AWS ML Algorithm Selection Quiz

1. You have sparse, high-dimensional data for click-through rate prediction. Which algorithm handles this well?

Linear Regression

Factorization Machines

DeepAR

Semantic Segmentation

2. For large-scale regression with regularization (L1/L2), which linear algorithm is optimal?

Linear Learner

K-Means Clustering

Factorization Machines

DeepAR

3. Which algorithm discovers latent semantic relationships in text using matrix factorization?

Neural Topic Model

Latent Semantic Indexing

K-Means Clustering

XGBoost

4. For multivariate time-series forecasting with multiple related series, which algorithm is suitable?

Linear Regression

DeepAR

K-Means

Apriori

5. Which algorithm learns embeddings for similar objects in the same vector space?

Linear Regression

Object2Vec

Apriori

DeepAR

6. For object detection in images, which deep learning technique is commonly used?

Object Detection

Logistic Regression

K-Means Clustering

Latent Dirichlet Allocation

7. You need to perform semantic segmentation on satellite images. Which algorithm applies?

Linear Learner

Semantic Segmentation

Apriori

DeepAR

8. For sequence-to-sequence tasks like machine translation, which architecture is standard?

Linear Regression

Sequence-to-Sequence

K-Means Clustering

Apriori Algorithm

9. Which algorithm is used to find association rules in transactional data (e.g., market basket analysis)?

K-Means

Apriori

XGBoost

DeepAR

10. For image classification tasks, which deep learning architecture is most appropriate?

Convolutional Neural Network

Recurrent Neural Network

Linear Learner

Principal Component Analysis

11. You need to predict continuous house prices from features like square footage and location. Which algorithm is most appropriate?

Linear Regression

K-Means Clustering

Random Cut Forest

Latent Dirichlet Allocation

12. Which algorithm is best for discovering patterns in text documents, such as topic modeling?

Latent Dirichlet Allocation

K-Means Clustering

Factorization Machines

DeepAR

13. For natural language processing tasks like sentiment analysis, which neural network architecture is commonly used?

Convolutional Neural Network

Recurrent Neural Network

Principal Component Analysis

Apriori Algorithm

14. You need to detect anomalies in streaming data. Which unsupervised algorithm is ideal?

XGBoost

Random Cut Forest

K-Nearest Neighbors

Latent Dirichlet Allocation

15. For multi-class classification with more than two categories, which algorithm is suitable?

Linear Regression

Logistic Regression with softmax

Random Cut Forest

DeepAR

16. Which algorithm is best for product recommendations based on user-item interactions?

Linear Learner

Factorization Machines

Image Classification

Semantic Segmentation

17. You want to reduce 100 features to 10 while preserving variance. Which dimensionality reduction technique applies?

K-Means Clustering

Principal Component Analysis

Latent Semantic Indexing

Sequence-to-Sequence

18. For time-series forecasting of daily website traffic, which AWS ML algorithm is purpose-built?

DeepAR

Factorization Machines

Neural Topic Model

Object2Vec

19. You are building a binary classifier to detect fraudulent transactions. Which algorithm typically provides the best accuracy for this task?

Linear Regression

XGBoost

Principal Component Analysis

Apriori

20. Your dataset has 1 million unlabeled customer records and you want to segment them into 5 distinct groups. What algorithm should you use?

XGBoost

K-Means

Logistic Regression

DeepAR

AWS ML Specialty Algorithm Selection Quiz

1. You have sparse, high-dimensional data for click-through rate prediction. Which algorithm handles this well?

2.

What first name or nickname would you like us to use?

2. For large-scale regression with regularization (L1/L2), which linear algorithm is optimal?

3. Which algorithm discovers latent semantic relationships in text using matrix factorization?

4. For multivariate time-series forecasting with multiple related series, which algorithm is suitable?

5. Which algorithm learns embeddings for similar objects in the same vector space?

6. For object detection in images, which deep learning technique is commonly used?

7. You need to perform semantic segmentation on satellite images. Which algorithm applies?

8. For sequence-to-sequence tasks like machine translation, which architecture is standard?

9. Which algorithm is used to find association rules in transactional data (e.g., market basket analysis)?

10. For image classification tasks, which deep learning architecture is most appropriate?

11. You need to predict continuous house prices from features like square footage and location. Which algorithm is most appropriate?

12. Which algorithm is best for discovering patterns in text documents, such as topic modeling?

13. For natural language processing tasks like sentiment analysis, which neural network architecture is commonly used?

14. You need to detect anomalies in streaming data. Which unsupervised algorithm is ideal?

15. For multi-class classification with more than two categories, which algorithm is suitable?

16. Which algorithm is best for product recommendations based on user-item interactions?

17. You want to reduce 100 features to 10 while preserving variance. Which dimensionality reduction technique applies?

18. For time-series forecasting of daily website traffic, which AWS ML algorithm is purpose-built?

19. You are building a binary classifier to detect fraudulent transactions. Which algorithm typically provides the best accuracy for this task?

20. Your dataset has 1 million unlabeled customer records and you want to segment them into 5 distinct groups. What algorithm should you use?