AI use cases and applicationsFull notesSummaryRingkasanStoriesPracticeMachine Learning Techniques and Use Cases10 exam-style questions on this lesson.Question 1 of 10A real estate company wants to predict the sale price of houses from past sales that include the final price. Which technique fits?AClassificationBDimensionality reductionCClusteringDRegressionCheck answerQuestion 2 of 10A telecom company wants to predict whether each customer will cancel their contract next month, using historical data about which customers left. Which technique fits?AClassificationBReinforcement learningCClusteringDRegressionCheck answerQuestion 3 of 10A marketing team has customer data with no labels and wants to divide customers into groups for targeted campaigns. Which technique fits?AClassificationBClusteringCReinforcement learningDRegressionCheck answerQuestion 4 of 10A dataset has 500 features, which makes training slow and charts impossible to read. The team wants fewer features while keeping the important information. Which technique fits?AData augmentationBClusteringCDimensionality reductionDRegressionCheck answerQuestion 5 of 10In supervised learning, what acts as the "supervisor"?AThe reward functionBThe number of clustersCThe data scientist watching trainingDThe labels in the training dataCheck answerQuestion 6 of 10When is reinforcement learning the right choice?AWhen you know what a good outcome is but not the steps to reach itBWhen you need to predict a continuous number from historyCWhen you have labeled data and need a categoryDWhen you need to group unlabeled dataCheck answerQuestion 7 of 10In AWS DeepRacer, what is the agent?AThe race trackBThe carCThe throttle and steeringDThe reward functionCheck answerQuestion 8 of 10. Select two.Which TWO use cases are typical for classification? (Select TWO.)ASales forecastingBCustomer segmentation without labelsCFraud detectionDHouse price estimationEMedical diagnosticsCheck answer0 of 2 selectedQuestion 9 of 10A streaming service wants to recommend shows by grouping viewers with similar tastes, without labeled data. Which learning type and technique fit?ASupervised learning, classificationBSupervised learning, regressionCUnsupervised learning, clusteringDReinforcement learning, reward shapingCheck answerQuestion 10 of 10A retailer wants to forecast next month's demand for each product in units sold, based on labeled sales history. Which technique fits?ADimensionality reductionBClusteringCClassificationDRegressionCheck answerMachine Learning5 exam-style questions on this lesson.Generative AI3 exam-style questions on this lesson.