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Amazon AWS Certified AI Practitioner Sample Questions:

1. Which AWS service or feature can help an AI development team quickly deploy and consume a foundation model (FM) within the team's VPC?

A) PartyRock, an Amazon Bedrock Playground
B) Amazon SageMaker endpoints
C) Amazon SageMaker JumpStart
D) Amazon Personalize

2. A medical company is customizing a foundation model (FM) for diagnostic purposes. The company needs the model to be transparent and explainable to meet regulatory requirements.
Which solution will meet these requirements?

A) Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.
B) Encrypt and secure training data by using Amazon Macie.
C) Gather more data. Use Amazon Rekognition to add custom labels to the data.
D) Configure the security and compliance by using Amazon Inspector.

3. A company wants to classify human genes into 20 categories based on gene characteristics.
The company needs an ML algorithm to document how the inner mechanism of the model affects the output.
Which ML algorithm meets these requirements?

A) Decision trees
B) Linear regression
C) Logistic regression
D) Neural networks

4. A company needs to build its own large language model (LLM) based on only the company's private data.
The company is concerned about the environmental effect of the training process.
Which Amazon EC2 instance type has the LEAST environmental effect when training LLMs?

A) Amazon EC2 G series
B) Amazon EC2 P series
C) Amazon EC2 Trn series
D) Amazon EC2 C series

5. An education provider is building a question and answer application that uses a generative AI model to explain complex concepts. The education provider wants to automatically change the style of the model response depending on who is asking the question. The education provider will give the model the age range of the user who has asked the question.
Which solution meets these requirements with the LEAST implementation effort?

A) Use chain-of-thought reasoning to deduce the correct style and complexity for a response suitable for that user.
B) Add a role description to the prompt context that instructs the model of the age range that the response should target.
C) Fine-tune the model by using additional training data that is representative of the various age ranges that the application will support.
D) Summarize the response text depending on the age of the user so that younger users receive shorter responses.

Solutions: