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 May. 10, 2026 (see below)
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.
Which two of the following criteria are essential for machine learning models to achieve before deployment? (Select two.)
Scalability and explainability are two criteria that are essential for ML models to achieve before deployment. Scalability is the ability of an ML model to handle increasing amounts of data or requests without compromising its performance or quality. Scalability can help ensure that the model can meet the demand and expectations of users or customers, as well as adapt to changing conditions or environments. Explainability is the ability of an ML model to provide clear and intuitive explanations for its predictions or decisions. Explainability can help increase trust and confidence among users or stakeholders, as well as enable accountability and responsibility for the model's actions and outcomes.
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?
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 .
R-squared is a statistical measure that:
R-squared is a statistical measure that indicates how well a regression model fits the data. R-squared is calculated by dividing the explained variance by the total variance. The explained variance is the amount of variation in the dependent variable that can be attributed to the independent variables. The total variance is the amount of variation in the dependent variable that can be observed in the data. R-squared ranges from 0 to 1, where 0 means no fit and 1 means perfect fit.
- Select Question Types you want
- Set your Desired Pass Percentage
- Allocate Time (Hours : Minutes)
- Create Multiple Practice tests with Limited Questions
- Customer Support
Amanda Moore
5 days agoEmma Davis
15 days agoHarold Robinson
7 days agoOlivia Mitchell
6 hours agoMaria Scott
10 days agoFabiola
1 month agoShayne
2 months agoKerry
2 months agoTaryn
2 months agoKatie
2 months agoMalcolm
3 months agoIlona
3 months agoWilson
3 months agoUla
3 months agoCandida
4 months agoLouann
4 months agoXenia
4 months agoLynsey
4 months agoIrma
5 months agoMargart
5 months agoMyrtie
5 months agoAsuncion
5 months agoIdella
6 months agoJohnetta
6 months agoShenika
6 months agoMarylin
6 months agoJestine
7 months agoCharlette
7 months agoChanel
7 months agoCatarina
7 months agoStevie
8 months agoDominque
8 months agoAlisha
8 months agoGwenn
10 months agoEleonora
11 months agoSalena
1 year agoKirby
1 year agoBarbra
1 year agoLawana
1 year agoKrystal
1 year agoKassandra
1 year agoLelia
1 year agoLashawnda
1 year agoCarole
1 year agoTomoko
1 year 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