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Amazon AWS Certified AI Practitioner Sample Questions:

1. A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention. The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that adhere to company tone.
Which solution meets these requirements?

A) Define a higher number for the temperature parameter.
B) Experiment and refine the prompt until the FM produces the desired responses.
C) Use batch inferencing to process detailed responses.
D) Set a low limit on the number of tokens the FM can produce.

2. 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) Summarize the response text depending on the age of the user so that younger users receive shorter responses.
C) Fine-tune the model by using additional training data that is representative of the various age ranges that the application will support.
D) Add a role description to the prompt context that instructs the model of the age range that the response should target.

3. A company needs to choose a model from Amazon Bedrock to use internally.
The company must identify a model that generates responses in a style that the company's employees prefer.
What should the company do to meet these requirements?

A) Evaluate the models by using built-in prompt datasets.
B) Evaluate the models by using a human workforce and custom prompt datasets.
C) Use the model InvocationLatency runtime metrics in Amazon CloudWatch when trying models.
D) Use public model leaderboards to identify the model.

4. A research company implemented a chatbot by using a foundation model (FM) from Amazon Bedrock. The chatbot searches for answers to questions from a large database of research papers.
After multiple prompt engineering attempts, the company notices that the FM is performing poorly because of the complex scientific terms in the research papers.
How can the company improve the performance of the chatbot?

A) Use few-shot prompting to define how the FM can answer the questions.
B) Change the FM inference parameters.
C) Clean the research paper data to remove complex scientific terms.
D) Use domain adaptation fine-tuning to adapt the FM to complex scientific terms.

5. An AI practitioner is building a model to generate images of humans in various professions. The AI practitioner discovered that the input data is biased and that specific attributes affect the image generation and create bias in the model.
Which technique will solve the problem?

A) Model monitoring for class distribution
B) Retrieval Augmented Generation (RAG)
C) Watermark detection for images
D) Data augmentation for imbalanced classes

Solutions: