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| Section | Objectives |
|---|---|
| Fundamentals of Generative AI | - Foundation models and prompt engineering basics - Large language models (LLMs) concepts - AWS generative AI services overview (e.g., Amazon Bedrock) |
| Applications of Foundation Models | - Content generation and summarization - Use cases for generative AI in business - AI-powered assistants and automation |
| Fundamentals of Artificial Intelligence and Machine Learning | - Data fundamentals for AI/ML - Core AI and ML concepts - Common ML workflows and lifecycle |
| Responsible AI and Security | - Bias, fairness, and explainability - Security, privacy, and governance in AI systems - AI ethics and responsible use |
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NEW QUESTION # 21
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to know how much information can fit into one prompt.
Which consideration will inform the company's decision?
Answer: D
Explanation:
The context window determines how much information can fit into a single prompt when using a large language model (LLM) like those on Amazon Bedrock.
* Context Window:
* The context window is the maximum amount of text (measured in tokens) that a language model can process in a single pass.
* For LLM applications, the size of the context window limits how much input data, such as text for sentiment analysis, can be fed into the model at once.
* Why Option B is Correct:
* Determines Prompt Size: The context window size directly informs how much information (e.
g., words or sentences) can fit in one prompt.
* Model Capacity: The larger the context window, the more information the model can consider for generating outputs.
* Why Other Options are Incorrect:
* A. Temperature: Controls randomness in model outputs but does not affect the prompt size.
* C. Batch size: Refers to the number of training samples processed in one iteration, not the amount of information in a prompt.
* D. Model size: Refers to the number of parameters in the model, not the input size for a single prompt.
NEW QUESTION # 22
A company is developing a new image classification model by using a dataset of photos. The dataset must follow the AWS principles of responsible AI.
Which characteristics should the dataset have to meet this requirement?
Answer: D
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS Responsible AI principles stress the importance of fairness, robustness, and accountability, which begin with the quality of the data used to train models. A responsible dataset should:
Be diverse, representing different populations, environments, and conditions to reduce bias Be sourced from reputable and appropriate sources, ensuring data quality and ethical use Have balanced categories, preventing the model from favoring one class over others and reducing the risk of discriminatory outcomes These characteristics help ensure that the resulting image classification model behaves fairly, performs reliably across groups, and aligns with AWS Responsible AI best practices.
Why the other options are incorrect:
B focuses on dataset size rather than quality, balance, or representativeness.
C increases the risk of bias and poor generalization.
D limits diversity and does not inherently ensure fairness or accountability.
AWS AI Study Guide Reference:
AWS Responsible AI principles: fairness and data quality
AWS guidance on dataset selection and preparation for responsible ML
NEW QUESTION # 23
Which option is a benefit of using Amazon SageMaker Model Cards to document AI models?
Answer: A
Explanation:
Amazon SageMaker Model Cards provide a standardized way to document important details about an AI model, such as its purpose, performance, intended usage, and known limitations. This enables transparency and compliance while fostering better communication between stakeholders. It does not store models physically or optimize computational requirements. References: AWS SageMaker Model Cards Documentation.
NEW QUESTION # 24
A company needs to train an ML model to classify images of different types of animals. The company has a large dataset of labeled images and will not label more dat a. Which type of learning should the company use to train the model?
Answer: D
Explanation:
Supervised learning is appropriate when the dataset is labeled. The model uses this data to learn patterns and classify images. Unsupervised learning, reinforcement learning, and active learning are not suitable since they either require unlabeled data or different problem settings. Reference: AWS Machine Learning Best Practices.
NEW QUESTION # 25
Which functionality does Amazon SageMaker Clarify provide?
Answer: B
Explanation:
Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further steps in the ML pipeline.
* Option C (Correct): "Exploratory data analysis": This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.
* Option A: "Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.
* Option B: "Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data's existing structure.
* Option D: "Hyperparameter tuning" is incorrect because it refers to optimizing model parameters, not analyzing the data.
AWS AI Practitioner References:
* Stages of the Machine Learning Pipeline: AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.
NEW QUESTION # 26
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