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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q170-Q175):

NEW QUESTION # 170
A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Choose three.)

  • A. Increase regularization.
  • B. Decrease regularization.
  • C. Decrease dropout.
  • D. Decrease feature combinations.
  • E. Increase feature combinations.
  • F. Increase dropout.

Answer: A,C,E


NEW QUESTION # 171
An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen.
Which combination of algorithms would provide the appropriate insights? (Select TWO.)

  • A. The Latent Dirichlet Allocation (LDA) algorithm
  • B. The principal component analysis (PCA) algorithm
  • C. The k-means algorithm
  • D. The Random Cut Forest (RCF) algorithm
  • E. The factorization machines (FM) algorithm

Answer: B,C

Explanation:
The PCA and K-means algorithms are useful in collection of data using census form.


NEW QUESTION # 172
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?

  • A. Use the GetDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page
  • B. Use the Prediction API call on the documents. Return the signatures and confidence scores for each page.
  • C. Use the StartDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page.
  • D. Use the AnalyzeDocument API action. Set the FeatureTypes parameter to SIGNATURES. Return the confidence scores for each page.

Answer: D

Explanation:
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


NEW QUESTION # 173
A data engineer at a bank is evaluating a new tabular dataset that includes customer dat a. The data engineer will use the customer data to create a new model to predict customer behavior. After creating a correlation matrix for the variables, the data engineer notices that many of the 100 features are highly correlated with each other.
Which steps should the data engineer take to address this issue? (Choose two.)

  • A. Apply one-hot encoding category-based variables.
  • B. Apply principal component analysis (PCA).
  • C. Remove a portion of highly correlated features from the dataset.
  • D. Apply min-max feature scaling to the dataset.
  • E. Use a linear-based algorithm to train the model.

Answer: B,C

Explanation:
B) Apply principal component analysis (PCA): PCA is a technique that reduces the dimensionality of a dataset by transforming the original features into a smaller set of new features that capture most of the variance in the data. PCA can help address the issue of multicollinearity, which occurs when some features are highly correlated with each other and can cause problems for some machine learning algorithms. By applying PCA, the data engineer can reduce the number of features and remove the redundancy in the data.
C) Remove a portion of highly correlated features from the dataset: Another way to deal with multicollinearity is to manually remove some of the features that are highly correlated with each other. This can help simplify the model and avoid overfitting. The data engineer can use the correlation matrix to identify the features that have a high correlation coefficient (e.g., above 0.8 or below -0.8) and remove one of them from the dataset. References: = Principal Component Analysis: This is a document from AWS that explains what PCA is, how it works, and how to use it with Amazon SageMaker.
Multicollinearity: This is a document from AWS that describes what multicollinearity is, how to detect it, and how to deal with it.


NEW QUESTION # 174
A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively.
How should the Specialist address this issue and what is the reason behind it?

  • A. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
  • B. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
  • C. The learning rate should be increased because the optimization process was trapped at a local minimum.
  • D. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.

Answer: C


NEW QUESTION # 175
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