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| Section | Weight | Objectives |
|---|---|---|
| Fundamentals of AI and ML | 20% | - Define artificial intelligence (AI), machine learning (ML), and deep learning - Identify types of ML: supervised, unsupervised, reinforcement learning - Describe common ML workflows and lifecycle - Recognize key concepts: data, models, training, inference, evaluation |
| Applications of Foundation Models | 28% | - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Recognize tools for building and deploying generative AI solutions - Describe use cases for text, image, video, and code generation - Explain integration of foundation models into applications |
| Security, Compliance, and Governance for AI Solutions | 14% | - Governance frameworks for AI lifecycle - Compliance requirements and regulations - Security controls for AI data and models - Data protection and privacy in AI workflows |
| Fundamentals of Generative AI | 24% | - Differentiate between generative AI and traditional ML - Define generative AI and foundation models - Explain capabilities and use cases of generative AI - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) |
| Guidelines for Responsible AI | 14% | - Identify risks and mitigation strategies for AI systems - Describe ethical and societal impacts of AI - Define responsible AI principles: fairness, transparency, privacy, safety - Explain bias detection and reduction |
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NEW QUESTION # 62
What is an example of structured data?
Answer: B
Explanation:
The correct answer is C - A CSV file that consists of measurement data, which is a classic example of structured data. According to AWS documentation, structured data is data that is organized in a predefined schema, typically stored in tabular formats with fixed rows, columns, and data types. CSV files, relational databases, and spreadsheets fall into this category because their structure allows deterministic querying and processing. Measurement data contained in a CSV file is easy to analyze with SQL, Amazon Athena, Amazon Redshift, or SageMaker Data Wrangler. In contrast, options A and D (text comments and transcriptions) are examples of unstructured data, requiring NLP techniques. Option B (video files) represents unstructured multimedia data, requiring computer vision. Structured data is essential for supervised ML models, business analytics, and statistical modeling. AWS emphasizes that structured data provides the simplest path to feature engineering and model training due to its consistent format.
Referenced AWS Documentation:
* AWS Data Analytics Whitepaper - Structured vs. Unstructured Data
* Amazon SageMaker Data Wrangler Documentation
NEW QUESTION # 63
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: B
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 # 64
A financial institution is using Amazon Bedrock to develop an AI application. The application is hosted in a VPC. To meet regulatory compliance standards, the VPC is not allowed access to any internet traffic.
Which AWS service or feature will meet these requirements?
Answer: A
NEW QUESTION # 65
A bank is building a chatbot to answer customer questions about opening a bank account. The chatbot will use public bank documents to generate responses. The company will use Amazon Bedrock and prompt engineering to improve the chatbot's responses.
Which prompt engineering technique meets these requirements?
Answer: C
Explanation:
Directional stimulus prompting guides the foundation model to produce outputs aligned with business context. It's particularly effective for aligning responses with public documents and improving coherence. From Bedrock Prompt Engineering Techniques documentation:
Explanation:
Directional stimulus prompting guides the foundation model to produce outputs aligned with business context. It's particularly effective for aligning responses with public documents and improving coherence. From Bedrock Prompt Engineering Techniques documentation:
"Directional stimulus prompting provides structured prompts to steer the model output towards desired formats or behaviors using specific linguistic cues."
NEW QUESTION # 66
A company is using a generative AI model to develop a digital assistant. The model's responses occasionally include undesirable and potentially harmful content. Select the correct Amazon Bedrock filter policy from the following list for each mitigation action. Each filter policy should be selected one time. (Select FOUR.)
* Content filters
* Contextual grounding check
* Denied topics
* Word filters
Answer:
Explanation:
Explanation:
Block input prompts or model responses that contain harmful content such as hate, insults, violence, or misconduct:Content filters Avoid subjects related to illegal investment advice or legal advice:Denied topics Detect and block specific offensive terms:Word filters Detect and filter out information in the model's responses that is not grounded in the provided source information:Contextual grounding check The company is using a generative AI model on Amazon Bedrock and needs to mitigate undesirable and potentially harmful content in the model's responses. Amazon Bedrock provides several guardrail mechanisms, including content filters, denied topics, word filters, and contextual grounding checks, to ensure safe and accurate outputs. Each mitigation action in the hotspot aligns with a specific Bedrock filter policy, and each policy must be used exactly once.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
*"Amazon Bedrock guardrails provide mechanisms to control model outputs, including:
* Content filters: Block harmful content such as hate speech, violence, or misconduct.
* Denied topics: Prevent the model from generating responses on specific subjects, such as illegal activities or advice.
* Word filters: Detect and block specific offensive or inappropriate terms.
* Contextual grounding check: Ensure responses are grounded in the provided source information, filtering out ungrounded or hallucinated content."*(Source: AWS Bedrock User Guide, Guardrails for Responsible AI) Detailed Explanation:
* Block input prompts or model responses that contain harmful content such as hate, insults, violence, or misconduct: Content filtersContent filters in Amazon Bedrock are designed to detect and block harmful content, such as hate speech, insults, violence, or misconduct, ensuring the model's outputs are safe and appropriate. This matches the first mitigation action.
* Avoid subjects related to illegal investment advice or legal advice: Denied topicsDenied topics allow users to specify subjects the model should avoid, such as illegal investment advice or legal advice, which could have regulatory implications. This policy aligns with the second mitigation action.
* Detect and block specific offensive terms: Word filtersWord filters enable the detection and blocking of specific offensive or inappropriate terms defined by the user, making them ideal for this mitigation action focused on specific terms.
* Detect and filter out information in the model's responses that is not grounded in the provided source information: Contextual grounding checkThe contextual grounding check ensures that the model's responses are based on the provided source information, filtering out ungrounded or hallucinated content. This matches the fourth mitigation action.
Hotspot Selection Analysis:
The hotspot lists four mitigation actions, each with the same dropdown options: "Select...," "Content filters,"
"Contextual grounding check," "Denied topics," and "Word filters." The correct selections are:
* First action: Content filters
* Second action: Denied topics
* Third action: Word filters
* Fourth action: Contextual grounding check
Each filter policy is used exactly once, as required, and aligns with Amazon Bedrock's guardrail capabilities.
References:
AWS Bedrock User Guide: Guardrails for Responsible AI (https://docs.aws.amazon.com/bedrock/latest
/userguide/guardrails.html)
AWS AI Practitioner Learning Path: Module on Responsible AI and Model Safety Amazon Bedrock Developer Guide: Configuring Guardrails (https://aws.amazon.com/bedrock/)
NEW QUESTION # 67
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