Topic 1 Question 60
An ML engineer receives datasets that contain missing values, duplicates, and extreme outliers. The ML engineer must consolidate these datasets into a single data frame and must prepare the data for ML. Which solution will meet these requirements?
Use Amazon SageMaker Data Wrangler to import the datasets and to consolidate them into a single data frame. Use the cleansing and enrichment functionalities to prepare the data.
Use Amazon SageMaker Ground Truth to import the datasets and to consolidate them into a single data frame. Use the human-in-the-loop capability to prepare the data.
Manually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon Q Developer to generate code snippets that will prepare the data.
Manually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon SageMaker data labeling to prepare the data.
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コメント(2)
- 正解だと思う選択肢: A
This is basic use-case for data wrangler
👍 1GiorgioGss2024/11/27 - 正解だと思う選択肢: A
A: SageMaker Data Wrangler simplifies merging and cleaning datasets. (Correct answer) B: Ground Truth is for labeling, not cleaning. C: Manual merging is slow and inefficient. D: Data Labeling adds labels but doesn’t clean data.
👍 1Saransundar2024/12/04
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