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Amazon AWS Certified AI Practitioner Sample Questions (Q83-Q88):

NEW QUESTION # 83
A company has terabytes of data in a database that the company can use for business analysis. The company wants to build an AI-based application that can build a SQL query from input text that employees provide. The employees have minimal experience with technology.
Which solution meets these requirements?

  • A. Support vector machine
  • B. Generative pre-trained transformers (GPT)
  • C. WaveNet
  • D. Residual neural network

Answer: B


NEW QUESTION # 84
A company manually reviews all submitted resumes in PDF format. As the company grows, the company expects the volume of resumes to exceed the company's review capacity. The company needs an automated system to convert the PDF resumes into plain text format for additional processing.
Which AWS service meets this requirement?

  • A. Amazon Textract
  • B. Amazon Personalize
  • C. Amazon Transcribe
  • D. Amazon Lex

Answer: A

Explanation:
Amazon Textract is a service that automatically extracts text and data from scanned documents, including PDFs. It is the best choice for converting resumes from PDF format to plain text for further processing.
* Amazon Textract:
* Extracts text, forms, and tables from scanned documents accurately.
* Ideal for automating the process of converting PDF resumes into plain text format.
* Why Option A is Correct:
* Automation of Text Extraction: Textract is designed to handle large volumes of documents and convert them into machine-readable text, perfect for the company's need.
* Scalability and Efficiency: Supports scalability to handle a growing volume of resumes as the company expands.
* Why Other Options are Incorrect:
* B. Amazon Personalize: Used for creating personalized recommendations, not for text extraction.
* C. Amazon Lex: A service for building conversational interfaces, not for processing documents.
* D. Amazon Transcribe: Used for converting speech to text, not for extracting text from documents.


NEW QUESTION # 85
A company is developing an ML model to predict customer churn.
Which evaluation metric will assess the model's performance on a binary classification task such as predicting chum?

  • A. Mean squared error (MSE)
  • B. R-squared
  • C. Time used to train the model
  • D. F1 score

Answer: D

Explanation:
The company is developing an ML model to predict customer churn, a binary classification task (churn or no churn). The F1 score is an evaluation metric that balances precision and recall, making it suitable for assessing the performance of binary classification models, especially when dealing with imbalanced datasets, which is common in churn prediction.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"The F1 score is a metric for evaluating binary classification models, combining precision and recall into a single value. It is particularly useful for tasks like churn prediction, where class imbalance may exist, ensuring the model performs well on both positive and negative classes." (Source: Amazon SageMaker Developer Guide, Model Evaluation Metrics) Detailed Explanation:
* Option A: F1 scoreThis is the correct answer. The F1 score is ideal for binary classification tasks like churn prediction, as it measures the model's ability to correctly identify both churners and non-churners.
* Option B: Mean squared error (MSE)MSE is used for regression tasks to measure the average squared difference between predicted and actual values, not for binary classification.
* Option C: R-squaredR-squared is a metric for regression models, indicating how well the model explains the variability of the target variable. It is not applicable to classification tasks.
* Option D: Time used to train the modelTraining time is not an evaluation metric for model performance; it measures the duration of training, not the model's accuracy or effectiveness.
References:
Amazon SageMaker Developer Guide: Model Evaluation Metrics (https://docs.aws.amazon.com/sagemaker
/latest/dg/model-evaluation.html)
AWS AI Practitioner Learning Path: Module on Model Performance and Evaluation AWS Documentation: Metrics for Classification (https://aws.amazon.com/machine-learning/)


NEW QUESTION # 86
Which scenario represents a practical use case for generative AI?

  • A. Employing a chatbot to provide human-like responses to customer queries in real time
  • B. Implementing a rule-based recommendation engine to suggest products to customers
  • C. Using an analytics dashboard to track website traffic and user behavior
  • D. Using an ML model to forecast product demand

Answer: A

Explanation:
Generative AI is a type of AI that creates new content, such as text, images, or audio, often mimicking human- like outputs. A practical use case for generative AI is employing a chatbot to provide human-like responses to customer queries in real time, as it leverages the ability of large language models (LLMs) to generate natural language responses dynamically.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Generative AI enables applications like chatbots to produce human-like text responses in real time, enhancing customer support by providing natural and contextually relevant answers to user queries." (Source: AWS Bedrock User Guide, Introduction to Generative AI) Detailed Explanation:
* Option A: Using an ML model to forecast product demandForecasting product demand typically involves predictive analytics using supervised learning (e.g., regression models), not generative AI, which focuses on creating new content.
* Option B: Employing a chatbot to provide human-like responses to customer queries in real time This is the correct answer. Generative AI, particularly LLMs, is commonly used to power chatbots that generate human-like responses, making this a practical use case.
* Option C: Using an analytics dashboard to track website traffic and user behaviorAn analytics dashboard involves data visualization and analysis, not generative AI, which is about creating new content.
* Option D: Implementing a rule-based recommendation engine to suggest products to customersA rule-based recommendation engine relies on predefined rules, not generative AI. Generative AI could be used for more dynamic recommendations, but this scenario does not describe such a case.
References:
AWS Bedrock User Guide: Introduction to Generative AI (https://docs.aws.amazon.com/bedrock/latest
/userguide/what-is-bedrock.html)
AWS AI Practitioner Learning Path: Module on Generative AI Applications AWS Documentation: Generative AI Use Cases (https://aws.amazon.com/generative-ai/)


NEW QUESTION # 87
A company wants to create an application by using Amazon Bedrock. The company has a limited budget and prefers flexibility without long-term commitment.
Which Amazon Bedrock pricing model meets these requirements?

  • A. On-Demand
  • B. Model customization
  • C. Provisioned Throughput
  • D. Spot Instance

Answer: A

Explanation:
Amazon Bedrock offers an on-demand pricing model that provides flexibility without long-term commitments. This model allows companies to pay only for the resources they use, which is ideal for a limited budget and offers flexibility.
* Option A (Correct): "On-Demand": This is the correct answer because on-demand pricing allows the company to use Amazon Bedrock without any long-term commitments and to manage costs according to their budget.
* Option B: "Model customization" is a feature, not a pricing model.
* Option C: "Provisioned Throughput" involves reserving capacity ahead of time, which might not offer the desired flexibility and could lead to higher costs if the capacity is not fully used.
* Option D: "Spot Instance" is a pricing model for EC2 instances and does not apply to Amazon Bedrock.
AWS AI Practitioner References:
* AWS Pricing Models for Flexibility: On-demand pricing is a key AWS model for services that require flexibility and no long-term commitment, ensuring cost-effectiveness for projects with variable usage patterns.


NEW QUESTION # 88
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