A company has petabytes of unlabeled customer data to use for an advertisement campaign. The company wants to classify its customers into tiers to advertise and promote the company's products.Which methodology should the company use to meet these requirements?
Answer(s): B
Unsupervised learning is suitable for analyzing unlabeled data and grouping it into clusters or tiers, which aligns with the company's goal of classifying customers. The other methods require labeled data or are used for different types of problems.
An AI practitioner wants to use a foundation model (FM) to design a search application. The search application must handle queries that have text and images.Which type of FM should the AI practitioner use to power the search application?
Answer(s): A
A multi-modal embedding model can handle both text and image queries by embedding them into a shared space, enabling the search application to process and relate different data types. The other options are not suitable for handling both text and image inputs effectively.
A company uses a foundation model (FM) from Amazon Bedrock for an AI search tool. The company wants to fine-tune the model to be more accurate by using the company's data.Which strategy will successfully fine-tune the model?
Fine-tuning a foundation model involves training it with labeled data that contains both input prompts and corresponding expected completions to adjust the model's behavior to fit the company's needs. The other options are not directly related to the fine-tuning process using specific labeled data.
A company wants to use AI to protect its application from threats. The AI solution needs to check if an IP address is from a suspicious source.Which solution meets these requirements?
Answer(s): C
An anomaly detection system can identify suspicious behavior, such as IP addresses that deviate from expected patterns, which helps in protecting the application from threats. The other options are not designed for detecting suspicious IP addresses.
Which feature of Amazon OpenSearch Service gives companies the ability to build vector database applications?
The scalable index management and nearest neighbor search capability in Amazon OpenSearch Service enables companies to build vector database applications, which are crucial for tasks like similarity search in AI models. The other options do not specifically provide the vector search functionality.
Which option is a use case for generative AI models?
Generative AI models are used to create new content, such as photorealistic images from text descriptions, which is useful for digital marketing. The other options involve tasks better suited for analytical or detection systems rather than generative models.
A company wants to build a generative AI application by using Amazon Bedrock and needs to choose a foundation model (FM). The company wants to know how much information can fit into one prompt.Which consideration will inform the company's decision?
The context window determines how much information can fit into a single prompt. It specifies the number of tokens the foundation model can process at once, affecting the length of input that can be provided. The other options do not directly relate to prompt size.
A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention.The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that adhere to company tone.Which solution meets these requirements?
Experimenting and refining the prompt allows you to guide the FM to produce responses that align with the company's desired tone. This approach helps to shape the behavior of the chatbot. The other options do not directly ensure adherence to company tone.
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