A company wants to use a pre-trained generative AI model to generate content for its marketing campaigns. The company needs to ensure that the generated content aligns with the company's brand voice and messaging requirements.
Which solution meets these requirements?
Correct Answer:C
Creating effective prompts is the best solution to ensure that the content generated by a pre-trained generative AI model aligns with the company's brand voice and messaging requirements.
✑ Effective Prompt Engineering:
✑ Why Option C is Correct:
✑ Why Other Options are Incorrect:
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?
Correct Answer:A
To achieve the lowest latency possible for inference on edge devices, deploying optimized small language models (SLMs) is the most effective solution. SLMs require fewer
resources and have faster inference times, making them ideal for deployment on edge devices where processing power and memory are limited.
✑ Option A (Correct): "Deploy optimized small language models (SLMs) on edge
devices": This is the correct answer because SLMs provide fast inference with low latency, which is crucial for edge deployments.
✑ Option B: "Deploy optimized large language models (LLMs) on edge devices" is
incorrect because LLMs are resource-intensive and may not perform well on edge devices due to their size and computational demands.
✑ Option C: "Incorporate a centralized small language model (SLM) API for
asynchronous communication with edge devices" is incorrect because it introduces network latency due to the need for communication with a centralized server.
✑ Option D: "Incorporate a centralized large language model (LLM) API for
asynchronous communication with edge devices" is incorrect for the same reason, with even greater latency due to the larger model size.
AWS AI Practitioner References:
✑ Optimizing AI Models for Edge Devices on AWS: AWS recommends using small, optimized models for edge deployments to ensure minimal latency and efficient performance.
A company has installed a security camera. The company uses an ML model to evaluate the security camera footage for potential thefts. The company has discovered that the model disproportionately flags people who are members of a specific ethnic group.
Which type of bias is affecting the model output?
Correct Answer:B
Sampling bias is the correct type of bias affecting the model output when it disproportionately flags people from a specific ethnic group.
✑ Sampling Bias:
✑ Why Option B is Correct:
✑ Why Other Options are Incorrect:
Which AWS feature records details about ML instance data for governance and reporting?
Correct Answer:A
Amazon SageMaker Model Cards provide a centralized and standardized repository for documenting machine learning models. They capture key details such as the model's intended use, training and evaluation datasets, performance metrics, ethical considerations, and other relevant information. This documentation facilitates governance and reporting by ensuring that all stakeholders have access to consistent and comprehensive information about each model. While Amazon SageMaker Debugger is used for real-time debugging and monitoring during training, and Amazon SageMaker Model Monitor tracks deployed models for data and prediction quality, neither offers the comprehensive documentation capabilities of Model Cards. Amazon SageMaker JumpStart provides pre-built models and solutions but does not focus on governance documentation.
Reference: Amazon SageMaker Model Cards
What does an F1 score measure in the context of foundation model (FM) performance?
Correct Answer:A
The F1 score is the harmonic mean of precision and recall, making it a balanced metric for evaluating model performance when there is an imbalance between false positives and false negatives. Speed, cost, and energy efficiency are unrelated to the F1 score. References: AWS Foundation Models Guide.