A retail store wants to predict the demand for a specific product for the next few weeks by using the Amazon SageMaker DeepAR forecasting algorithm.
Which type of data will meet this requirement?
Correct Answer:C
Amazon SageMaker's DeepAR is a supervised learning algorithm designed for forecasting scalar (one-dimensional) time series data. Time series data consists of sequences of data points indexed in time order, typically with consistent intervals between them. In the context of a retail store aiming to predict product demand, relevant time series data might include historical sales figures, inventory levels, or related metrics recorded over regular time intervals (e.g., daily or weekly). By training the DeepAR model on this historical time series data, the store can generate forecasts for future product demand. This capability is
particularly useful for inventory management, staffing, and supply chain optimization. Other data types, such as text, image, or binary data, are not suitable for time series forecasting tasks and would not be appropriate inputs for the DeepAR algorithm.
Reference: Amazon SageMaker DeepAR Algorithm
Which term describes the numerical representations of real-world objects and concepts that AI and natural language processing (NLP) models use to improve understanding of textual information?
Correct Answer:A
Embeddings are numerical representations of objects (such as words, sentences, or documents) that capture the objects' semantic meanings in a form that AI and NLP models can easily understand. These representations help models improve their understanding of textual information by representing concepts in a continuous vector space.
✑ Option A (Correct): "Embeddings": This is the correct term, as embeddings provide
a way for models to learn relationships between different objects in their input space, improving their understanding and processing capabilities.
✑ Option B: "Tokens" are pieces of text used in processing, but they do not capture
semantic meanings like embeddings do.
✑ Option C: "Models" are the algorithms that use embeddings and other inputs, not the representations themselves.
✑ Option D: "Binaries" refer to data represented in binary form, which is unrelated to the concept of embeddings.
AWS AI Practitioner References:
✑ Understanding Embeddings in AI and NLP: AWS provides resources and tools, like Amazon SageMaker, that utilize embeddings to represent data in formats suitable for machine learning models.
A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents. Which solution meets these requirements?
Correct Answer:C
A summarization chatbot is ideal for extracting key points from legal documents. Large language models (LLMs) can be used to summarize complex texts, such as legal documents, making them more accessible and understandable.
✑ Option C (Correct): "Develop a summarization chatbot": This is the correct answer
because a summarization chatbot uses LLMs to condense and extract key information from text, which is precisely the requirement for reading and summarizing legal documents.
✑ Option A: "Build an automatic named entity recognition system" is incorrect
because it focuses on identifying specific entities, not summarizing documents.
✑ Option B: "Create a recommendation engine" is incorrect as it is used to suggest products or content, not summarize text.
✑ Option D: "Develop a multi-language translation system" is incorrect because translation is unrelated to summarizing text.
AWS AI Practitioner References:
✑ Using LLMs for Text Summarization on AWS: AWS supports developing summarization tools using its AI services, including Amazon Bedrock.