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| Section | Weight | Objectives |
|---|---|---|
| Security, Compliance, and Governance for AI Solutions | 14% | - Governance frameworks for AI lifecycle - Data protection and privacy in AI workflows - Security controls for AI data and models - Compliance requirements and regulations |
| Fundamentals of Generative AI | 24% | - Differentiate between generative AI and traditional ML - Explain capabilities and use cases of generative AI - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) - Define generative AI and foundation models |
| Applications of Foundation Models | 28% | - 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 - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan |
| Guidelines for Responsible AI | 14% | - Explain bias detection and reduction - Define responsible AI principles: fairness, transparency, privacy, safety - Describe ethical and societal impacts of AI - Identify risks and mitigation strategies for AI systems |
| Fundamentals of AI and ML | 20% | - Describe common ML workflows and lifecycle - Define artificial intelligence (AI), machine learning (ML), and deep learning - Identify types of ML: supervised, unsupervised, reinforcement learning - Recognize key concepts: data, models, training, inference, evaluation |
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NEW QUESTION # 260
An animation company wants to provide subtitles for its content. Which AWS service meets this requirement?
Answer: A
Explanation:
Amazon Transcribe is the AWS service that converts speech to text, enabling the generation of subtitles (closed captions) for audio and video content automatically.
C is correct:
"Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for developers to add speech-to-text capability to applications." This feature supports creating subtitles and transcripts for media files.
(Reference: Amazon Transcribe Overview, AWS AI Practitioner Official Study Guide) A (Comprehend) is for NLP/text analytics.
B (Polly) is text-to-speech.
D (Translate) translates text, but does not create subtitles from audio/video.
NEW QUESTION # 261
A company makes forecasts each quarter to decide how to optimize operations to meet expected demand. The company uses ML models to make these forecasts.
An AI practitioner is writing a report about the trained ML models to provide transparency and explainability to company stakeholders.
What should the AI practitioner include in the report to meet the transparency and explainability requirements?
Answer: D
NEW QUESTION # 262
An AI practitioner is building an ML model. The AI practitioner wants to provide model transparency and explainability to stakeholders.
Which solution will meet these requirements?
Answer: B
Explanation:
The verified answer is A. Present the model Shapley values. AWS documentation states that SageMaker Clarify provides feature attributions based on Shapley values. These values can determine the contribution each feature made to model predictions and can be provided for individual predictions or at a global level for the model as a whole. That is exactly what stakeholders need when they ask for transparency and explainability: they need to understand which features influenced the model's decisions and how strongly those features contributed.
AWS also explains that SageMaker Clarify helps explain how ML models make predictions and that transparency is critical for consumers and regulators. Clarify's explainability uses a model-agnostic feature attribution approach and includes a scalable implementation of SHAP. These explanations answer practical stakeholder questions such as why the model made a prediction, how it makes predictions, and which features have the largest influence on model behavior.
Option B is incorrect because accuracy measures predictive correctness, not explainability. A model can be highly accurate while still being opaque. Accuracy alone does not show why a specific prediction was made.
Option C is incorrect because a confusion matrix summarizes classification performance by showing true positives, false positives, true negatives, and false negatives. It is useful for evaluation, but it does not explain individual feature influence or model reasoning.
Option D is incorrect because a secure inference endpoint protects access to the deployed model. Security is important, but it does not provide transparency or explainability.
Therefore, presenting Shapley values is the correct solution because it directly explains feature contributions to model predictions.
NEW QUESTION # 263
Which AI technique combines large language models (LLMs) with external knowledge bases to improve response accuracy?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Retrieval Augmented Generation (RAG) enhances LLM responses by:
* Retrieving relevant information from external knowledge sources
* Injecting retrieved content into the prompt context
* Reducing hallucinations and improving factual accuracy
AWS generative AI guidance describes RAG as a best practice when models must use up-to-date or domain- specific knowledge that is not embedded in the model weights.
Why the other options are incorrect:
* RL (A) focuses on reward-based learning.
* NLP (B) is a broad field, not a specific technique.
* Transfer learning (D) adapts model weights but does not retrieve external data at inference time.
AWS AI document references:
* Retrieval Augmented Generation on AWS
* Improving LLM Accuracy with External Knowledge
* Generative AI Architectures
NEW QUESTION # 264
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: A
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.
NEW QUESTION # 265
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