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| Section | Objectives |
|---|---|
| Topic 1: Applications of Foundation Models | - Content generation and summarization - AI-powered assistants and automation - Use cases for generative AI in business |
| Topic 2: Responsible AI and Security | - AI ethics and responsible use - Security, privacy, and governance in AI systems - Bias, fairness, and explainability |
| Topic 3: Fundamentals of Artificial Intelligence and Machine Learning | - Common ML workflows and lifecycle - Core AI and ML concepts - Data fundamentals for AI/ML |
| Topic 4: Fundamentals of Generative AI | - Foundation models and prompt engineering basics - Large language models (LLMs) concepts - AWS generative AI services overview (e.g., Amazon Bedrock) |
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NEW QUESTION # 101
A research company needs to analyze legal documents. The documents are up to 1 million tokens long and include embedded high-resolution charts. The company also needs to ingest video summaries to generate compliance reports.
Which Amazon Nova model meets these requirements?
Answer: B
Explanation:
The verified answer is D. Amazon Nova Premier . The decisive requirement is the combination of a 1 million-token context window , multimodal document understanding, chart analysis, and video input. AWS documentation identifies Amazon Nova Premier as the most capable multimodal Amazon Nova understanding model. It supports text, image, and video inputs and generates text output. AWS also lists Amazon Nova Premier with a 1M context window , while Amazon Nova Pro and Amazon Nova Lite support
300k , and Amazon Nova Micro supports 128k and is text-only. Because the company's legal documents are up to 1 million tokens long, Nova Premier is the only listed option that satisfies the context-length requirement.
AWS documentation also states that Amazon Nova Premier has a supported context length of 1 million tokens , which AWS equates to 1 million tokens of text, 500 images, or 90 minutes of video. The same documentation explains that Nova Premier excels at question answering on long documents. This directly matches the research company's need to analyze very long legal documents and ingest video summaries for compliance reporting.
Amazon Nova Micro is incorrect because it is a text-only model and has a smaller context window. It cannot process embedded high-resolution charts or video inputs. Amazon Nova Lite is incorrect because it is multimodal, but its context window is 300k, which is below the 1 million-token requirement. Amazon Nova Pro is also incorrect for the same reason: it is highly capable and multimodal, but its context window is 300k rather than 1M. The requirement is not simply "multimodal"; it is long-context multimodal analysis at the 1 million-token scale. Therefore, the only model that fully meets the requirements is Amazon Nova Premier .
NEW QUESTION # 102
A company acquires International Organization for Standardization (ISO) accreditation to manage AI risks and to use AI responsibly. What does this accreditation certify?
Answer: D
Explanation:
Comprehensive and Detailed
ISO certifications apply to processes, frameworks, and systems - not individuals or every piece of software.
When a company is ISO-certified, its development framework and governance processes comply with ISO standards for security, risk, or AI responsibility.
Reference:
AWS Compliance Programs - ISO
NEW QUESTION # 103
A company plans to build an AI model for the company's global customer base. The company wants to train the model on a dataset that reflects user diversity.
Which action will meet this requirement?
Answer: C
Explanation:
The verified answer is A. Balance class representation in the dataset . The requirement is to train an AI model on data that reflects the diversity of a global customer base. In AWS responsible AI guidance, dataset quality is directly connected to fairness, bias reduction, and model behavior across different user groups. AWS identifies dataset characteristics such as inclusivity, diversity, curated data sources, and balanced datasets as part of responsible AI knowledge for AI practitioners. This directly supports the need for a dataset that represents diverse users instead of overrepresenting one group and underrepresenting another.
AWS also explains that bias can result from imbalances in data or from differences in model performance across groups. Amazon SageMaker Clarify is designed to help detect and mitigate potential bias during data preparation, after training, and in deployed models by examining specified attributes. This supports the principle that the training dataset should be examined and balanced before model training so that the model does not preferentially learn patterns from majority groups while performing poorly for minority groups.
Option B is incorrect because using a regional dataset, even if complete, does not satisfy a global diversity requirement. A complete regional dataset may still exclude languages, demographics, cultural contexts, behaviors, and usage patterns from other regions. Option C is incorrect because oversampling majority class data worsens imbalance. It increases the dominance of already overrepresented groups and can amplify biased model behavior. Option D is also incorrect because dropping minority class records removes exactly the user groups that must be represented for a global customer base.
AWS SageMaker Clarify documentation specifically describes class imbalance bias as a situation where one facet has fewer training samples than another. AWS notes that models may preferentially fit larger facets at the expense of smaller facets, leading to higher training error for underrepresented groups. Therefore, balancing class representation is the best action because it improves dataset representativeness and supports responsible AI fairness for a diverse global user base.
NEW QUESTION # 104
A company wants to implement a large language model (LLM)-based chatbot to provide customer service agents with real-time contextual responses to customers' inquiries. The company will use the company's policies as the knowledge base.
Answer: B
Explanation:
* Retraining or pre-training is costly and unnecessary for just using company policies.
* Fine-tuning adapts models but is inefficient for frequently changing company documents.
* Retrieval-Augmented Generation (RAG) is the best approach - it retrieves relevant policy documents from a knowledge base and feeds them into the model context in real time, ensuring accurate and up-to-date responses.
# Reference:
AWS Documentation - RAG with Amazon Bedrock
NEW QUESTION # 105
A company wants to upload customer service email messages to Amazon S3 to develop a business analysis application. The messages sometimes contain sensitive data. The company wants to receive an alert every time sensitive information is found.
Which solution fully automates the sensitive information detection process with the LEAST development effort?
Answer: A
Explanation:
The correct answer is A because Amazon Macie is a fully managed data security and privacy service that uses machine learning to automatically detect sensitive data such as PII (personally identifiable information) in Amazon S3. It requires no custom development, and it can be configured to generate alerts when sensitive data is detected in newly uploaded objects.
From AWS documentation:
"Amazon Macie automatically discovers and classifies sensitive data in S3 buckets and generates alerts when it detects sensitive content, such as names, addresses, and credit card numbers." Explanation of other options:
B). Deploying an LLM on SageMaker to perform redaction is custom and operationally intensive.
C). Regex-based detection is brittle and requires extensive manual work, with high maintenance overhead.
D). Asking customers to avoid sharing sensitive data is not enforceable and does not meet compliance or security standards.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon Macie Documentation - Sensitive Data Discovery and Alerting
* AWS Security Best Practices - Data Privacy and Governance
* AWS ML Specialty Guide - Governance and Compliance Automation
NEW QUESTION # 106
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