the order of the words.

NEW QUESTION 68
When working with textual data and trying to classify text into different languages, which approach to representing features makes the most sense?

Bag of words model with TF-IDF

Bag of bigrams (2 letter pairs)

Word2Vec algorithm

Clustering similar words and representing words by group membership

NEW QUESTION 69
In addition to understanding model performance, what does continuous monitoring of bias and variance help ML engineers to do?

Detect hidden attacks

Prevent hidden attacks

Recover from hidden attacks

Respond to hidden attacks

NEW QUESTION 70
Which of the following is NOT a valid cross-validation method?

Bootstrapping

K-fold