AWS Certified Machine Learning Engineer Associate · Free Practice Question Medium
Question 37
A machine learning engineer is using Amazon SageMaker Studio to perform exploratory data analysis on a large dataset stored in Amazon S3. The analyst needs to create visualizations, run data preprocessing workflows, and share the results with team members. The analyst also wants to avoid setting up infrastructure manually. Considering these requirements, which SageMaker features should the analyst leverage to achieve these tasks efficiently? (Select THREE)
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A
Share SageMaker Studio notebooks directly with team members for collaboration, ensuring that they can access the same environment and results.
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B
Store the dataset in the SageMaker notebook instance's local storage to avoid the complexity of using external services like Amazon S3.
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C
Use Amazon SageMaker's Real-Time Inference Endpoints to deploy the model and generate immediate predictions on the dataset.
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D
Utilize SageMaker Studio's JupyterLab environment to interactively run code cells, visualize data, and tweak preprocessing workflows without worrying about underlying infrastructure.
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E
Use SageMaker Automatic Model Tuning to automatically handle hyperparameter optimization during the preprocessing and analysis steps.
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F
Take advantage of the built-in integrations with popular data visualization libraries such as Matplotlib and Seaborn to create interactive data visualizations.
Reveal correct answers
Correct answers: A, D, F
A.
SageMaker Studio allows sharing notebooks with other team members, enabling collaboration and shared access to results and workflows. This is especially useful for teams working together on the same dataset.
B.
Storing data locally on a SageMaker notebook instance would complicate the process. Instead, Amazon S3 offers better scalability, persistence, and integration for managing datasets.
C.
Real-Time Inference Endpoints are used for deploying models to serve predictions, but this is not relevant for preprocessing and visualization tasks.
D.
SageMaker Studio's JupyterLab environment provides an interactive space for running code, visualizing data, and modifying workflows without requiring manual infrastructure setup, making it ideal for data exploration and preprocessing tasks.
E.
SageMaker Automatic Model Tuning is useful during model training, but it does not apply to preprocessing or data analysis tasks.
F.
SageMaker Studio supports data visualization libraries such as Matplotlib and Seaborn, which can be used to create rich, interactive visualizations for the analysis.
Discussion
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