AWS Certified AI Practitioner Practice Questions: Data Engineering for AI Domain
Test your AWS Certified AI Practitioner knowledge with 10 practice questions from the Data Engineering for AI domain. Includes detailed explanations and answers.
AWS Certified AI Practitioner Practice Questions
Master the Data Engineering for AI Domain
Test your knowledge in the Data Engineering for AI domain with these 10 practice questions. Each question is designed to help you prepare for the AWS Certified AI Practitioner certification exam with detailed explanations to reinforce your learning.
Question 1
A financial institution needs to transcribe customer service calls for compliance purposes. They require a cost-effective solution that integrates with their existing AWS services. Which service should they use?
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Correct Answer: A
Explanation: Amazon Transcribe is specifically designed for converting speech to text, making it ideal for transcribing customer service calls. Amazon Polly is used for text-to-speech, Amazon Lex is for building conversational interfaces, and Amazon Comprehend is for text analysis.
Question 2
Your company wants to build a scalable image recognition system to identify products in user-uploaded images. The system must handle thousands of requests per second and minimize latency. Which AWS services should you consider for this solution?
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Correct Answer: A
Explanation: Amazon Rekognition is ideal for image analysis, and AWS Lambda can process requests in a scalable manner with low latency. Amazon Comprehend is for text analysis, not images. Amazon SageMaker is more suited for custom model training rather than direct image recognition. Amazon Translate is for language translation, and AWS Glue is for ETL tasks.
Question 3
A financial services company needs to process and analyze large volumes of customer feedback from various sources to improve their services. They require a solution that can handle text data and provide sentiment analysis. Due to regulatory requirements, data must be encrypted at rest. Which AWS service should they choose, considering their requirements?
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Correct Answer: B
Explanation: Amazon Comprehend is a natural language processing service that can perform sentiment analysis on text data. It supports encryption at rest using AWS KMS, which meets the company's regulatory requirements. Option A is incorrect because SageMaker is primarily for building, training, and deploying machine learning models, not specifically for sentiment analysis. Option C is incorrect because Rekognition is for image and video analysis, not text. Option D is incorrect because Bedrock is for generative AI applications, not specifically for sentiment analysis.
Question 4
A retail company wants to implement a real-time recommendation system to enhance their customer experience on their e-commerce platform. They need a solution that can handle high traffic with low latency. Which AWS service should they primarily consider for this requirement?
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Correct Answer: B
Explanation: Amazon Personalize is specifically designed for building real-time recommendation systems with low latency, making it ideal for e-commerce platforms. Amazon SageMaker could also be used, but it would require more custom development. AWS Lambda is not designed for recommendation systems, and Amazon Rekognition is for image and video analysis.
Question 5
An organization is planning to automate the content moderation of images uploaded by users to their social media platform. They are concerned about cost and want to ensure that the service scales automatically. Which AWS service should they use?
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Correct Answer: A
Explanation: Amazon Rekognition provides image and video analysis capabilities, including content moderation. It automatically scales with the workload, which addresses the organization's concern about scalability and cost. AWS Batch is for batch processing jobs, Amazon SageMaker is for building and training models, and Amazon Elastic Transcoder is for media transcoding.
Question 6
A retail company wants to implement a real-time recommendation system for its online store. They are considering using either Amazon Personalize or Amazon SageMaker to build this solution. The company has a limited budget and needs to minimize latency to enhance user experience. Which AWS service should they choose and why?
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Correct Answer: A
Explanation: Amazon Personalize is specifically designed for building recommendation systems and comes with pre-built algorithms, which reduces both deployment time and costs compared to building a custom model with Amazon SageMaker. Amazon Comprehend and Amazon Rekognition are not suitable for this use case as they are designed for text analysis and image recognition, respectively.
Question 7
Your organization is developing a chatbot to handle customer inquiries. You need a service that supports multiple languages and can be easily integrated into your existing AWS infrastructure. Which service would be the most appropriate choice?
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Correct Answer: A
Explanation: Amazon Lex is specifically designed for building conversational interfaces and can be integrated with other AWS services. It supports multiple languages and is well-suited for developing chatbots. Amazon Translate is for language translation, Amazon Polly is for converting text to speech, and Amazon SageMaker is for building and training custom machine learning models.
Question 8
A healthcare provider needs to ensure that their machine learning models comply with strict data privacy regulations. Which AWS service feature can help achieve this by encrypting data both at rest and in transit?
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Correct Answer: A
Explanation: AWS Key Management Service (KMS) provides the capability to encrypt data both at rest and in transit, which is essential for compliance with data privacy regulations. Amazon SageMaker and AWS Glue do not inherently provide encryption capabilities, and Amazon Rekognition is not related to data encryption. AWS Shield is used for DDoS protection.
Question 9
An e-commerce company wants to automate the extraction of product information from invoices. They are concerned about processing costs and want to minimize them while ensuring accuracy. Which AWS service should they choose?
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Correct Answer: A
Explanation: Amazon Textract is designed to automatically extract text, forms, and tables from scanned documents, like invoices, with high accuracy. It is cost-effective for this specific task. Amazon Rekognition is used for image and video analysis, Amazon Comprehend is for text analysis, and Amazon SageMaker is for building custom ML models, which would be more expensive and complex for this use case.
Question 10
An e-commerce platform wants to provide personalized product recommendations to its users. They are looking for a managed service that can handle this use case efficiently. Which AWS service should they use?
Show Answer & Explanation
Correct Answer: A
Explanation: Amazon Personalize is a machine learning service that allows developers to create individualized recommendations for customers. Amazon Lex is for building conversational interfaces, Amazon Rekognition is for image and video analysis, and Amazon Kendra is for search capabilities, none of which are suitable for personalized recommendations.
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About AWS Certified AI Practitioner Certification
The AWS Certified AI Practitioner certification validates your expertise in data engineering for ai and other critical domains. Our comprehensive practice questions are carefully crafted to mirror the actual exam experience and help you identify knowledge gaps before test day.