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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 Machine Learning Specialist is deciding between building a naive Bayesian model or a full Bayesian network for a classification problem. The Specialist computes the Pearson correlation coefficients between each feature and finds that their absolute values range between 0.1 to 0.95.

Which model describes the underlying data in this situation?

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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:

Ilona
18 days ago
A Bayesian network, huh? Sounds like a great way to get caught in a web of probability distributions. Maybe we should just flip a coin and call it a day.
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Daniela
21 days ago
Let's not get too Bayes-ic here. I'd go with the naïve approach, unless you want to get tangled up in all those conditional probabilities.
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Lera
1 months ago
Hmm, I'm not sure. If the features are all over the place, from 0.1 to 0.95 correlation, that sounds like a job for a full Bayesian network to me.
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Claudia
1 days ago
B) A full Bayesian network, since some of the features are statistically dependent.
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Jeannine
21 days ago
A) A naive Bayesian model, since the features are all conditionally independent.
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Magda
1 months ago
Hold up, if the features are conditionally independent, wouldn't a full Bayesian network be overkill? Just keep it simple with a naive Bayes.
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Krissy
1 months ago
A) A naive Bayesian model, since the features are all conditionally independent.
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Cecily
2 months ago
A naive Bayesian model would be the better choice here since the features have varying degrees of correlation, indicating some statistical dependence.
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Samuel
2 months ago
But the correlation coefficients are not very high, so maybe the features are not strongly dependent.
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Serina
2 months ago
I disagree, I believe the answer is D) A full Bayesian network.
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Samuel
2 months ago
I think the answer is A) A naive Bayesian model.
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