A company is training a foundation model (FM). The company wants to increase the accuracy of the model up to a specific acceptance level.Which solution will meet these requirements?
Answer(s): B
Increasing the number of epochs allows the model to train for more iterations, improving its accuracy until the model reaches an optimal level. The other options are either less effective or unrelated to improving accuracy.
A company is building a large language model (LLM) question answering chatbot. The company wants to decrease the number of actions call center employees need to take to respond to customer questions.Which business objective should the company use to evaluate the effect of the LLM chatbot?
Reducing the average call duration directly indicates how effectively the LLM chatbot is helping call center employees answer customer questions, thus reducing the number of actions needed. The other options are not directly related to the performance of a call center chatbot.
Which functionality does Amazon SageMaker Clarify provide?
Answer(s): D
Amazon SageMaker Clarify helps detect potential bias in datasets and models during data preparation, training, and deployment. It also provides tools for explainability. The other options are functionalities that do not directly match SageMaker Clarify's core features.
A company is developing a new model to predict the prices of specific items. The model performed well on the training dataset. When the company deployed the model to production, the model's performance decreased significantly.What should the company do to mitigate this problem?
Answer(s): C
Increasing the volume of data used in training helps the model generalize better to new, unseen data, reducing overfitting and improving performance in production. The other options either do not address the issue of model generalization or are unlikely to effectively solve the problem.
An ecommerce company wants to build a solution to determine customer sentiments based on written customer reviews of products.Which AWS services meet these requirements? (Choose two.)
Answer(s): B,D
Amazon Comprehend: This service is specifically designed for natural language processing (NLP) tasks, including sentiment analysis, making it ideal for analyzing customer reviews.Amazon Bedrock: Bedrock can be used to leverage foundation models, which can also be employed for sentiment analysis tasks.The other options are not suitable for sentiment analysis of written customer reviews.
A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company's product manuals. The manuals are stored as PDF files.Which solution meets these requirements MOST cost-effectively?
Using an Amazon Bedrock knowledge base allows the model to efficiently access relevant information from the PDF manuals when needed, reducing the cost compared to continuously fine-tuning a model or providing all PDFs as context in each prompt. This approach ensures that only necessary context is provided, making it cost-effective.
A social media company wants to use a large language model (LLM) for content moderation. The company wants to evaluate the LLM outputs for bias and potential discrimination against specific groups or individuals.Which data source should the company use to evaluate the LLM outputs with the LEAST administrative effort?
Benchmark datasets are standardized datasets specifically designed for evaluating models for bias and fairness, allowing for efficient assessment with minimal administrative effort. The other options would require more manual processing and might not provide a consistent basis for evaluating bias and discrimination.
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?
Creating effective prompts helps guide the pre-trained generative AI model to produce content that aligns with the company's brand voice and messaging. The other options either involve model architecture changes or require extensive training, which are not necessary for aligning content generation.
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