Free Preparation Discussions
MENU
CertNexus AIP-210 Exam Questions
- Topic 1: Identify potential ethical concerns/ Analyze machine learning system use cases
- Topic 2: Train, validate, and test data subsets/ Training and Tuning ML Systems and Models
- Topic 3: Recognize relative impact of data quality and size to algorithms/ Engineering Features for Machine Learning
- Topic 4: Transform numerical and categorical data/ Address business risks, ethical concerns, and related concepts in operationalizing the model
- Topic 5: Understanding the Artificial Intelligence Problem/ Analyze the use cases of ML algorithms to rank them by their success probability
- Topic 6: Address business risks, ethical concerns, and related concepts in training and tuning/ Work with textual, numerical, audio, or video data formats
- Topic 7: Design machine and deep learning models/ Explain data collection/transformation process in ML workflow
Free CertNexus AIP-210 Exam Actual Questions
Note: Premium Questions for AIP-210 were last updated On Jun. 18, 2026 (see below)
For each of the last 10 years, your team has been collecting data from a group of subjects, including their age and numerous biomarkers collected from blood samples. You are tasked with creating a prediction model of age using the biomarkers as input. You start by performing a linear regression using all of the data over the 10-year period, with age as the dependent variable and the biomarkers as predictors.
Which assumption of linear regression is being violated?
Independence is an assumption of linear regression that states that the errors (residuals) of the model are independent of each other, meaning that they are not correlated or influenced by previous or subsequent errors. Independence can be violated when the data has serial correlation or autocorrelation, which means that the value of a variable at a given time depends on its previous or future values. This can happen when the data is collected over time (time series) or over space (spatial data). In this case, the data is collected over time from a group of subjects, which may introduce serial correlation among the errors.
Which of the following algorithms is an example of unsupervised learning?
Unsupervised learning is a type of machine learning that involves finding patterns or structures in unlabeled data without any predefined outcome or feedback. Unsupervised learning can be used for various tasks, such as clustering, dimensionality reduction, anomaly detection, or association rule mining. Some of the common algorithms for unsupervised learning are:
Principal components analysis: Principal components analysis (PCA) is a method that reduces the dimensionality of data by transforming it into a new set of orthogonal variables (principal components) that capture the maximum amount of variance in the data. PCA can help simplify and visualize high-dimensional data, as well as remove noise or redundancy from the data.
K-means clustering: K-means clustering is a method that partitions data into k groups (clusters) based on their similarity or distance. K-means clustering can help discover natural or hidden groups in the data, as well as identify outliers or anomalies in the data.
Apriori algorithm: Apriori algorithm is a method that finds frequent itemsets (sets of items that occur together frequently) and association rules (rules that describe how items are related or correlated) in transactional data. Apriori algorithm can help discover patterns or insights in the data, such as customer behavior, preferences, or recommendations.
Which of the following describes a typical use case of video tracking?
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.
Which of the following can take a question in natural language and return a precise answer to the question?
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.
You train a neural network model with two layers, each layer having four nodes, and realize that the model is underfit. Which of the actions below will NOT work to fix this underfitting?
Underfitting is a problem that occurs when a model learns too little from the training data and fails to capture the underlying complexity or structure of the data. Underfitting can result from using insufficient or irrelevant features, a low complexity of the model, or a lack of training data. Underfitting can reduce the accuracy and generalization of the model, as it may produce oversimplified or inaccurate predictions. Some of the ways to fix underfitting are:
Add features to training data: Adding more features or variables to the training data can help increase the information and diversity of the data, which can help the model learn more complex patterns and relationships.
Increase the complexity of the model: Increasing the complexity of the model can help increase its expressive power and flexibility, which can help it fit better to the data. For example, adding more layers or nodes to a neural network can increase its complexity.
Train the model for more epochs: Training the model for more epochs can help increase its learning ability and convergence, which can help it optimize its parameters and reduce its error.
Getting more training data will not work to fix underfitting, as it will not change the complexity or structure of the data or the model. Getting more training data may help with overfitting, which is when a model learns too much from the training data and fails to generalize well to new or unseen data.
- Select Question Types you want
- Set your Desired Pass Percentage
- Allocate Time (Hours : Minutes)
- Create Multiple Practice tests with Limited Questions
- Customer Support
Ronald Wilson
6 days agoDonna Nelson
28 days agoKevin Martinez
1 month agoAmanda Moore
2 months agoEmma Davis
2 months agoHarold Robinson
2 months agoOlivia Mitchell
2 months agoDavid Campbell
1 month agoMaria Scott
2 months agoMichael Rivera
1 month agoFabiola
3 months agoShayne
3 months agoKerry
3 months agoTaryn
4 months agoKatie
4 months agoMalcolm
4 months agoIlona
4 months agoWilson
5 months agoUla
5 months agoCandida
5 months agoLouann
5 months agoXenia
6 months agoLynsey
6 months agoIrma
6 months agoMargart
6 months agoMyrtie
7 months agoAsuncion
7 months agoIdella
7 months agoJohnetta
7 months agoShenika
8 months agoMarylin
8 months agoJestine
8 months agoCharlette
8 months agoChanel
9 months agoCatarina
9 months agoStevie
9 months agoDominque
10 months agoAlisha
10 months agoGwenn
12 months agoEleonora
1 year agoSalena
1 year agoKirby
1 year agoBarbra
1 year agoLawana
1 year agoKrystal
1 year agoKassandra
2 years agoLelia
2 years agoLashawnda
2 years agoCarole
2 years agoTomoko
2 years agoGlenn
2 years agoTheresia
2 years agoValentin
2 years agoKami
2 years agoMalcom
2 years agoMeaghan
2 years agoYvonne
2 years agoStaci
2 years agoLera
2 years agoAdelaide
2 years agoTori
2 years agoWilliam
2 years agoTyra
2 years agoTegan
2 years agoMaryanne
2 years agoLorean
2 years agoSkye
2 years agoJamie
2 years agoAlex
2 years ago