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AWS Certified AI Practitioner Practice Questions: Model Deployment and Operations Domain

Test your AWS Certified AI Practitioner knowledge with 10 practice questions from the Model Deployment and Operations domain. Includes detailed explanations and answers.

AWS Certified AI Practitioner Practice Questions

Master the Model Deployment and Operations Domain

Test your knowledge in the Model Deployment and Operations 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

An e-commerce company wants to deploy a recommendation engine using a pre-trained model. They need a service that offers pre-built models and can be integrated with their existing applications. Which AWS service is most appropriate?

A) Amazon Personalize

B) Amazon SageMaker

C) AWS DeepLens

D) Amazon Lex

Show Answer & Explanation

Correct Answer: A

Explanation: Amazon Personalize provides pre-built models for creating personalized recommendations and can be easily integrated with existing applications. SageMaker is more for custom model training and deployment, DeepLens is for deep learning on edge devices, and Lex is for building conversational interfaces.

Question 2

A media company wants to deploy a sentiment analysis model for social media monitoring. They have data privacy concerns. Which AWS service should they consider for deployment?

A) Amazon Comprehend with AWS KMS Encryption

B) Amazon SageMaker

C) Amazon Rekognition

D) Amazon Bedrock

Show Answer & Explanation

Correct Answer: A

Explanation: Amazon Comprehend with AWS KMS Encryption provides sentiment analysis capabilities while ensuring data privacy through encryption. SageMaker is more suitable for custom models, Rekognition is for image analysis, and Bedrock does not focus on sentiment analysis.

Question 3

You are deploying a machine learning model using Amazon SageMaker and need to ensure that the model's predictions are explainable to comply with regulatory requirements. Which feature should you use?

A) SageMaker Clarify

B) SageMaker Neo

C) SageMaker Autopilot

D) SageMaker Ground Truth

Show Answer & Explanation

Correct Answer: A

Explanation: SageMaker Clarify provides tools for detecting bias and explaining model predictions, making it suitable for regulatory compliance. SageMaker Neo is for optimizing models for different hardware platforms, SageMaker Autopilot is for automating model creation, and SageMaker Ground Truth is for data labeling.

Question 4

A media company needs to analyze large volumes of video content to identify specific objects and scenes. Which AWS service should they use to deploy a solution that can automatically scale based on the load?

A) Amazon Rekognition Video

B) Amazon SageMaker

C) AWS Lambda

D) Amazon Polly

Show Answer & Explanation

Correct Answer: A

Explanation: Amazon Rekognition Video is designed for analyzing video content, including object and scene detection, and can automatically scale based on the load. SageMaker is for general ML model deployment, Lambda is for running code, and Polly is for text-to-speech conversion.

Question 5

A retail company wants to deploy a machine learning model to predict daily sales using historical data. They need a fully managed service that can handle data preprocessing, training, and deployment with minimal setup. The company is concerned about cost and wants to ensure they only pay for the resources they use. Which AWS service should they use?

A) Amazon SageMaker

B) AWS Lambda

C) Amazon Comprehend

D) Amazon Bedrock

Show Answer & Explanation

Correct Answer: A

Explanation: Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly. It offers a pay-as-you-go pricing model, which aligns with the company's cost concerns. AWS Lambda is not suitable for model training and deployment. Amazon Comprehend is for natural language processing, not sales prediction. Amazon Bedrock is more focused on generative AI rather than traditional ML model deployment.

Question 6

Your team is deploying a machine learning model with Amazon SageMaker and wants to ensure cost efficiency. Which instance type should you consider for inference to balance performance and cost?

A) Inf1 instances

B) P3 instances

C) M5 instances

D) C5 instances

Show Answer & Explanation

Correct Answer: A

Explanation: Inf1 instances are designed for high-performance inference at a low cost, making them ideal for cost-efficient deployments. P3 instances are more expensive and suited for training. M5 and C5 instances are general-purpose and compute-optimized, respectively, but do not offer the same inference cost benefits as Inf1.

Question 7

Your organization needs to deploy a sentiment analysis model that processes customer feedback in real-time. Due to compliance requirements, all data must remain within your VPC. Which AWS service should you use to ensure compliance while deploying the model?

A) Amazon SageMaker Endpoints with VPC support

B) Amazon Comprehend

C) Amazon Bedrock

D) Amazon Rekognition

Show Answer & Explanation

Correct Answer: A

Explanation: Amazon SageMaker Endpoints with VPC support allows you to deploy models within your VPC, ensuring compliance with data residency requirements. Amazon Comprehend is a managed service that doesn't support VPC deployment directly, Bedrock is for foundation models, and Rekognition is for image and video analysis.

Question 8

An e-commerce platform wants to enhance its product recommendation system using machine learning. They are concerned about bias in their model and want to ensure responsible AI practices. Which AWS service and feature should they use to address these concerns?

A) Amazon SageMaker with Clarify for bias detection

B) Amazon Lex with sentiment analysis

C) Amazon Rekognition with facial analysis

D) Amazon Comprehend with entity recognition

Show Answer & Explanation

Correct Answer: A

Explanation: Amazon SageMaker Clarify helps detect bias in machine learning models and data, ensuring responsible AI practices, which is crucial for a product recommendation system. Amazon Lex, Rekognition, and Comprehend do not provide bias detection capabilities and are not directly applicable to product recommendation systems.

Question 9

A healthcare organization needs to deploy a machine learning model that complies with strict data privacy regulations, including data encryption at rest and in transit. Which AWS services and features should be considered to ensure compliance?

A) Amazon SageMaker with AWS KMS and VPC endpoints

B) Amazon Comprehend with default settings

C) Amazon Rekognition without encryption

D) AWS Glue with public data access

Show Answer & Explanation

Correct Answer: A

Explanation: Amazon SageMaker can be configured to use AWS Key Management Service (KMS) for encrypting data at rest and VPC endpoints for secure data transfer, meeting strict compliance requirements. Amazon Comprehend and Amazon Rekognition without encryption do not meet the necessary compliance standards. AWS Glue with public data access would not provide the required data protection.

Question 10

A global enterprise needs to deploy a multilingual chatbot that adheres to regional compliance. Which AWS service combination should they consider for deployment?

A) Amazon Lex with Amazon Translate

B) Amazon Polly with Amazon Comprehend

C) Amazon Translate with AWS Lambda

D) Amazon SageMaker with Amazon Lex

Show Answer & Explanation

Correct Answer: A

Explanation: Amazon Lex can be used to build chatbots, and Amazon Translate can handle multilingual capabilities. This combination allows for regional deployment and compliance. Amazon Polly is for text-to-speech, not chatbots.

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About AWS Certified AI Practitioner Certification

The AWS Certified AI Practitioner certification validates your expertise in model deployment and operations 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.