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Amazon AWS Certified AI Practitioner Sample Questions:

1. A company is building a customer service chatbot. The company wants the chatbot to improve its responses by learning from past interactions and online resources.
Which AI learning strategy provides this self-improvement capability?

A) Reinforcement learning with rewards for positive customer feedback
B) Supervised learning with a continuously updated FAQ database
C) Unsupervised learning to find clusters of similar customer inquiries
D) Supervised learning with a manually curated dataset of good responses and bad responses

2. A company has documents that are missing some words because of a database error. The company wants to build an ML model that can suggest potential words to fill in the missing text.
Which type of model meets this requirement?

A) Prescriptive ML models
B) Clustering models
C) BERT-based models
D) Topic modeling

3. A company has a foundation model (FM) that was customized by using Amazon Bedrock to answer customer queries about products. The company wants to validate the model's responses to new types of queries. The company needs to upload a new dataset that Amazon Bedrock can use for validation.
Which AWS service meets these requirements?

A) Amazon Elastic File System (Amazon EFS)
B) AWS Snowcone
C) Amazon S3
D) Amazon Elastic Block Store (Amazon EBS)

4. 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) Neural networks
C) Linear regression
D) Logistic regression

5. A company is building an application that needs to generate synthetic data that is based on existing data.
Which type of model can the company use to meet this requirement?

A) XGBoost
B) Generative adversarial network (GAN)
C) Residual neural network
D) WaveNet

Solutions: