AWS Lake Formation FindMatches for Record Matching
A company ingests data from multiple data sources and stores the data in an Amazon S3 bucket. An AWS Glue extract, transform, and load (ETL) job transforms the data and writes the transformed data to an Amazon S3 based data lake. The company uses Amazon Athena to query the data that is in the data lake. The company needs to identify matching records even when the records do not have a common unique identifier. Which solution will meet this requirement?
Community Votes
100% of anonymous learners picked answer D. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The exam tests knowledge of specific AWS Lake Formation capabilities, specifically the 'FindMatches' transform, which is designed for record linkage and deduplication without exact key matches.
This question addresses using AWS Lake Formation's built-in ML transform to identify matching records without unique identifiers in an ETL pipeline. The correct solution leverages automated machine learning rather than manual pattern matching or filtering.
Candidates often choose Amazon Macie (Option A) because it involves data discovery and patterns, but Macie is focused on security and PII detection, not general record matching for business logic.
Community Discussion (3 comments)
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Expert Analysis
Why the Answer Is Correct
AWS Lake Formation provides a native ML transform called FindMatches within its Glue ETL integration. This transform uses machine learning models trained on sample data to identify duplicate or matching records across datasets, even when there is no common unique identifier (like a primary key) and fields do not match exactly. It automates the complex task of record linkage, which fits the scenario perfectly.Why the Other Options Are Wrong
Amazon Macie (A) is a security service that uses machine learning to discover sensitive data (PII) and enforce policies; it does not perform general-purpose record matching for business data integration. AWS Glue PySpark Filter class (B) requires predefined logical conditions and cannot intelligently infer matches between non-identical fields without extensive custom coding and heuristics. Partitioning tables (C) organizes data for query performance but does not solve the problem of identifying which rows are duplicates or matches.Community Comment Notes
Community consensus strongly supports D. As Shanmahi noted, "The FindMatches transform enables you to identify duplicate or matching records... even when the records do not have a common unique identifier." Another user confirmed simply that "Correct answer is D," reflecting the straightforward nature of this specific feature knowledge point.Exam Strategy
Memorize the specific ML transforms available in AWS Lake Formation, particularly FindMatches for deduplication and MatchLabels for classification. When a question mentions 'matching records without unique IDs,' think immediately of Lake Formation's ML capabilities rather than standard Glue transformations or security tools.
Frequently Asked Questions
Can I use Amazon Macie for record matching?
No. Macie is for security and PII discovery, not for business logic record linkage or deduplication.
Is FindMatches part of standard AWS Glue?
It is integrated with Glue ETL jobs but is a specific capability provided by AWS Lake Formation's ML features.
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