AWS Certified AI Practitioner Practice Questions: Machine Learning Fundamentals Domain
Test your AWS Certified AI Practitioner knowledge with 10 practice questions from the Machine Learning Fundamentals domain. Includes detailed explanations and answers.
AWS Certified AI Practitioner Practice Questions
Master the Machine Learning Fundamentals Domain
Test your knowledge in the Machine Learning Fundamentals 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 logistics company wants to optimize delivery routes using historical data. They need a solution that balances cost and performance. Which AWS service should they primarily consider?
Show Answer & Explanation
Correct Answer: A
Explanation: Amazon SageMaker is ideal for building and training machine learning models to optimize delivery routes, offering a balance between cost and performance. Comprehend is for text analysis, Rekognition is for image/video analysis, and Bedrock is for deploying large language models.
Question 2
A media company wants to automate the tagging of their video content to improve searchability. They need a solution that can process large volumes of video with minimal latency. Which AWS service should they choose?
Show Answer & Explanation
Correct Answer: B
Explanation: Amazon Rekognition is designed for analyzing images and videos, making it ideal for automating video content tagging. It can handle large volumes with minimal latency. Amazon Personalize is for recommendations, Amazon SageMaker is for building custom models, and Amazon Lex is for conversational interfaces.
Question 3
A media company wants to automatically tag and categorize thousands of images uploaded daily. Which AWS service should they use for image analysis and tagging?
Show Answer & Explanation
Correct Answer: A
Explanation: Amazon Rekognition is specifically designed for image and video analysis, including object detection, scene recognition, and automatic tagging. Amazon Comprehend is for text analysis, Amazon SageMaker is for building custom ML models, and Amazon Translate is for language translation.
Question 4
A startup wants to deploy a large language model for generating product descriptions. They are concerned about bias and ethical AI. Which AWS service and feature should they use?
Show Answer & Explanation
Correct Answer: C
Explanation: Amazon Bedrock offers the deployment of large language models with Guardrails to ensure ethical AI and bias mitigation. SageMaker's bias detection is more suitable for custom models, Comprehend is for text analysis, and Rekognition is for image/video analysis.
Question 5
A financial institution needs to develop a machine learning model to detect fraudulent transactions. They are concerned about data privacy and need to ensure that their data is encrypted at rest and in transit. Which AWS service provides built-in support for these security requirements?
Show Answer & Explanation
Correct Answer: A
Explanation: Amazon SageMaker provides built-in support for data encryption at rest and in transit, making it suitable for applications requiring high levels of security and compliance, such as fraud detection in financial institutions. AWS Glue is a data integration service, Amazon Rekognition is for image analysis, and Amazon Comprehend is for text analysis; none of these services are specifically tailored for building machine learning models with encryption requirements.
Question 6
A financial institution needs to build a fraud detection model that can process large volumes of transaction data in real-time. They are concerned about latency and cost. Which AWS service combination should they consider?
Show Answer & Explanation
Correct Answer: B
Explanation: Amazon Kinesis Data Analytics can process streaming data in real-time, which is ideal for fraud detection in financial transactions. Amazon SageMaker can be used to build and deploy the machine learning model. This combination addresses both latency and cost concerns. AWS Lambda is serverless but may not handle high-volume streaming data efficiently. Amazon Comprehend is for text analysis, not suitable for transaction data. Amazon Rekognition is for image/video analysis.
Question 7
A healthcare provider wants to implement a machine learning model to predict patient readmissions. They need to ensure that their solution adheres to ethical AI principles and mitigates bias in predictions. Which AWS service can help them achieve this with built-in tools for bias detection?
Show Answer & Explanation
Correct Answer: A
Explanation: Amazon SageMaker offers tools like SageMaker Clarify to help detect and mitigate bias in machine learning models, making it suitable for applications in healthcare where ethical AI is crucial. Amazon Rekognition is for image and video analysis, Amazon Polly is for text-to-speech, and Amazon Lex is for building conversational interfaces, none of which are designed for building predictive models with bias detection capabilities.
Question 8
An e-commerce company needs to personalize product recommendations for its users based on their browsing history. They require a solution that minimizes latency to ensure a seamless user experience. Which AWS service should they use?
Show Answer & Explanation
Correct Answer: A
Explanation: Amazon Personalize is designed for creating real-time personalized recommendations with low latency, making it ideal for e-commerce platforms. Amazon SageMaker could be used to build a custom recommendation model but would require more development effort. Amazon Forecast is for time-series forecasting, and Amazon Lex is for building conversational interfaces.
Question 9
Your team is tasked with deploying a machine learning model for fraud detection in real-time transactions. The model must be highly scalable and cost-efficient. Which AWS service should you choose for this deployment?
Show Answer & Explanation
Correct Answer: A
Explanation: Amazon SageMaker is ideal for deploying machine learning models at scale with cost efficiency. It provides managed infrastructure and can integrate with other AWS services for real-time predictions. AWS Lambda is serverless but not optimized for ML model deployment, Amazon Elastic Inference is for adding inference acceleration to existing EC2 instances, and Amazon Bedrock is for generative AI applications.
Question 10
A marketing agency wants to analyze the effectiveness of their advertising campaigns by understanding the emotions conveyed in customer feedback. They need a solution that can handle large volumes of text data efficiently. Which AWS service should they use?
Show Answer & Explanation
Correct Answer: A
Explanation: Amazon Comprehend is designed for natural language processing tasks, including sentiment and emotion analysis, making it suitable for analyzing customer feedback. Amazon SageMaker is a general-purpose machine learning service requiring more setup. Amazon Rekognition is for image/video analysis, and AWS Glue is for data integration, neither of which are suitable for text emotion analysis.
Ready to Accelerate Your AWS Certified AI Practitioner Preparation?
Join thousands of professionals who are advancing their careers through expert certification preparation with FlashGenius.
- ✅ Unlimited practice questions across all AWS Certified AI Practitioner domains
- ✅ Full-length exam simulations with real-time scoring
- ✅ AI-powered performance tracking and weak area identification
- ✅ Personalized study plans with adaptive learning
- ✅ Mobile-friendly platform for studying anywhere, anytime
- ✅ Expert explanations and study resources
Already have an account? Sign in here
About AWS Certified AI Practitioner Certification
The AWS Certified AI Practitioner certification validates your expertise in machine learning fundamentals 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.