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| Section | Weight | Objectives |
|---|---|---|
| Applications of Foundation Models | 28% | - Recognize tools for building and deploying generative AI solutions - Explain integration of foundation models into applications - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan - Describe use cases for text, image, video, and code generation |
| Security, Compliance, and Governance for AI Solutions | 14% | - Security controls for AI data and models - Governance frameworks for AI lifecycle - Compliance requirements and regulations - Data protection and privacy in AI workflows |
| Guidelines for Responsible AI | 14% | - Define responsible AI principles: fairness, transparency, privacy, safety - Identify risks and mitigation strategies for AI systems - Describe ethical and societal impacts of AI - Explain bias detection and reduction |
| Fundamentals of AI and ML | 20% | - Recognize key concepts: data, models, training, inference, evaluation - Define artificial intelligence (AI), machine learning (ML), and deep learning - Describe common ML workflows and lifecycle - Identify types of ML: supervised, unsupervised, reinforcement learning |
| Fundamentals of Generative AI | 24% | - Differentiate between generative AI and traditional ML - Define generative AI and foundation models - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) - Explain capabilities and use cases of generative AI |
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NEW QUESTION # 44
A company is building an AI application to summarize books of varying lengths. During testing, the application fails to summarize some books. Why does the application fail to summarize some books?
Answer: C
Explanation:
* Foundation models have a context window (max tokens), which limits the size of the input text (prompt + instructions).
* If the input (e.g., a very long book) exceeds this limit, the model cannot process it, causing failure.
* Temperature (A) and Top P (C) control randomness, not input size.
* Fine-tuning (B) is irrelevant to input truncation failures.
# Reference:
AWS Documentation - Amazon Bedrock Model Parameters (context size limits)
NEW QUESTION # 45
A company used Amazon Bedrock to build an AI assistant. The AI assistant received a user prompt that instructed the assistant to provide the guidelines that the company used to configure the assistant. The prompt also instructed the assistant to display the full text of the initial setup. The AI assistant revealed its system prompt in the response.
Which type of prompt attack has occurred?
Answer: D
Explanation:
The attack is specifically prompt leakage because the user ' s objective is to obtain confidential system-level instructions or configuration information from the AI application. AWS defines prompt leakage as user prompts designed to "extract or reveal the system prompt, developer instructions, or other confidential configuration details." The scenario precisely matches this definition. The user asks the assistant to disclose the guidelines used to configure it and to reproduce the complete initial setup. The assistant then reveals its system prompt. The defining outcome is therefore unauthorized disclosure of protected prompt instructions.
Prompt leakage is related to the broader category of prompt attacks , and a leakage attempt can involve prompt-injection techniques. However, when AWS asks for the specific type of attack and the attacker is intentionally trying to expose the hidden system prompt or developer instructions, prompt leakage is the more precise classification.
A jailbreak generally attempts to bypass model safeguards or restrictions so the model performs behavior that would normally be prohibited. Although jailbreaks and prompt leakage can overlap, the core purpose in this scenario is disclosure of system configuration rather than simply bypassing a safety policy.
Prompt injection is the broader technique of introducing malicious instructions designed to override or manipulate existing instructions. For example, an attacker might tell the application to ignore previous instructions. AWS includes jailbreaks, prompt injections, and prompt leakages within its prompt-attack filtering capabilities.
A denied topic violation occurs when content concerns a topic explicitly configured as prohibited in Amazon Bedrock Guardrails. It does not describe system-prompt disclosure.
AWS recommends protecting generative AI applications by using Bedrock Guardrails prompt-attack detection, clearly defined system instructions, controlled access to tools and resources, input evaluation, output validation, and security testing.
Because confidential system instructions were explicitly requested and subsequently disclosed, the exact AWS-aligned classification is B. Prompt leakage .
NEW QUESTION # 46
A company has created a custom model by fine-tuning an existing large language model (LLM) from Amazon Bedrock. The company wants to deploy the model to production and use the model to handle a steady rate of requests each minute.
Which solution meets these requirements MOST cost-effectively?
Answer: D
Explanation:
The correct answer is D - Purchase Provisioned Throughput on Amazon Bedrock, which provides guaranteed and predictable model capacity for workloads with consistent or steady request volume. According to AWS Bedrock documentation, Provisioned Throughput is specifically designed for production applications that require reliable, consistent inference performance at a controlled cost. It allows customers to reserve a fixed number of model inference units (MIUs), ensuring low latency and cost savings compared to on-demand pricing when traffic is steady. On-demand throughput (option B) is ideal for unpredictable or sporadic usage, but it becomes more expensive for stable traffic patterns because it charges per token with no discount for steady volume. Hosting the model on EC2 (option A) or Lambda (option C) increases operational overhead, requires model containerization, scaling management, and may not support LLM-level GPU performance efficiently. Bedrock Provisioned Throughput eliminates infrastructure management and provides the most cost-effective solution for stable, predictable workloads.
Referenced AWS Documentation:
* Amazon Bedrock Developer Guide - Provisioned Throughput
* AWS ML Specialty Study Guide - Cost Optimization for Generative AI
NEW QUESTION # 47
A company wants to display the total sales for its top-selling products across various retail locations in the past 12 months.
Which AWS solution should the company use to automate the generation of graphs?
Answer: B
NEW QUESTION # 48
A company wants to develop an Al application to help its employees check open customer claims, identify details for a specific claim, and access documents for a claim. Which solution meets these requirements?
Answer: A
Explanation:
The company wants an AI application to help employees check open customer claims, identify claim details, and access related documents. Agents for Amazon Bedrock can automate tasks by interacting with external systems, while Amazon Bedrock knowledge bases provide a repository of information (e.g., claim details and documents) that the agent can query to respond to employee requests, making this the best solution.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Agents for Amazon Bedrock enable developers to build applications that can perform tasks by interacting with external systems and data sources. When paired with Amazon Bedrock knowledge bases, agents can access structured and unstructured data, such as documents or databases, to provide detailed responses for use cases like customer service or claims management." (Source: AWS Bedrock User Guide, Agents and Knowledge Bases) Detailed Explanation:
Option A: Use Agents for Amazon Bedrock with Amazon Fraud Detector to build the application.Amazon Fraud Detector is for detecting fraudulent activities, not for managing customer claims or accessing documents. This option is irrelevant.
Option B: Use Agents for Amazon Bedrock with Amazon Bedrock knowledge bases to build the application.
This is the correct answer. Agents for Amazon Bedrock can interact with knowledge bases to retrieve claim details and documents, enabling employees to check open claims and access relevant information.
Option C: Use Amazon Personalize with Amazon Bedrock knowledge bases to build the application.Amazon Personalize is for building recommendation systems, not for retrieving claim details or documents. This option does not meet the requirements.
Option D: Use Amazon SageMaker AI to build the application by training a new ML model.Training a new ML model on SageMaker is unnecessary and complex for this use case, as the task can be efficiently handled by Agents and knowledge bases on Amazon Bedrock.
References:
AWS Bedrock User Guide: Agents and Knowledge Bases (https://docs.aws.amazon.com/bedrock/latest
/userguide/agents.html)
AWS AI Practitioner Learning Path: Module on Generative AI and Knowledge Bases Amazon Bedrock Developer Guide: Building AI Applications (https://aws.amazon.com/bedrock/)
NEW QUESTION # 49
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