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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q180-Q185):
NEW QUESTION # 180
A data scientist has developed a machine learning translation model for English to Japanese by using Amazon SageMaker's built-in seq2seq algorithm with 500,000 aligned sentence pairs. While testing with sample sentences, the data scientist finds that the translation quality is reasonable for an example as short as five words. However, the quality becomes unacceptable if the sentence is 100 words long.
Which action will resolve the problem?
- A. Adjust hyperparameters related to the attention mechanism.
- B. Choose a different weight initialization type.
- C. Add more nodes to the recurrent neural network (RNN) than the largest sentence's word count.
- D. Change preprocessing to use n-grams.
Answer: A
Explanation:
Explanation
The data scientist should adjust hyperparameters related to the attention mechanism to resolve the problem.
The attention mechanism is a technique that allows the decoder to focus on different parts of the input sequence when generating the output sequence. It helps the model cope with long input sequences and improve the translation quality. The Amazon SageMaker seq2seq algorithm supports different types of attention mechanisms, such as dot, general, concat, and mlp. The data scientist can use the hyperparameter attention_type to choose the type of attention mechanism. The data scientist can also use the hyperparameter attention_coverage_type to enable coverage, which is a mechanism that penalizes the model for attending to the same input positions repeatedly. By adjusting these hyperparameters, the data scientist can fine-tune the attention mechanism and reduce the number of false negative predictions by the model.
References:
Sequence-to-Sequence Algorithm - Amazon SageMaker
Attention Mechanism - Sockeye Documentation
NEW QUESTION # 181
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 Prediction API call on the documents. Return the signatures and confidence scores for each page.
- B. Use the GetDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page
- C. Use the AnalyzeDocument API action. Set the FeatureTypes parameter to SIGNATURES. Return the confidence scores for each page.
- D. Use the StartDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page.
Answer: C
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 # 182
A term frequency-inverse document frequency (tf-idf) matrix using both unigrams and bigrams is built from a text corpus consisting of the following two sentences:
1. Please call the number below.
2. Please do not call us.
What are the dimensions of the tf-idf matrix?
- A. (2, 10)
- B. (2, 16)
- C. (8, 10)
- D. (2, 8)
Answer: B
Explanation:
There are 2 sentences, 8 unique unigrams, and 8 unique bigrams, so the result would be (2,16).
The phrases are "Please call the number below" and "Please do not call us." Each word individually (unigram) is "Please," "call," "the," "number," "below," "do," "not," and "us." The unique bigrams are "Please call," "call the," "the number," "number below," "Please do," "do not," "not call," and "call us."
NEW QUESTION # 183
A company is running an Amazon SageMaker training job that will access data stored in its Amazon S3 bucket A compliance policy requires that the data never be transmitted across the internet How should the company set up the job?
- A. Launch the notebook instances in a public subnet and access the data through the public S3 endpoint
- B. Launch the notebook instances in a public subnet and access the data through a NAT gateway
- C. Launch the notebook instances in a private subnet and access the data through a NAT gateway
- D. Launch the notebook instances in a private subnet and access the data through an S3 VPC endpoint.
Answer: D
Explanation:
A private subnet is a subnet that does not have a route to the internet gateway, which means that the resources in the private subnet cannot access the internet or be accessed from the internet. An S3 VPC endpoint is a gateway endpoint that allows the resources in the VPC to access the S3 service without going through the internet. By launching the notebook instances in a private subnet and accessing the data through an S3 VPC endpoint, the company can set up the job in a secure and compliant way, as the data never leaves the AWS network and is not exposed to the internet. This can also improve the performance and reliability of the data transfer, as the traffic does not depend on the internet bandwidth or availability.
References:
Amazon VPC Endpoints - Amazon Virtual Private Cloud
Endpoints for Amazon S3 - Amazon Virtual Private Cloud
Connect to SageMaker Within your VPC - Amazon SageMaker
Working with VPCs and Subnets - Amazon Virtual Private Cloud
NEW QUESTION # 184
For the given confusion matrix, what is the recall and precision of the model?
- A. Recall = 0.92 Precision = 0.8
- B. Recall = 0.92 Precision = 0.84
- C. Recall = 0.8 Precision = 0.92
- D. Recall = 0.84 Precision = 0.8
Answer: A
NEW QUESTION # 185
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