1. A team is planning to use Amazon Comprehend for sentiment analysis on their customer feedback data stored in Amazon S3. They are interested in extracting specific topics and entities from the text for further analysis. Which of the following statements is true regarding using Amazon Comprehend for this use case?

A) Amazon Comprehend requires that the customer feedback data is first pre-processed and cleaned before being analyzed for sentiment and other attributes.
B) Amazon Comprehend provides a one-size-fits-all pre-built model for sentiment analysis, topic extraction, and entity recognition for all languages.
C) Amazon Comprehend supports topic and entity extraction and provides pre-built models for various languages including English, Spanish, German, French, Italian, and Portuguese.
D) Amazon Comprehend can only extract sentiment analysis and does not support topic or entity extraction.



2. You have been tasked with deploying a machine learning application that requires running a Docker container image in a serverless environment on AWS Fargate. The application needs to process large volumes of data and requires high network throughput. Which of the following options is the most appropriate for optimizing network performance in this scenario?

A) Use AWS PrivateLink to securely access the data source over a private network connection.
B) Increase the CPU and memory allocation of the Fargate task to increase network performance.
C) Use a smaller Fargate task size to limit network traffic and reduce latency.
D) Use AWS Direct Connect to establish a dedicated network connection between the Fargate task and the data source.



3. Which of the following statements about Amazon Polly is true?

A) Amazon Polly is designed for image and video analysis.
B) Amazon Polly offers both standard and neural text-to-speech (TTS) voices.
C) Amazon Polly requires the user to provide pre-recorded speech to generate synthesized speech.
D) Amazon Polly only supports English language voices.
E) Amazon Polly offers speech recognition and natural language understanding (NLU) services.


4. Which of the following is a benefit of using Amazon Textract for document processing in comparison to traditional OCR methods?

A) Amazon Textract does not support handwriting recognition, while traditional OCR methods can recognize both printed and handwritten text.
B) Amazon Textract can identify and extract structured data such as tables and forms, while traditional OCR methods only extract text.
C) Amazon Textract is limited to processing a maximum of 10,000 pages per month, while traditional OCR methods can handle unlimited volumes.
D) Amazon Textract requires minimal preprocessing of documents, while traditional OCR methods require extensive document preparation.



5. You are building a fraud detection system using Amazon Fraud Detector. Your dataset contains information on credit card transactions, including the transaction amount, the merchant category code, and the card issuer. You have trained a machine learning model and are now ready to deploy it as a detector.Which of the following is a valid rule language that can be used to define your fraud detection rules in Amazon Fraud Detector?

A) Python
B) JavaScript
C) Ruby
D) JSON
E) SQL