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| Section | Weight | Objectives |
|---|---|---|
| Applications of Foundation Models | 28% | - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Describe use cases for text, image, video, and code generation - Explain integration of foundation models into applications - Recognize tools for building and deploying generative AI solutions |
| Fundamentals of Generative AI | 24% | - Explain capabilities and use cases of generative AI - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) - Define generative AI and foundation models - Differentiate between generative AI and traditional ML |
| Security, Compliance, and Governance for AI Solutions | 14% | - Security controls for AI data and models - Compliance requirements and regulations - Governance frameworks for AI lifecycle - Data protection and privacy in AI workflows |
| Guidelines for Responsible AI | 14% | - Explain bias detection and reduction - Describe ethical and societal impacts of AI - Identify risks and mitigation strategies for AI systems - Define responsible AI principles: fairness, transparency, privacy, safety |
| Fundamentals of AI and ML | 20% | - Recognize key concepts: data, models, training, inference, evaluation - Identify types of ML: supervised, unsupervised, reinforcement learning - Define artificial intelligence (AI), machine learning (ML), and deep learning - Describe common ML workflows and lifecycle |
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NEW QUESTION # 372
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: C
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 # 373
An AI practitioner has prepared a dataset for training models in Amazon SageMaker AI. The AI practitioner wants to share the dataset within the company so that future employees can discover and reuse the dataset.
Which solution will meet these requirements?
Answer: D
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS recommends Amazon SageMaker Feature Store as a centralized, managed repository for storing, discovering, and reusing features and datasets across teams and projects. Feature Store enables:
Feature discovery through searchable metadata
Reusability of curated datasets and features by future practitioners
Consistency between training and inference datasets
Governance and lineage, supporting responsible AI practices
Why the other options are incorrect:
A is intended for retrieval-augmented generation, not dataset sharing.
B does not provide discoverability, governance, or long-term reuse.
D is designed for sharing data with external subscribers, not internal ML teams.
AWS AI Study Guide Reference:
Amazon SageMaker Feature Store overview
AWS best practices for feature management and reuse
NEW QUESTION # 374
A company wants to build an ML application.
Select and order the correct steps from the following list to develop a well-architected ML workload. Each step should be selected one time. (Select and order FOUR.)
* Deploy model
* Develop model
* Monitor model
* Define business goal and frame ML problem
Answer:
Explanation:
Explanation:
Building a well-architected ML workload follows a structured lifecycle as outlined in AWS best practices.
The process begins with defining the business goal and framing the ML problem to ensure the project aligns with organizational objectives. Next, the model is developed, which includes data preparation, training, and evaluation. Once the model is ready, it is deployed tomake predictions in a production environment. Finally, the model is monitored to ensure it performs as expected and to address any issues like drift or degradation over time. This order ensures a systematic approach to ML development.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"The machine learning lifecycle typically follows these stages: 1) Define the business goal and frame the ML problem, 2) Develop the model (including data preparation, training, and evaluation), 3) Deploy the model to production, and 4) Monitor the model for performance and drift to ensure it continues to meet business needs." (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Lifecycle) Detailed Explanation:
Step 1: Define business goal and frame ML problemThis is the first step in any ML project. It involves understanding the business objective (e.g., reducing churn) and framing the ML problem (e.g., classification or regression). Without this step, the project lacks direction. The hotspot lists this option as "Define business goal and frame ML problem," which matches this stage.
Step 2: Develop modelAfter defining the problem, the next step is to develop the model. This includes collecting and preparing data, selecting an algorithm, training the model, and evaluating its performance. The hotspot lists "Develop model" as an option, aligning with this stage.
Step 3: Deploy modelOnce the model is developed and meets performance requirements, it is deployed to a production environment to make predictions or automate decisions. The hotspot includes "Deploy model" as an option, which fits this stage.
Step 4: Monitor modelAfter deployment, the model must be monitored to ensure it performs well over time, addressing issues like data drift or performance degradation. The hotspot lists "Monitor model" as an option, completing the lifecycle.
Hotspot Selection Analysis:
The hotspot provides four steps, each with the same dropdown options: "Select...," "Deploy model," "Develop model," "Monitor model," and "Define business goal and frame ML problem." The correct selections are:
Step 1: Define business goal and frame ML problem
Step 2: Develop model
Step 3: Deploy model
Step 4: Monitor model
Each option is used exactly once, as required, and follows the logical order of the ML lifecycle.
References:
AWS AI Practitioner Learning Path: Module on Machine Learning Lifecycle Amazon SageMaker Developer Guide: Machine Learning Workflow (https://docs.aws.amazon.com/sagemaker
/latest/dg/how-it-works-mlconcepts.html)
AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected
/latest/machine-learning-lens/)
NEW QUESTION # 375
A company wants to build an interactive application for children that generates new stories based on classic stories. The company wants to use Amazon Bedrock and needs to ensure that the results and topics are appropriate for children.
Which AWS service or feature will meet these requirements?
Answer: C
Explanation:
Amazon Bedrock is a service that provides foundational models for building generative AI applications.
When creating an application for children, it is crucial to ensure that the generated content is appropriate for the target audience. "Guardrails" in Amazon Bedrock provide mechanisms to control the outputs and topics of generated content to align with desired safety standards and appropriateness levels.
* Option C (Correct): "Guardrails for Amazon Bedrock": This is the correct answer because guardrails are specifically designed to help users enforce content moderation, filtering, and safety checks on the outputs generated by models in Amazon Bedrock. For a children's application, guardrails ensure that all content generated is suitable and appropriate for the intended audience.
* Option A: "Amazon Rekognition" is incorrect. Amazon Rekognition is an image and video analysis service that can detect inappropriate content in images or videos, but it does not handle text or story generation.
* Option B: "Amazon Bedrock playgrounds" is incorrect because playgrounds are environments for experimenting and testing model outputs, but they do not inherently provide safeguards to ensure content appropriateness for specific audiences, such as children.
* Option D: "Agents for Amazon Bedrock" is incorrect. Agents in Amazon Bedrock facilitate building AI applications with more interactive capabilities, but they do not provide specific guardrails for ensuring content appropriateness for children.
AWS AI Practitioner References:
* Guardrails in Amazon Bedrock: Designed to help implement controls that ensure generated content is safe and suitable for specific use cases or audiences, such as children, by moderating and filtering inappropriate or undesired content.
* Building Safe AI Applications: AWS provides guidance on implementing ethical AI practices, including using guardrails to protect against generating inappropriate or biased content.
NEW QUESTION # 376
A financial company is developing a fraud detection system that flags potential fraud cases in credit card transactions. Employees will evaluate the flagged fraud cases. The company wants to minimize the amount of time the employees spend reviewing flagged fraud cases that are not actually fraudulent.
Which evaluation metric meets these requirements?
Answer: C
Explanation:
Precision is the metric that measures the proportion of true positives (actual frauds) among all flagged positives (flagged frauds). High precision ensures that most of the flagged cases are truly fraudulent, minimizing the number of false positives employees must review.
C is correct:
"Precision is the ratio of true positives to all predicted positives, and it answers: 'Of all the cases flagged as fraud, how many were actually fraud?' High precision means fewer non-fraudulent cases are sent for manual review." (Reference: AWS ML Concepts - Precision and Recall, AWS Certified AI Practitioner Study Guide)
"Precision is the ratio of true positives to all predicted positives, and it answers: 'Of all the cases flagged as fraud, how many were actually fraud?' High precision means fewer non-fraudulent cases are sent for manual review." (Reference: AWS ML Concepts - Precision and Recall, AWS Certified AI Practitioner Study Guide) A (Recall) measures how many actual frauds are caught, but does not minimize false positives.
B (Accuracy) can be misleading in imbalanced datasets (like fraud detection).
NEW QUESTION # 377
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