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Amazon MLS-C01 Exam - 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?

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:

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Kate
3 months ago
Full Bayesian networks are more flexible with dependencies, so D makes sense.
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Cruz
3 months ago
I thought naive Bayes was the go-to for this kind of data!
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Daren
3 months ago
Wait, aren't naive Bayes models for independent features?
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Evangelina
4 months ago
Definitely leaning towards option D here.
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Mollie
4 months ago
The absolute values of correlation coefficients suggest some dependencies.
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Daniel
4 months ago
I recall that a naive Bayesian model is typically used when features are independent, but with some dependencies here, I might lean towards the full Bayesian network.
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Pamella
4 months ago
I’m a bit confused. If the features have high correlation, does that mean they can’t be independent? I feel like I need to review this more.
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Kimberely
4 months ago
I think I practiced a similar question where the dependencies mattered, so maybe a full Bayesian network is the way to go since some features are dependent.
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Pura
5 months ago
I remember that naive Bayes assumes features are independent, but with those correlation values, I'm not sure if that's the case here.
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Shayne
5 months ago
I'm a bit confused on this one. The wording about "conditionally independent" features is throwing me off. I'll need to revisit the differences between naive Bayesian and full Bayesian network models to make the right call.
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Louann
5 months ago
Okay, I've got this. Since the features are not completely independent, a full Bayesian network is the better choice here. The conditional independence assumption for the naive Bayesian model is not met.
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Cassi
5 months ago
Hmm, the question mentions that the features are "conditionally independent", which makes me think a naive Bayesian model might be the way to go. But the range of correlation coefficients is concerning. I'll need to review the assumptions for each model to decide.
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Karol
5 months ago
This is a tricky one. The Pearson correlation coefficients range from 0.1 to 0.95, which suggests that the features are not completely independent. I'm leaning towards a full Bayesian network, but I'll need to think it through carefully.
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Tora
5 months ago
This question is testing our understanding of the Inventory Management feature. I'll need to think through the options logically.
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Domonique
5 months ago
I'm leaning towards the Varying Attribute Dimension as well. That seems like the best way to capture the month-to-month changes in customer status. The other options don't seem as well-suited for this use case.
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Ilona
9 months 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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Brittni
8 months ago
It's important to choose the model that best fits the underlying data to ensure accurate predictions.
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Mabel
8 months ago
Let's consider the complexity of the relationships in the data before making a decision.
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Lorenza
8 months ago
Full Bayesian network would be more suitable for complex relationships with higher correlation coefficients.
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Lourdes
9 months ago
Naive Bayesian model would be simpler to implement with lower correlation coefficients.
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Daniela
9 months 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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Paulina
8 months ago
D) A full Bayesian network, since some of the features are statistically dependent.
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Lenna
8 months ago
I agree, keeping it simple with the naive approach makes sense.
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Delisa
9 months ago
A) A naive Bayesian model, since the features are all conditionally independent.
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Lera
10 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
9 months ago
B) A full Bayesian network, since some of the features are statistically dependent.
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Jeannine
9 months ago
A) A naive Bayesian model, since the features are all conditionally independent.
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Magda
10 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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Reed
8 months ago
C) A naive Bayesian model, since some of the features are statistically dependent.
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Ryan
9 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
10 months ago
A) A naive Bayesian model, since the features are all conditionally independent.
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Cecily
10 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
11 months ago
But the correlation coefficients are not very high, so maybe the features are not strongly dependent.
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Serina
11 months ago
I disagree, I believe the answer is D) A full Bayesian network.
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Samuel
11 months ago
I think the answer is A) A naive Bayesian model.
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