A company has a foundation model (FM) that was customized by using Amazon Bedrock to answer customer queries about products. The company wants to validate the model's responses to new types of queries. The company needs to upload a new dataset that Amazon Bedrock can use for validation.
Which AWS service meets these requirements?
Correct Answer:
A
Amazon S3 is the optimal choice for storing and uploading datasets used for machine learning model validation and training. It offers scalable, durable, and secure storage, making it ideal for holding datasets required by Amazon Bedrock for validation purposes.
✑ Option A (Correct): "Amazon S3": This is the correct answer because Amazon S3
is widely used for storing large datasets that are accessed by machine learning models, including those in Amazon Bedrock.
✑ Option B: "Amazon Elastic Block Store (Amazon EBS)" is incorrect because EBS
is a block storage service for use with Amazon EC2, not for directly storing datasets for Amazon Bedrock.
✑ Option C: "Amazon Elastic File System (Amazon EFS)" is incorrect as it is
primarily used for file storage with shared access by multiple instances.
✑ Option D: "AWS Snowcone" is incorrect because it is a physical device for offline data transfer, not suitable for directly providing data to Amazon Bedrock.
AWS AI Practitioner References:
✑ Storing and Managing Datasets on AWS for Machine Learning: AWS recommends using S3 for storing and managing datasets required for ML model training and validation.
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 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 wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output.
Which ML algorithm meets these requirements?
Correct Answer:
A
Decision trees are an interpretable machine learning algorithm that clearly documents the decision-making process by showing how each input feature affects the output. This transparency is particularly useful when explaining how the model arrives at a certain decision, making it suitable for classifying genes into categories.
✑ Option A (Correct): "Decision trees": This is the correct answer because decision
trees provide a clear and interpretable representation of how input features influence the model's output, making it ideal for understanding the inner mechanisms affecting predictions.
✑ Option B: "Linear regression" is incorrect because it is used for regression tasks,
not classification.
✑ Option C: "Logistic regression" is incorrect as it does not provide the same level of interpretability in documenting decision-making processes.
✑ Option D: "Neural networks" is incorrect because they are often considered "black boxes" and do not easily explain how they arrive at their outputs.
AWS AI Practitioner References:
✑ Interpretable Machine Learning Models on AWS: AWS supports using interpretable models, such as decision trees, for tasks that require clear documentation of how input data affects output decisions.
What are tokens in the context of generative AI models?
Correct Answer:
A
Tokens in generative AI models are the smallest units that the model processes, typically representing words, subwords, or characters. They are essential for the model to understand and generate language, breaking down text into manageable parts for processing.
✑ Option A (Correct): "Tokens are the basic units of input and output that a
generative AI model operates on, representing words, subwords, or other linguistic units": This is the correct definition of tokens in the context of generative AI models.
✑ Option B: "Mathematical representations of words" describes embeddings, not
tokens.
✑ Option C: "Pre-trained weights of a model" refers to the parameters of a model, not tokens.
✑ Option D: "Prompts or instructions given to a model" refers to the queries or commands provided to a model, not tokens.
AWS AI Practitioner References:
✑ Understanding Tokens in NLP: AWS provides detailed explanations of how tokens are used in natural language processing tasks by AI models, such as in Amazon Comprehend and other AWS AI services.
