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 Dec. 28, 2024 (see below)
You have a dataset with thousands of features, all of which are categorical. Using these features as predictors, you are tasked with creating a prediction model to accurately predict the value of a continuous dependent variable. Which of the following would be appropriate algorithms to use? (Select two.)
Lasso regression and ridge regression are both types of linear regression models that can handle high-dimensional and categorical data. They use regularization techniques to reduce the complexity of the model and avoid overfitting. Lasso regression uses L1 regularization, which adds a penalty term proportional to the absolute value of the coefficients to the loss function. This can shrink some coefficients to zero and perform feature selection. Ridge regression uses L2 regularization, which adds a penalty term proportional to the square of the coefficients to the loss function. This can shrink all coefficients towards zero and reduce multicollinearity. Reference: [Lasso (statistics) - Wikipedia], [Ridge regression - Wikipedia]
You are implementing a support-vector machine on your data, and a colleague suggests you use a polynomial kernel. In what situation might this help improve the prediction of your model?
A support-vector machine (SVM) is a supervised learning algorithm that can be used for classification or regression problems. An SVM tries to find an optimal hyperplane that separates the data into different categories or classes. However, sometimes the data is not linearly separable, meaning there is no straight line or plane that can separate them. In such cases, a polynomial kernel can help improve the prediction of the SVM by transforming the data into a higher-dimensional space where it becomes linearly separable. A polynomial kernel is a function that computes the similarity between two data points using a polynomial function of their features.
Which of the following tests should be performed at the production level before deploying a newly retrained model?
Performance testing is a type of testing that should be performed at the production level before deploying a newly retrained model. Performance testing measures how well the model meets the non-functional requirements, such as speed, scalability, reliability, availability, and resource consumption. Performance testing can help identify any bottlenecks or issues that may affect the user experience or satisfaction with the model. Reference: [Performance Testing Tutorial: What is, Types, Metrics & Example], [Performance Testing for Machine Learning Systems | by David Talby | Towards Data Science]
Which of the following is NOT a valid cross-validation method?
Stratification is not a valid cross-validation method, but a technique to ensure that each subset of data has the same proportion of classes or labels as the original data. Stratification can be used in conjunction with cross-validation methods such as k-fold or leave-one-out to preserve the class distribution and reduce bias or variance in the validation results. Bootstrapping, k-fold, and leave-one-out are all valid cross-validation methods that use different ways of splitting and resampling the data to estimate the performance of a machine learning model.
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?
Neural network models are suitable for classification problems with a large number of features, because they can learn complex and non-linear patterns from high-dimensional data. They can also handle image data, which is likely to be the input for the human face detection problem. Neural networks can also be trained using transfer learning, which can leverage pre-trained models on similar tasks and improve the accuracy and efficiency of the model. Reference: [Neural network - Wikipedia], [Transfer Learning - Machine Learning's Next Frontier]
- Select Question Types you want
- Set your Desired Pass Percentage
- Allocate Time (Hours : Minutes)
- Create Multiple Practice tests with Limited Questions
- Customer Support
Kassandra
3 days agoLelia
5 days agoLashawnda
20 days agoCarole
1 months agoTomoko
1 months agoGlenn
2 months agoTheresia
2 months agoValentin
2 months agoKami
3 months agoMalcom
3 months agoMeaghan
3 months agoYvonne
3 months agoStaci
4 months agoLera
4 months agoAdelaide
4 months agoTori
4 months agoWilliam
5 months agoTyra
6 months agoTegan
6 months agoMaryanne
6 months agoLorean
7 months agoSkye
7 months agoJamie
7 months agoAlex
8 months ago