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 Aug. 10, 2026 (see below)
You are developing a prediction model. Your team indicates they need an algorithm that is fast and requires low memory and low processing power. Assuming the following algorithms have similar accuracy on your data, which is most likely to be an ideal choice for the job?
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.
You are building a prediction model to develop a tool that can diagnose a particular disease so that individuals with the disease can receive treatment. The treatment is cheap and has no side effects. Patients with the disease who don't receive treatment have a high risk of mortality.
It is of primary importance that your diagnostic tool has which of the following?
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.
When should the model be retrained in the ML pipeline?
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
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.
- Select Question Types you want
- Set your Desired Pass Percentage
- Allocate Time (Hours : Minutes)
- Create Multiple Practice tests with Limited Questions
- Customer Support
Eric Morris
15 days agoJennifer Anderson
24 days agoAdam Martinez
2 months agoRonald Wilson
2 months agoDonna Nelson
3 months agoKevin Martinez
3 months agoAmanda Moore
3 months agoEmma Davis
4 months agoHarold Robinson
3 months agoOlivia Mitchell
3 months agoDavid Campbell
3 months agoMaria Scott
4 months agoMichael Rivera
3 months agoFabiola
4 months agoShayne
5 months agoKerry
5 months agoTaryn
5 months agoKatie
6 months agoMalcolm
6 months agoIlona
6 months agoWilson
6 months agoUla
7 months agoCandida
7 months agoLouann
7 months agoXenia
7 months agoLynsey
8 months agoIrma
8 months agoMargart
8 months agoMyrtie
8 months agoAsuncion
9 months agoIdella
9 months agoJohnetta
9 months agoShenika
9 months agoMarylin
10 months agoJestine
10 months agoCharlette
10 months agoChanel
10 months agoCatarina
11 months agoStevie
11 months agoDominque
11 months agoAlisha
11 months agoGwenn
1 year agoEleonora
1 year agoSalena
1 year agoKirby
1 year agoBarbra
1 year agoLawana
2 years agoKrystal
2 years 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