Which feature of Amazon OpenSearch Service gives companies the ability to build vector database applications?
Correct Answer:C
Amazon OpenSearch Service (formerly Amazon Elasticsearch Service) has introduced capabilities to support vector search, which allows companies to build vector database applications. This is particularly useful in machine learning, where vector representations (embeddings) of data are often used to capture semantic meaning.
Scalable index management and nearest neighbor search capability are the core features enabling vector database functionalities in OpenSearch. The service allows users to index high-dimensional vectors and perform efficient nearest neighbor searches, which are crucial for tasks such as recommendation systems, anomaly detection, and semantic search.
Here is why option C is the correct Answer:
✑ Scalable Index Management: OpenSearch Service supports scalable indexing of vector data. This means you can index a large volume of high-dimensional vectors
and manage these indexes in a cost-effective and performance-optimized way. The service leverages underlying AWS infrastructure to ensure that indexing scales seamlessly with data size.
✑ Nearest Neighbor Search Capability: OpenSearch Service's nearest neighbor
search capability allows for fast and efficient searches over vector data. This is essential for applications like product recommendation engines, where the system needs to quickly find the most similar items based on a user's query or behavior.
✑ AWS AI Practitioner References:
The other options do not directly relate to building vector database applications:
✑ A. Integration with Amazon S3 for object storage is about storing data objects, not vector-based searching or indexing.
✑ B. Support for geospatial indexing and queries is related to location-based data, not vectors used in machine learning.
✑ D. Ability to perform real-time analysis on streaming data relates to analyzing incoming data streams, which is different from the vector search capabilities.
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?
Correct Answer:B
The business objective to evaluate the effect of an LLM chatbot aimed at reducing the actions required by call center employees should be average call duration.
✑ Average Call Duration:
✑ Why Option B is Correct:
✑ Why Other Options are Incorrect:
Which option is a use case for generative AI models?
Correct Answer:B
Generative AI models are used to create new content based on existing data. One common use case is generating photorealistic images from text descriptions, which is particularly useful in digital marketing, where visual content is key to engaging potential customers.
✑ Option B (Correct): "Creating photorealistic images from text descriptions for digital
marketing": This is the correct answer because generative AI models, like those offered by Amazon Bedrock, can create images based on text descriptions, making them highly valuable for generating marketing materials.
✑ Option A: "Improving network security by using intrusion detection systems" is
incorrect because this is a use case for traditional machine learning models, not generative AI.
✑ Option C: "Enhancing database performance by using optimized indexing" is
incorrect as it is unrelated to generative AI.
✑ Option D: "Analyzing financial data to forecast stock market trends" is incorrect because it typically involves predictive modeling rather than generative AI.
AWS AI Practitioner References:
✑ Use Cases for Generative AI Models on AWS: AWS highlights the use of generative AI for creative content generation, including image creation, text generation, and more, which is suited for digital marketing applications.
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?
Correct Answer:A
Providing labeled data with both a prompt field and a completion field is the correct strategy for fine-tuning a foundation model (FM) on Amazon Bedrock.
✑ Fine-Tuning Strategy:
✑ Why Option A is Correct:
✑ Why Other Options are Incorrect:
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?
Correct Answer:D
Benchmark datasets are pre-validated datasets specifically designed to evaluate machine learning models for bias, fairness, and potential discrimination. These datasets are the most efficient tool for assessing an LLM??s performance against known standards with
minimal administrative effort.
✑ Option D (Correct): "Benchmark datasets": This is the correct answer because using standardized benchmark datasets allows the company to evaluate model outputs for bias with minimal administrative overhead.
✑ Option A: "User-generated content" is incorrect because it is unstructured and would require significant effort to analyze for bias.
✑ Option B: "Moderation logs" is incorrect because they represent historical data and do not provide a standardized basis for evaluating bias.
✑ Option C: "Content moderation guidelines" is incorrect because they provide qualitative criteria rather than a quantitative basis for evaluation.
AWS AI Practitioner References:
✑ Evaluating AI Models for Bias on AWS: AWS supports using benchmark datasets to assess model fairness and detect potential bias efficiently.