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1. What are tokens in the context of generative AI models?

A) Tokens are the pre-trained weights of a generative AI model that are fine-tuned for specific tasks.
B) Tokens are the mathematical representations of words or concepts used in generative AI models.
C) Tokens are the basic units of input and output that a generative AI model operates on, representing words, subwords, or other linguistic units.
D) Tokens are the specific prompts or instructions given to a generative AI model to generate output.

2. A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model.
The company needs to perform analysis on internal data and external data.
Which solution will meet these requirements?

A) Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.
B) Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon SageMaker built-in algorithms that use the data from Amazon S3.
C) Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.
D) Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.

3. A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.
Which action must the company take to use the custom model through Amazon Bedrock?

A) Purchase Provisioned Throughput for the custom model.
B) Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.
C) Register the model with the Amazon SageMaker Model Registry.
D) Grant access to the custom model in Amazon Bedrock.

4. An accounting firm wants to implement a large language model (LLM) to automate document processing.
The firm must proceed responsibly to avoid potential harms.
What should the firm do when developing and deploying the LLM? (Select TWO.)

A) Apply prompt engineering techniques.
B) Include fairness metrics for model evaluation.
C) Modify the training data to mitigate bias.
D) Adjust the temperature parameter of the model.
E) Avoid overfitting on the training data.

5. A company wants to use language models to create an application for inference on edge devices.
The inference must have the lowest latency possible.
Which solution will meet these requirements?

A) Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices.
B) Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.
C) Deploy optimized large language models (LLMs) on edge devices.
D) Deploy optimized small language models (SLMs) on edge devices.

問題與答案：