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Question 36 - MLS-C01 discussion

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A retail company wants to combine its customer orders with the product description data from its product catalog. The structure and format of the records in each dataset is different. A data analyst tried to use a spreadsheet to combine the datasets, but the effort resulted in duplicate records and records that were not properly combined. The company needs a solution that it can use to combine similar records from the two datasets and remove any duplicates.

Which solution will meet these requirements?

A.
Use an AWS Lambda function to process the data. Use two arrays to compare equal strings in the fields from the two datasets and remove any duplicates.
Answers
A.
Use an AWS Lambda function to process the data. Use two arrays to compare equal strings in the fields from the two datasets and remove any duplicates.
B.
Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Call the AWS Glue SearchTables API operation to perform a fuzzy-matching search on the two datasets, and cleanse the data accordingly.
Answers
B.
Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Call the AWS Glue SearchTables API operation to perform a fuzzy-matching search on the two datasets, and cleanse the data accordingly.
C.
Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data.
Answers
C.
Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data.
D.
Create an AWS Lake Formation custom transform. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically.
Answers
D.
Create an AWS Lake Formation custom transform. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically.
Suggested answer: C

Explanation:

The FindMatches transform is a machine learning transform that can identify and match similar records from different datasets, even when the records do not have a common unique identifier or exact field values. The FindMatches transform can also remove duplicate records from a single dataset. The FindMatches transform can be used with AWS Glue crawlers and jobs to process the data from various sources and store it in a data lake. The FindMatches transform can be created and managed using the AWS Glue console, API, or AWS Glue Studio.

The other options are not suitable for this use case because:

Option A: Using an AWS Lambda function to process the data and compare equal strings in the fields from the two datasets is not an efficient or scalable solution. It would require writing custom code and handling the data loading and cleansing logic. It would also not account for variations or inconsistencies in the field values, such as spelling errors, abbreviations, or missing data.

Option B: The AWS Glue SearchTables API operation is used to search for tables in the AWS Glue Data Catalog based on a set of criteria. It is not a machine learning transform that can match records across different datasets or remove duplicates. It would also require writing custom code to invoke the API and process the results.

Option D: AWS Lake Formation does not provide a custom transform feature. It provides predefined blueprints for common data ingestion scenarios, such as database snapshot, incremental database, and log file. These blueprints do not support matching records across different datasets or removing duplicates.

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asked 16/09/2024
Hristo Slaveev
25 questions
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