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…

Question

Options

• AUse Amazon SageMaker Data Wrangler to import the datasets and to consolidate them into a
• BUse Amazon SageMaker Ground Truth to import the datasets and to consolidate them into a
• CManually import and merge the datasets. Consolidate the datasets into a single data frame. Use
• DManually import and merge the datasets. Consolidate the datasets into a single data frame. Use

• A
  70% (21)
• B
  10% (3)
• C
  17% (5)
• D
  3% (1)

Explanation

Amazon SageMaker Data Wrangler is the purpose-built AWS tool for exactly this scenario - it provides a visual, low-code interface to import multiple datasets, consolidate them, and apply transformations like handling missing values, removing duplicates, and clipping outliers, all before exporting the prepared data for ML training.

Why the distractors fail:

• B (SageMaker Ground Truth) is a data labeling service for creating training labels (e.g., image annotations), not a data cleaning or consolidation tool.
• C & D (Manual import + pandas/custom code) describe valid Python workflows but are not the best solution when a managed AWS service exists - AWS exam questions typically favor managed services that reduce operational overhead and address the full set of requirements out of the box.

Memory tip: Think of Data Wrangler = Data Janitor - it wrangles messy, multi-source data (missing values, duplicates, outliers) into a clean, ML-ready form, while Ground Truth is about labeling clean data, not cleaning messy data.

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