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Amazon Exam MLS-C01 Topic 2 Question 99 Discussion

Actual exam question for Amazon's MLS-C01 exam
Question #: 99
Topic #: 2
[All MLS-C01 Questions]

A law firm handles thousands of contracts every day. Every contract must be signed. Currently, a lawyer manually checks all contracts for signatures.

The law firm is developing a machine learning (ML) solution to automate signature detection for each contract. The ML solution must also provide a confidence score for each contract page.

Which Amazon Textract API action can the law firm use to generate a confidence score for each page of each contract?

Show Suggested Answer Hide Answer
Suggested Answer: A

The AnalyzeDocument API action is the best option to generate a confidence score for each page of each contract. This API action analyzes an input document for relationships between detected items. The input document can be an image file in JPEG or PNG format, or a PDF file. The output is a JSON structure that contains the extracted data from the document. The FeatureTypes parameter specifies the types of analysis to perform on the document. The available feature types are TABLES, FORMS, and SIGNATURES. By setting the FeatureTypes parameter to SIGNATURES, the API action will detect and extract information about signatures from the document. The output will include a list of SignatureDetection objects, each containing information about a detected signature, such as its location and confidence score. The confidence score is a value between 0 and 100 that indicates the probability that the detected signature is correct. The output will also include a list of Block objects, each representing a document page. Each Block object will have a Page attribute that contains the page number and a Confidence attribute that contains the confidence score for the page. The confidence score for the page is the average of the confidence scores of the blocks that are detected on the page. The law firm can use the AnalyzeDocument API action to generate a confidence score for each page of each contract by using the SIGNATURES feature type and returning the confidence scores from the SignatureDetection and Block objects.

The other options are not suitable for generating a confidence score for each page of each contract. The Prediction API call is not an Amazon Textract API action, but a generic term for making inference requests to a machine learning model. The StartDocumentAnalysis API action is used to start an asynchronous job to analyze a document. The output is a job identifier (JobId) that is used to get the results of the analysis with the GetDocumentAnalysis API action. The GetDocumentAnalysis API action is used to get the results of a document analysis started by the StartDocumentAnalysis API action. The output is a JSON structure that contains the extracted data from the document. However, both the StartDocumentAnalysis and the GetDocumentAnalysis API actions do not support the SIGNATURES feature type, and therefore cannot detect signatures or provide confidence scores for them.

References:

* AnalyzeDocument

* SignatureDetection

* Block

* Amazon Textract launches the ability to detect signatures on any document


Contribute your Thoughts:

Julio
3 months ago
I think using the AnalyzeDocument API action makes the most sense. It specifically mentions returning confidence scores for each page.
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Jade
3 months ago
Haha, the lawyers must be thrilled to have this automation. No more paper cuts from all those contracts!
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Gwen
2 months ago
C) Use the StartDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page.
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Lajuana
3 months ago
A) Use the AnalyzeDocument API action. Set the FeatureTypes parameter to SIGNATURES. Return the confidence scores for each page.
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Merilyn
3 months ago
I'm not sure. Maybe we should also consider using the StartDocumentAnalysis API action.
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Katy
3 months ago
I agree with Rory. That option seems to be the most relevant for generating confidence scores for each page.
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Florinda
3 months ago
Option A, definitely. Who wants to manually check thousands of contracts for signatures? ML is the way to go!
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Pete
4 months ago
D seems like a valid option too, but I'm not sure if the GetDocumentAnalysis API is the best fit for this requirement.
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Alaine
2 months ago
D) Use the GetDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page
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Jerrod
3 months ago
C) Use the StartDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page.
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Meghann
3 months ago
A) Use the AnalyzeDocument API action. Set the FeatureTypes parameter to SIGNATURES. Return the confidence scores for each page.
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Sommer
4 months ago
C looks good to me. The StartDocumentAnalysis API action should do the trick.
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Stefania
3 months ago
Yes, automating the signature detection process will definitely save time and improve efficiency.
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Rosio
3 months ago
It's important to get accurate confidence scores for each page of the contracts.
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Maryln
3 months ago
I agree, using StartDocumentAnalysis API action should help detect signatures and provide confidence scores.
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Tawanna
3 months ago
I think C is the best option. StartDocumentAnalysis API action seems like the right choice.
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Hayley
4 months ago
Hmm, I'm not sure. The Prediction API seems a bit overkill for just detecting signatures and confidence scores.
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Bettina
3 months ago
C) Use the StartDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page.
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Agustin
3 months ago
B) I agree, the Prediction API might be too much for just detecting signatures.
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Rory
4 months ago
A) Use the AnalyzeDocument API action. Set the FeatureTypes parameter to SIGNATURES. Return the confidence scores for each page.
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Latrice
4 months ago
I think option A is the way to go. Textract's AnalyzeDocument API seems like the perfect fit for this use case.
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Honey
3 months ago
Agreed, having that level of detail would streamline the process for the law firm.
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Wendell
3 months ago
It would definitely be helpful to have confidence scores for each page of the contracts.
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Junita
3 months ago
Yeah, using the AnalyzeDocument API action with the FeatureTypes parameter set to SIGNATURES makes sense.
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Marshall
4 months ago
I think option A is the way to go. Textract's AnalyzeDocument API seems like the perfect fit for this use case.
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Rory
4 months ago
I think we should use the AnalyzeDocument API action with FeatureTypes set to SIGNATURES.
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