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Amazon AWS Certified AI Practitioner Sample Questions:

1. Which option is a use case for generative AI models?

A) Analyzing financial data to forecast stock market trends
B) Improving network security by using intrusion detection systems
C) Creating photorealistic images from text descriptions for digital marketing
D) Enhancing database performance by using optimized indexing

2. 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) Linear regression
B) Logistic regression
C) Decision trees
D) Neural networks

3. A company is building an ML model. The company collected new data and analyzed the data by creating a correlation matrix, calculating statistics, and visualizing the data.
Which stage of the ML pipeline is the company currently in?

A) Feature engineering
B) Exploratory data analysis
C) Hyperparameter tuning
D) Data pre-processing

4. A company is using Amazon SageMaker Studio notebooks to build and train ML models. The company stores the data in an Amazon S3 bucket. The company needs to manage the flow of data from Amazon S3 to SageMaker Studio notebooks.
Which solution will meet this requirement?

A) Configure SageMaker to use S3 Glacier Deep Archive.
B) Use Amazon Inspector to monitor SageMaker Studio.
C) Configure SageMaker to use a VPC with an S3 endpoint.
D) Use Amazon Macie to monitor SageMaker Studio.

5. A company wants to deploy a conversational chatbot to answer customer questions. The chatbot is based on a fine-tuned Amazon SageMaker JumpStart model. The application must comply with multiple regulatory frameworks.
Which capabilities can the company show compliance for? (Select TWO.)

A) Auto scaling inference endpoints
B) Data protection
C) Threat detection
D) Loosely coupled microservices
E) Cost optimization

Solutions: