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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamentals of Generative AI | 24% | - Explain capabilities and use cases of generative AI - Differentiate between generative AI and traditional ML - Define generative AI and foundation models - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) |
| Topic 2: Fundamentals of AI and ML | 20% | - Identify types of ML: supervised, unsupervised, reinforcement learning - Define artificial intelligence (AI), machine learning (ML), and deep learning - Describe common ML workflows and lifecycle - Recognize key concepts: data, models, training, inference, evaluation |
| Topic 3: Security, Compliance, and Governance for AI Solutions | 14% | - Security controls for AI data and models - Governance frameworks for AI lifecycle - Data protection and privacy in AI workflows - Compliance requirements and regulations |
| Topic 4: 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 |
| Topic 5: Guidelines for Responsible AI | 14% | - Define responsible AI principles: fairness, transparency, privacy, safety - Explain bias detection and reduction - Describe ethical and societal impacts of AI - Identify risks and mitigation strategies for AI systems |
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NEW QUESTION # 141
A company is building a job recommendation system based on job posting data and job seeker user profiles.
The system shows bias in job recommendations based on gender for user profiles that are otherwise equivalent.
Which principle should the company follow to address this issue, according to AWS best practices for responsible AI?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Fairness is a core principle of AWS Responsible AI. It requires that AI systems:
* Do not produce biased or discriminatory outcomes
* Treat individuals and groups equitably
* Are evaluated and mitigated for bias across protected attributes
AWS Responsible AI guidance specifically highlights fairness as the principle used to identify, measure, and mitigate bias in AI systems such as recommendation engines.
Why the other options are incorrect:
* Governance (A) focuses on oversight and policies.
* Explainability (B) explains decisions but does not directly mitigate bias.
* Controllability (C) focuses on human oversight and intervention.
AWS AI document references:
* AWS Responsible AI Principles
* Bias and Fairness in ML Systems
* Responsible AI Best Practices on AWS
NEW QUESTION # 142
A company is using Amazon Bedrock Agents to build an application to automate business workflows.
Answer: B
Explanation:
The correct answer is D. Amazon Bedrock Agents are used to orchestrate and execute complex workflows by connecting foundation models with APIs, databases, and tools. According to AWS documentation, agents interpret user inputs, plan the necessary steps, call external APIs or systems, and return structured results. This allows the model to go beyond text generation into full automation workflows-such as booking tasks, querying internal systems, or summarizing reports. Option A describes multi-modal models, B refers to prompt tuning, and C misstates control delegation; agents act autonomously based on model reasoning. Thus, Bedrock Agents function as intelligent orchestrators, handling multi-step task execution through integrated tool use.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide - Agents Overview
AWS Generative AI Best Practices - Workflow Orchestration
NEW QUESTION # 143
An AI practitioner is determining the appropriate data type for various use cases.
Select the correct data type from the following list for each use case. Select each data type one time.
Answer:
Explanation:
Explanation:
Sentiment analysis # Text data
Traffic sign recognition # Image data
Customer demographics & purchase history # Tabular data
Stock price forecasting # Time series data
AWS classifies NLP tasks like sentiment analysis under text data
Computer vision tasks such as object and sign recognition use image data Structured rows and columns (demographics, transactions) are tabular data Sequential data indexed by time (prices, metrics) is time series data
NEW QUESTION # 144
A customer service team uses Amazon Bedrock to summarize conversation transcripts. The team needs to redact personally identifiable information (PII) from the summaries.
Which Amazon Bedrock Guardrails filter should the team configure?
Answer: D
Explanation:
Amazon Bedrock Guardrails provides sensitive information filters that identify and protect personally identifiable information within user inputs and model-generated responses.
These filters support built-in PII categories, including names, email addresses, telephone numbers, physical addresses, and other sensitive identifiers.
Organizations can configure the filters to block content or anonymize detected PII by replacing it with descriptive placeholders.
For example, a customer service summary containing an email address can have that information replaced with an appropriate PII category marker.
Word filters block specified terms or expressions rather than performing contextual PII detection.
Contextual grounding checks evaluate whether generated responses are supported by reference information.
Automated reasoning checks validate statements against formal policy rules.
Therefore, sensitive information filters are the correct solution for redacting customer PII from conversation summaries.
NEW QUESTION # 145
A company is building an application that needs to generate synthetic data that is based on existing data.
Which type of model can the company use to meet this requirement?
Answer: C
Explanation:
Generative adversarial networks (GANs) are a type of deep learning model used for generating synthetic data based on existing datasets. GANs consist of two neural networks (a generator and a discriminator) that work together to create realistic data.
* Option A (Correct): "Generative adversarial network (GAN)": This is the correct answer because GANs are specifically designed for generating synthetic data that closely resembles the real data they are trained on.
* Option B: "XGBoost" is a gradient boosting algorithm for classification and regression tasks, not for generating synthetic data.
* Option C: "Residual neural network" is primarily used for improving the performance of deep networks, not for generating synthetic data.
* Option D: "WaveNet" is a model architecture designed for generating raw audio waveforms, not synthetic data in general.
AWS AI Practitioner References:
* GANs on AWS for Synthetic Data Generation: AWS supports the use of GANs for creating synthetic datasets, which can be crucial for applications like training machine learning models in environments where real data is scarce or sensitive.
NEW QUESTION # 146
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