MLA-C01 · Question #11
Case Study An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transactio
The correct answer is C. Use Amazon SageMaker Data Wrangler to automatically detect the anomalies and to visualize. Option C is correct because Amazon SageMaker Data Wrangler is purpose-built for exactly this use case - it includes built-in, automatic data quality and anomaly detection capabilities, and provides native visualization of those results, all within a single integrated workflow tha
Question
Case Study An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3. The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data. After the data is aggregated, the ML engineer must implement a solution to automatically detect anomalies in the data and to visualize the result. Which solution will meet these requirements?
Options
- AUse Amazon Athena to automatically detect the anomalies and to visualize the result.
- BUse Amazon Redshift Spectrum to automatically detect the anomalies. Use Amazon QuickSight
- CUse Amazon SageMaker Data Wrangler to automatically detect the anomalies and to visualize
- DUse AWS Batch to automatically detect the anomalies. Use Amazon QuickSight to visualize the
How the community answered
(57 responses)- A4% (2)
- B14% (8)
- C75% (43)
- D7% (4)
Explanation
Option C is correct because Amazon SageMaker Data Wrangler is purpose-built for exactly this use case - it includes built-in, automatic data quality and anomaly detection capabilities, and provides native visualization of those results, all within a single integrated workflow that fits naturally into the SageMaker ML pipeline.
Why the distractors fail:
- A (Athena): Athena is a SQL query engine for S3 data - it has no built-in anomaly detection or visualization; it's the wrong tool category entirely.
- B (Redshift Spectrum + QuickSight): Redshift Spectrum queries S3 via Redshift and handles storage/querying well, but it has no automatic anomaly detection; you'd need custom logic. QuickSight covers visualization but doesn't fill the detection gap.
- D (AWS Batch + QuickSight): AWS Batch is a general-purpose batch compute scheduler - it has zero built-in ML or anomaly detection; you'd have to build everything from scratch, which contradicts the "automatically detect" requirement.
Memory tip: When a question asks for automatic anomaly detection plus visualization in the SageMaker ecosystem, Data Wrangler is the answer - remember it as "Wrangle → Reveal": it wrangles your data quality issues and reveals them visually, in one tool, no custom code required.
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