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| Section | Weight | Objectives |
|---|---|---|
| AI Application Development | 30% | - Evaluate and optimize AI applications
|
| AI/ML Fundamentals | 24% | - Understand the AI/ML lifecycle
|
| AWS AI Services Overview | 26% | - Describe Amazon Bedrock capabilities
|
| Responsible AI | 20% | - Explain the principles of responsible AI
|
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NEW QUESTION # 150
A media company uses three AI agents to automate content moderation. The three agents are a detector agent, a redactor agent, and an auditor agent.
Which mechanism ensures that the agents share flagged segment details and process tasks in the required order?
Answer: D
Explanation:
A coordinated multi-agent communication pattern with shared or persistent state best satisfies the requirement because the detector, redactor, and auditor must exchange information and maintain the state of a multi-step workflow.
AWS describes multi-agent collaboration as providing "a centralized mechanism for planning, orchestration, and user interaction." A supervisor can coordinate multiple specialized agents, assign work, route information, and execute a plan across collaborator agents.
In this scenario, the detector first identifies problematic content and produces details such as the affected segment and violation type. The redactor must receive those details to modify the appropriate content. The auditor must then receive the resulting context and verify that the required moderation action occurred. This requires communication plus persistent workflow state so that downstream agents retain the relevant information and understand the state of earlier processing.
AWS AgentCore Memory documentation explicitly identifies multi-agent systems as a memory use case, explaining that a team of agents can share memory to synchronize information. AWS also describes workflow agents as using memory to track the status of individual steps and maintain progress through a multi-step process.
Amazon Bedrock multi-agent functionality additionally supports sharing conversational history with collaborator agents, allowing relevant runtime context to flow from supervisor to collaborator.
Model Context Protocol (MCP) primarily standardizes how AI systems connect to external tools, services, and contextual resources. MCP by itself does not define the detector # redactor # auditor processing sequence required here.
Video compression concerns media transport or storage efficiency, not agent coordination. Adjusting inference batch size concerns performance and throughput rather than workflow state or inter-agent communication.
Strictly speaking, orchestration establishes task ordering while memory preserves shared state. Of the choices provided, option B is the only one representing the combination of agent communication and persistent context required for the workflow.
Therefore, B. Multi-agent communication patterns that use persistent memory is the correct answer.
NEW QUESTION # 151
An AI practitioner has prepared a dataset for training models in Amazon SageMaker AI. The AI practitioner wants to share the dataset within the company so that future employees can discover and reuse the dataset.
Which solution will meet these requirements?
Answer: D
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS recommends Amazon SageMaker Feature Store as a centralized, managed repository for storing, discovering, and reusing features and datasets across teams and projects. Feature Store enables:
Feature discovery through searchable metadata
Reusability of curated datasets and features by future practitioners
Consistency between training and inference datasets
Governance and lineage, supporting responsible AI practices
Why the other options are incorrect:
A is intended for retrieval-augmented generation, not dataset sharing.
B does not provide discoverability, governance, or long-term reuse.
D is designed for sharing data with external subscribers, not internal ML teams.
AWS AI Study Guide Reference:
Amazon SageMaker Feature Store overview
AWS best practices for feature management and reuse
NEW QUESTION # 152
A company has developed a neural network model to replace an existing decision tree model. The neural network model has a higher prediction accuracy compared to the decision tree model. However, the neural network model's decision process is not as explainable as the decision tree model's decision process.
Which tradeoff is the company making by adopting the neural network model?
Answer: A
Explanation:
The verified answer is C. Higher performance for lower interpretability . The question states that the neural network model has higher prediction accuracy than the decision tree model, but its decision process is less explainable. In machine learning, this is a classic tradeoff between predictive performance and interpretability. AWS machine learning guidance discusses the tradeoff between performance and model interpretability and explains that interpretability becomes especially important when there is a high cost for incorrect predictions or when stakeholders need to understand decisions made by the model.
A decision tree is generally more interpretable because its decision path can often be traced through visible splits and rules. A neural network, especially a deeper model, often captures complex nonlinear relationships and can achieve higher prediction accuracy, but its internal decision process is usually harder to explain directly. AWS SageMaker Clarify documentation supports this distinction by explaining that model explainability tools help explain how machine learning models make predictions and help stakeholders understand model characteristics before deployment and debug predictions after deployment.
Option A is incorrect because lower interpretability does not produce higher compliance. In regulated environments, lower interpretability can create more governance and compliance challenges, not fewer.
Option B is incorrect because portability is not the issue described. The question does not discuss moving the model across environments or platforms. Option D is incorrect for the same reason: portability is unrelated to the stated facts.
The company is choosing the model with better predictive accuracy, which means higher model performance.
But it is accepting a weaker ability to explain how the model reaches its predictions. That is why the correct tradeoff is higher performance for lower interpretability .
NEW QUESTION # 153
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: C
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 # 154
A company is using a pre-trained large language model (LLM) to extract information from documents. The company noticed that a newer LLM from a different provider is available on Amazon Bedrock. The company wants to transition to the new LLM on Amazon Bedrock.
What does the company need to do to transition to the new LLM?
Answer: B
Explanation:
Transitioning to a new large language model (LLM) on Amazon Bedrock typically involves minimal changes when the new model is pre-trained and available as a foundation model. Since the company is moving from one pre-trained LLM to another, the primary task is to ensure compatibility between the new model's input requirements and the existing application. Adjusting the prompt template is often necessary because different LLMs may have varying prompt formats, tokenization methods, or response behaviors, even for similar tasks like document extraction.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"When switching between foundation models in Amazon Bedrock, you may need to adjust the prompt template to align with the new model's expected input format and optimize its performance for your use case. Prompt engineering is critical to ensure the model understands the task and generates accurate outputs." (Source: AWS Bedrock User Guide, Prompt Engineering for Foundation Models) Detailed Option A: Create a new labeled dataset.Creating a new labeled dataset is unnecessary when transitioning to a new pre-trained LLM, as pre-trained models are already trained on large datasets. This option would only be relevant if the company were training a custom model from scratch, which is not the case here.
Option B: Perform feature engineering.Feature engineering is typically associated with traditional machine learning models, not pre-trained LLMs. LLMs process raw text inputs, and transitioning to a new LLM does not require restructuring input features. This option is incorrect.
Option C: Adjust the prompt template.This is the correct approach. Different LLMs may interpret prompts differently due to variations in training data, tokenization, or model architecture. Adjusting the prompt template ensures the new LLM understands the task (e.g., document extraction) and produces the desired output format. AWS documentation emphasizes prompt engineering as a key step when adopting a new foundation model.
Option D: Fine-tune the LLM.Fine-tuning is not required for transitioning to a new pre-trained LLM unless the company needs to customize the model for a highly specific task. Since the question does not indicate a need for customization beyond document extraction (a common LLM capability), fine-tuning is unnecessary.
Reference:
AWS Bedrock User Guide: Prompt Engineering for Foundation Models (https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-engineering.html) AWS AI Practitioner Learning Path: Module on Working with Foundation Models in Amazon Bedrock Amazon Bedrock Developer Guide: Transitioning Between Models (https://docs.aws.amazon.com/bedrock/latest/devguide/)
NEW QUESTION # 155
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