AWS Glue FindMatches Failure
Question
You work as a machine learning specialist for an auto manufacturer that produces several car models in several product lines.
Example models include an LX model, an EX model, a Sport model, etc.
These models have many similarities.
But of course, they also have defining differences.
Each model has its own parts list entries in your company's parts database.
When ordering commodity parts for these car models from auto parts manufacturers, you want to produce the most efficient orders for each parts manufacturer by combining orders for similar parts lists.
This will save your company money.
You have decided to use the AWS Glue FindMatches Machine Learning Transform to find your matching parts lists. You have created your data source file as a CSV, and you have also created your labeling file used to train your FindMatches to transform.
When you run your AWS Glue transform job, it fails.
Which of the following could be the root of the problem?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: D.
Option A is incorrect.
When using the AWS Glue FindMatches ML Transform, the labeling file must be in CSV format.
Option B is incorrect.
When using the AWS Glue FindMatches ML Transform, the first two columns of the labeling file are required to be labeling_set_id and label.
Also, the remaining columns must match the schema of the data to be processed.
Option C is incorrect.
When using the AWS Glue FindMatches ML Transform, if a record doesn't have a match, it is assigned a unique label.
Option D is correct.
When using the AWS Glue FindMatches ML Transform, the labeling file must be encoded as UTF-8 without BOM.
Reference:
Please see the AWS Glue developer guide titled Machine Learning Transforms in AWS Glue.
AWS Glue is a fully-managed ETL (Extract, Transform, and Load) service that makes it easy to move data between data stores. AWS Glue FindMatches is a machine learning transform that helps find matches between records in two datasets using machine learning techniques.
In this scenario, the machine learning specialist wants to use the AWS Glue FindMatches transform to find matching parts lists for different car models to optimize the ordering of commodity parts and save costs. The specialist has created a CSV data source file and a labeling file to train the FindMatches transform. However, when the specialist runs the transform job, it fails, and they want to know what could be the root of the problem.
Let's analyze each answer choice to determine which one could be the cause of the problem:
A. The labeling file is in the CSV format. This is not likely to be the root cause of the problem because CSV is a supported format for labeling files in AWS Glue. Therefore, this answer choice can be eliminated.
B. The labeling file has labeling_set_id and label as its first two columns with the remaining columns matching the schema of the parts list data to be processed. This is the correct format for the labeling file to train the AWS Glue FindMatches transform. Therefore, this answer choice can be eliminated as the root cause of the problem.
C. Records in the labeling file that don't have any matches have unique labels. This is not likely to be the root cause of the problem because records in the labeling file that don't have any matches should have unique labels. This helps the transform distinguish between records that don't match any other records. Therefore, this answer choice can be eliminated.
D. The labeling file is not encoded in UTF-8 without BOM (byte order mark). This could be the root cause of the problem. AWS Glue FindMatches transform requires that the labeling file be encoded in UTF-8 without BOM (byte order mark). If the labeling file is encoded in a different format or has a BOM, the transform job may fail. Therefore, this answer choice is the most likely cause of the problem.
In summary, the root cause of the problem with the failed AWS Glue FindMatches transform job is likely to be the labeling file not being encoded in UTF-8 without BOM (byte order mark).
