Developing ML solutionsFull notesSummaryRingkasanStoriesPracticeDeveloping ML Solutions with Amazon SageMaker AI10 exam-style questions on this lesson.Question 1 of 10What is the recommended web-based interface for working across the whole SageMaker AI workflow?ASageMaker CanvasBAWS Management Console onlyCPartyRockDSageMaker StudioCheck answerQuestion 2 of 10A business analyst with no coding skills wants to build ML predictions using a visual, point-and-click interface. Which feature fits?ASageMaker CanvasBSageMaker PipelinesCSageMaker Feature StoreDSageMaker Studio notebooksCheck answerQuestion 3 of 10A team needs thousands of images labeled by people before training a model. Which feature fits?ASageMaker Model RegistryBSageMaker Ground TruthCSageMaker ClarifyDSageMaker Data WranglerCheck answerQuestion 4 of 10A data engineer wants a low-code way to import, clean, transform, and analyze data for ML. Which feature fits?ASageMaker AutopilotBSageMaker Ground TruthCSageMaker Data WranglerDSageMaker ExperimentsCheck answerQuestion 5 of 10Several teams keep recomputing the same customer features for training and for inference. Which feature lets them store and reuse features consistently?ASageMaker CanvasBSageMaker Model RegistryCAmazon S3 GlacierDSageMaker Feature StoreCheck answerQuestion 6 of 10A team wants SageMaker to automatically try algorithms and settings and build the best model from a tabular dataset. Which feature fits?ASageMaker AutopilotBSageMaker JumpStartCSageMaker Model MonitorDSageMaker ClarifyCheck answerQuestion 7 of 10A data scientist runs dozens of training runs with different settings and wants to compare their metrics and parameters. Which feature fits?ASageMaker PipelinesBSageMaker ExperimentsCSageMaker Ground TruthDSageMaker Model RegistryCheck answerQuestion 8 of 10A team needs to catalog model versions and record which version is approved for production. Which feature fits?ASageMaker Feature StoreBSageMaker ExperimentsCSageMaker Model RegistryDSageMaker CanvasCheck answerQuestion 9 of 10A company wants to automate its ML workflow from data preparation through training, evaluation, and deployment as a CI/CD pipeline. Which feature fits?ASageMaker ClarifyBSageMaker CanvasCSageMaker Ground TruthDSageMaker PipelinesCheck answerQuestion 10 of 10. Select two.Which TWO statements about SageMaker features are correct? (Select TWO.)AModel Monitor labels training dataBAutomatic model tuning performs hyperparameter optimizationCSageMaker JumpStart provides pre-trained models to start fromDGround Truth monitors models in productionESageMaker Canvas requires writing Python codeCheck answer0 of 2 selectedMachine Learning Development Lifecycle10 exam-style questions on this lesson.Sources of ML Models7 exam-style questions on this lesson.